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BUGSSGF 2013 Papers - Index
422 papers. Source: SAS Global Forum conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | "Google-Like" Maps in SAS | A. Darrell Massengill | We are frequently asked if we can have maps similar to Google Maps in SAS. Customers want the background image displayed behind their data so they can see where streets or other features are located. They may also want to pan and zoom the map. Unfortunately, Google has legal restrictions and limitations on the use of their maps. Now, you can have "Google-like" maps inside of SAS. You may have already seen this capability in products like SAS Visual Analytics Explorer and other products using them will be available in future releases. This presentation will discuss and demonstrate these new capabilities in SAS Visual Analytics Explorer, SAS/GRAPH, and other products |
proc step, proc gmap, ods html, macro variable | source | 23 |
| 2 | "How Do I...?" There Is More Than One Way To Solve That Problem; Why Continuing To Learn Is So Important | Arthur L. Carpenter | In the SAS forums questions are often posted that start with "How do I . . . ?". Generally there are multiple solutions to the posted problem, and often these solutions vary from simple to complex. In many of the responses the simple solution is often inefficient and also reflects a somewhat naïve understanding of the SAS language. This would not be so very bad except sometimes the responder thinks that their response is the best solution, or perhaps worse, the only solution. Worse yet, when there is a range of solutions, the 'right answer' that the original poster selects often reflects the simplest solution that the original poster understands. In both cases these folks have stopped learning and have stopped expanding their understanding of the language. The examples in this presentation will illustrate the progression of solutions from the simple (simplistic) to the sophisticated for a number of 'How do I | macro variable, proc sql, proc step, set statement, proc format, point= option, proc freq, whichc, input statement, dictionary tables, if-then-else, macro function, where statement, stop, call symputx, lookup, dataset, proc glm | source | 19 |
| 3 | "How May I Help?" The SAS Enterprise Guide Analyze Program Feature | Ramya Purushothaman | Have you ever been looking into a lengthy and complex SAS code, may it be inherited or your own old program, and wished you could understand what is happening inside without having to go through every line? Left without any associated documentation and wondered where to start from? Felt that it would be better to have a process flow representation of what the code does, quickly? Then the Analyze Program feature that SAS Enterprise Guide offers might work for you! This paper discusses what to expect of this feature and what not to with example analyses from the Life Sciences industry | sas enterprise guide, proc step, proc sql | source | 9 |
| 4 | %GetReviews: A SAS Macro to Retrieve User Reviews in JSON Format from Review Websites and Create SAS Data Sets | Ganesh Badisa, Goutam Chakraborty, Siddhartha Reddy Mandati |
The proliferation of social networking sites and consumers' desires to create and share content on such sites has continued to generate a huge amount of unstructured data. Analytics users often want to tap into such unstructured data and extract information. Many websites such as Twitter, Facebook, and Rotten Tomatoes offer APIs for external systems to interact and retrieve the data in JSON format . The API of Rotten Tomatoes returns data in a complex text pattern that has information about user reviews. Currently there is no designated code in SAS to read the JSON response directly and fetch the needed data. This paper illustrates the development and application of a SAS Macro %GetReviews to retrieve the reviews of any desired movie from Rotten Tomatoes' API | prxnext, data mining | source | 9 |
| 5 | %MIXED_FIT: A SAS Macro to Assess Model Fit and Adequacy for Two-Level Linear Models | Bethany A. Bell, Mihaela Ene, Whitney Smiley |
When estimating multilevel models it is important for researchers to make sure their models fit their data. However, examining model fit can be quite cumbersome. We have developed the macro MIXED_FIT to help researchers assess model fit in a simple yet comprehensive way. Specifically, this paper provides a SAS macro that incorporates changes in model fit statistics [i.e., -2 log likelihood (-2LL), AIC, and BIC] as well as changes in pseudo-R2. By utilizing data from PROC MIXED ODS tables, the macro produces a comprehensive table of changes in model fit measures and allows SAS users to examine model fit in both nested and non-nested models both in terms of statistical and practical significance without having to calculate these values by hand. Keywords: MULTILEVEL MODELING, MODEL FIT, PROC MIXED, SAS MACRO |
proc mixed, proc step, likelihood function, fixed effects, feature selection, maximum likelihood estimation, sample size, macro definition, sample, random intercept | source |
8 |
| 6 | 10 SAS Skills for Grad Student Survival: A Grad Student ?How-To? Paper View Paper View Poster | Elisa L. Priest | Grad students learn the basics of SAS programming in class or on their own. Classroom lessons focus on statistical procedures and the datasets are usually ready for analysis. Students may learn basic coding such as IF-THEN statements, variable creation, and functions. However, real world research projects are usually complex and may require a variety of different SAS tools and techniques for data exploration and analysis. Most of these tools and techniques students have to learn on their own. Throughout the many , many, years that I was in graduate school, I spent a lot of time learning SAS on my own. This paper is a culmination of the SAS challenges I overcame and the SAS skills that I learned outside of the classroom. These 10 SAS skills helped me to survive graduate school and successfully write a complex simulation analysis in SAS for my dissertation. This simulation was one of the most challenging SAS programs that I ever wrote | sas enterprise guide, output delivery system, sas programming, pdv, proc step, macro definition, longitudinal data, macro variable, proc phreg, sas base, dataset, macro facility | source |
15 |
| 7 | 10-Minute JMP | George J. Hurley | Heard of JMP, but haven't had time to try it? Don't want to devote 50 minutes to a talk about software that you might not want to use? This is the talk to you. In 10 minutes, you will learn some of the amazing visualization and modeling features in JMP and how to use them. This talk will JMP-start your JMP usage. When it's complete, we suspect you will want to attend some of the longer talks, too | bubble plot, sample, bootstrap, proc reg, proc step, regression analysis, excel | source | 11 |
| 8 | 30 in 20 Things You May Not Know about SAS | Tim Berryhill | 30 things you may not know SAS can do. In 20 minutes, I hope to widen your eyes and improve your programming. I have used SAS on many platforms and operating systems, with many databases. Most of these ideas will run anywhere SAS runs | proc step, proc format, character variable, data step, macro definition, set statement, macro variable, sample, in=, proc delete, input statement, proc sql | source | 8 |
| 9 | A Beginner's Introduction to an Idiot's Guide to PROC TEMPLATE and GTL for Dummies | Christopher Battiston | The aim of this paper will be an extremely gentle introduction to the very exciting and s omewhat intimidating world of PROC TEMPLATE and Graph Template Language (GTL). As these two SAS features are still relatively new, not many people have had time to learn them and see what they are capable of accomplishing with minimal effort |
proc step, proc template, scatter plot, data visualization, sas base | source | 14 |
| 10 | A Better Way to Flip (Transpose) a SAS Data Set | Arthur S. Tabachneck, Joe Whitehurst, Robert Virgile, Xia Ke Shan |
Many SAS programmers have flipped out when confronted with having to flip (transpose) a SAS dataset, especially if they had to transpose multiple variables , needed transposed variables to be in a specific order, had a mixture of character and numeric variables to transpose, or if they needed to retain a number of non -transposed variables. Wouldn't it be nice to have a way to accomplish such tasks that was easier to understand and modify than PROC TRANSPOSE, was less system resource intensive, required fewer steps and could accomplish the task as much as fifty times or more faster? |
proc transpose, proc step, macro loop, numeric variable, character variable, proc sort, macro definition, %if-%then-%else, by group processing, in=, sas library, keep statement, dataset, cpu time, missing value, proc sql, sas programmer | source | 21 |
| 11 | A CareerView Mirror: Another Perspective on Your Work and Career Planning | Bill Donovan | Career planning in today's tumultuous job marketplace requires a more rigorous and disciplined approach, which must begin with each individual tracking his or her particular skills and experiences. The ability to organize and inventory your entire career-related experience is the foundation of a solid career plan. The catalog of your work assignments and functional responsibilities creates a reflection of your efforts in your career to-date. All of this helps to build your CareerView Mirror | life sciences, business intelligence | source | 5 |
| 12 | A Case Application of Propensity Score Matching in MTM Outcomes Evaluation at Retail Pharmacy | Michael Taitel, Youbei Lou, Zhongwen Huang |
Propensity score matching (PSM) is commonly used in obser vational studies to reduce the potential selection bias. The process of PMS includes propensity score estimation , matching and evaluation. This paper presents a case application in outcome evaluation of medication therapy management at retail pharmacy. It illustrates how baseline outcome balance check could help detect po tential omissions of covariates that could affect both the treatment and the outcomes. Further, it shows how in re tail MTM setting, it is possible to find a common set of covariates that affect multiple studies outcomes and perform a single propensity sco re matching rather than a separate matching for each individual study outcome. KEYWORDS: Observational study, selection bias, propensity score matching | propensity score, propensity score matching, create table, dataset | source |
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| 13 | A Case Study of Tuning an Enterprise Business Intelligence Application in a Multi-OS Environment | Fred R. Forst | A corporation is planning to expand its use of SAS Web Report Studio (WRS) by increasing its user base. Two questions emerge: 1) What happens to SAS Web Report Studio response time as users are added? 2) What tuning modifications can be used to improve performance? This case study uses a SAS Web Report Studio and utilizes a WRS simulation technique and 'grows' the workload and examines WRS response times. The SAS EBI environment utilizes IBM zEnterprise BladeCenter Extension (zBX) zBX blade technology for the SAS Mid-Tier using IBM WebSphere on Linux. Logs from WRS, WebSphere, NMON, and the server-tier are parsed and stored in a SAS data set for all analyses. Many stress test runs are executed, modifying various tuning parameters. The analysis shows the effects of tuning and offers insight on tuning best practices | null hypothesis, proc genmod, dataset, alternative hypothesis, duncan test, proc glm | source | 16 |
| 14 | A Comparison between GUI Prompts of SAS Enterprise Guide 4.1 and 4.3 and Approaches for Developing Next-Generation Prompts | Menaga Guruswamy Ponnupandy | SAS codes have to be edited as and when the crit eria of execution changes. The use of GUI Prompts helps in preserving source code from changes and in automation of SAS Enterprise Guide Projects by passing run time parameters to SAS. The main purpose of this paper is to compare the advanced features or functionalities of GUI Prompts between SAS Enterprise Guide 4.1 and SAS Enterprise Guide 4.3. This paper also discusses the limited ability of prompts and provides tips for handling such situations or highlights the need for development of future generation prompts | sas enterprise guide, macro variable, sas programmer | source | 12 |
| 15 | A Concise Display of Multiple Response Items | Patrick Thornton | Surveys often contain multiple response items, such as language where a respondent may indicate that she speaks more than one language. In this case, an indicator variable (1=Yes, 0=No) is often created for each language category. This paper shows how a concise tabulation of the count and percent of respondents with a "Yes" on one or more indicator variables may be obtained using PROC TABULATE and a MULTILABEL format. A series of indicator variables is used to create a binary variable and its base-10 equivalent, and a MULTILABEL format is created to properly aggregate observations with a "Yes" on two or more indicator variables. The BAND function may also be used to easily subset observations with "Yes" responses on certain combinations of the indicator variables |
binary variable, proc step, proc tabulate, proc format, proc freq | source | 7 |
| 16 | A Data-Driven Analytic Strategy for Increasing Yield and Retention at Western Kentucky University Using SAS Enterprise BI and SAS Enterprise Miner | Matt T. Bogard | As many Universities face the constraints of decl ining enrollment demographics, pressure from state governments for inc reased student success, as well as declining revenues, the costs of utilizing anecdotal evidence and intuition based on 'gut' feelings to make time and resource allocation decisions become significant. However, grasping advanced statis tical methods and analytics for data driven decision making can be overwhelming to some staff making buy in difficult. This paper describes how we are using SAS Enterprise Miner to develop a model to score university students based on their probability of enrollment and retention early in the enrollment funnel so that staff and administrators can work to recruit students that not only have an average or better chance of enrolling but also succeeding once they enroll . Incorporating these results into SAS EBI will allow us to deliver easy -to-understand results to university personnel | data mining, proc hpsplit, proc hpforest, gradient boosting, resource allocation, roc curve, training set, logistic distribution, regression analysis, score code | source | 12 |
| 17 | A Day in the Life of Data - Part 1 | Brian J. Bee | As a new SAS programmer, you may be overwhelmed with the variety of tricks and techniques that you see from experienced SAS programmers; as you try to piece together some of these techniques you get frustrated and perhaps confused because the data showing these techniques are inconsistent. That is, you read several papers and each uses different data. This series of four papers is different. They will step you through several techniques but all four papers will be using the same data. The authors will show how value is added to the data at each of the four major steps: Input, Data Manipulation, Data and Program Management, and Graphics and Reporting | sas enterprise guide, input statement, excel, proc sql, libname statement | source | 11 |
| 18 | A Day in the Life of Data - Part 2 | Harry Droogendyk | As a new SAS programmer, you may be overwhelmed with the variety of tricks and techniques that you see from experienced SAS programmers; as you try to piece together some of these techniques you get frustrated and perhaps confused because the data showing these techniques are inconsistent. That is, you read several papers and each uses different data. This series of four papers is different. They will step you through several techniques but all four papers will be using the same data. The authors will show how value is added to the data at each of the four major steps: Input, Data Manipulation, Data and Program Management, and Graphics and Reporting | proc sql, dimension table, proc step, macro variable, data step, data merging, full join, left join, merge statement, create table, order by | source | 13 |
| 19 | A Day in the Life of Data - Part 3 | Peter Crawford | As a new SAS programmer, you may be overwhelmed with the variety of tricks and techniques that you see from experienced SAS programmers; as you try to piece together some of these techniques you get frustrated and perhaps confused because the data showing these techniques are inconsistent. That is, you read several papers and each uses different data. This series of four papers is different. They will step you through several techniques but all four papers will be using the same data. The authors will show how value is added to the data at each of the four major steps: Input, Data Manipulation, Data and Program Management, and Graphics and Reporting | proc sql, proc step, sample, options statement, proc options | source | 8 |
| 20 | A Day in the Life of Data: Part 4 - Graphics and Reporting | Sanjay N. Matange | As a new SAS programmer, you might be overwhelmed with the variety of tricks and techniques that you see from experienced SAS programmers. As you try to piece together some of these techniques , you become frustrated and perhaps confused because the data showing these techniques are inconsistent. That is, you read several papers , and each uses different data. This series of four papers is different. T hey will step you through several techniques , but all four papers use the same data. The authors will show how value is added to the data at each of the four major steps: Input, Data Manipulation, Data and Program Management, and Graphics and Reporting | proc sgplot, raw data, histogram, graph template language, proc sgpanel, order by | source | 12 |
| 21 | A First Look at the ODS Destination for PowerPoint | Tim Hunter | This paper introduces the ODS destination for PowerPoint, one of the next generation of ODS destinations. Using our new PowerPoint destination you can send PROC output directly into native PowerPoint format. See examples of slides created by ODS. Learn how to create presentations using ODS, how to use ODS style templates to customize the look of your presentations and how to use pre-defined layouts to make title slides and two-column slides. Find out how the PowerPoint destination is like other ODS destinations and how it's different. Stop cutting and pasting and let the ODS destination for PowerPoint do the work for you! |
proc step, ods rtf, options statement, proc template, proc tabulate, ods pdf | source | 20 |
| 22 | A Flexible Method to Apply Multiple Imputation Using SAS/IML Studio | Lisa M. Lix, Xue Yao |
Multiple imputation methods are widely used for missing data problems in various scientific fields. Imputation methods can also be applied to measurement error problems, which arise frequently in many data-analytic problems. SAS/STAT software offers the MI and MIANALYZE procedures for creating and analyzing multiple imputation data. PROC MI can be used to impute continuous or categorical variables with a monotone missingness pattern and continuous variables with an arbitrary missingness pattern. This paper provides an imputation method developed using SAS/IML Studio for categorical variables with an arbitrary missingness pattern. This method expands the SAS analyst's ability to apply multiple imputation methods to a wide variety of variables. The method is illustrated using an example of measurement (i.e., misclassification) error in disease diagnoses |
imputation, multiple imputation, proc step, covariance, missing value, estimate, data cleaning, nominal variable, proc logistic, proc mi, proc mianalyze, logistic distribution, dataset, regression analysis, inferential statistics, response variable, predictor variable | source |
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| 23 | A Flock of C-Stats, or Efficiently Computing Multiple Statistics for Hundreds of Variables | Bruce S. Lund, Steven Raimi |
In other presentations, the authors have provided macros that efficiently compute univariate statistics for hundreds of variables at a time. The classic example is when a modeler must fit a binary model (two-valued target) and has available hundreds of potential numeric predictors. Such situations may occur when third-party data sets are added to in-house transactional data for direct marketing or credit scoring applications. The paper describes the SAS code to compute these statistics, focusing on the techniques that make these macros efficient. Topics include macro techniques for identifying and managing the input variables, restructuring the incoming data, and using hash objects to swiftly count the number of distinct values for each variable | hash table, proc step, proc rank, numeric variable, proc freq, predictor variable, data step, proc means, logistic distribution, missing value, proc logistic, response variable, logistic regression, macro variable, proc sql | source | 11 |
| 24 | A Macro to Read in Medi-Span Text Format Database by Data Dictionary | Sijian Zhang | Investigators often use commercial databases to obtain useful information for their researches. However, many companies do not offer the code for transferring the data files from their deliverable file format into the one used in customer's system. With many data files and variables, the data transfer process can be very tedious. If the databases vary in different versions, the transfer code revision can be another pain. This paper presents an approach to simplify the data transfer process of reading in Medi-Span drug information text data files by taking the advantage of macro programming and its data dictionary information. One of Medi-Span text data files, "MF2STR", is used as an example through this paper | dataset, data dictionary, macro variable, label statement | source | 8 |
| 25 | A Macro to Verify a Macro Exists | Richard D. Langston | Although the %SYSMACEXIST function can do macro existence checking, it is limited to pre-compiled macros. This paper describes the %macro_exists macro, which will verify that a specified macro will be found if invoked. The macro will search for pre-compiled macros and will search all autocall libraries to verify the existence | macro facility, macro definition, macro variable, stored macro | source | 5 |
| 26 | A Metadata-Driven Programming Technique Using SAS | Xiyun Wang | In a typical statistical SAS system, validations on user inputs and imputation on missing values using default values are essential to ensure core system processes finish successfully wi thout errors. This paper introduces a metadata driven programming technique using SAS for validations and imputations in SAS macros. It first explains how to define validation rules as metadata for various types of i nputs and how to store default values for missing values as metadata using SAS syntax; then it illustrates how to u tilize the defined metadata to automate validation a nd imputation processes against user inputs. This metadata dr iven programming technique using SAS eases our system development by promoting efficiency, reusability, easy maintainability, and good coding consistency | imputation, data cleaning, macro definition, macro parameter, in=, sas log, macro variable, if-then-else, excel, missing value, sas syntax, dataset, macro facility, data validation rules | source | 14 |
| 27 | A Multilevel Model Primer Using SAS PROC MIXED | Bethany A. Bell, Jason A. Schoeneberger, Mihaela Ene, Whitney Smiley |
This paper provides an introduction to specifying multilevel models using PROC MIXED. After a brief introduction to the field of multilevel modeling, users are provided with concrete examples of how PROC MIXED can be used to estimate (a) two-level organizational models, (b) two-level growth models, and three-level organizational models. Both random intercept and slope models are illustrated. Examples are shown using different real world data sources, including the publically available Early Childhood Longitudinal Study-Kindergarten cohort data. For each example, different research questions are examined through both narrative explanations and examples of the PROC MIXED code and corresponding output. Keywords: MULTILEVEL MODELING, PROC MIXED, MODEL FIT, STATISTICAL ASSUMPTIONS |
proc mixed, proc step, fixed effects, random intercept, random slope, covariance, regression analysis, residual, degrees of freedom, estimate, predictor variable, proc calis, likelihood function, correlation | source | 19 |
| 28 | A Practical Approach to Creating Define.XML by Using SDTM Specifications and Excel functions | Amos Shu | Define.xml (Case Report Tabulation Data Definition Specification) is a part of new drug submission required by the FDA. Clinical SAS programmers usually use SAS programming [1, 2, 3, 4, 5] to generate the code of Define.xml as described in the CDISC Case Report Tabulation Data Definition Specification (define.xml) V1.0.0 [6]. This paper illustrates the process of using SDTM specifications and EXCEL functions to generate the code of Define.xml in an easy and straightforward way |
excel, annotated crf, clinical trial, controlled terminology, cdisc | source |
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| 29 | A Preventive Approach for Automatic Checking of CDISC ADaM Metadata to Detect Noncompliance | Xiangchen (Bob) Cui, Min Chen, Tathabbai Pakalapati |
Presentation: Writing ADaM programming specification has become a perfunctory task in regulatory submissions. The programming specification documentation serves as the primary source for ADaM programming, Define.xml, and Study Data Reviewer's Guide. ADaM programming for FDA submission should meet submission requirements and follow CDISC ADaM guidelines. The OpenCDISC Validator is a very useful tool to check data compliance with CDISC models such as SDTM, ADaM and Define.xml. However the tool requires datasets as one of its inputs, which are normally available at later stage of programming cycle. Sometimes it is too late and/or costly to fix the errors identified by the tool at the later stage of programming cycle. This paper introduces a preventive | adam dataset, macro definition, cdisc | source | 2 |
| 30 | A Row Is a Row Is a Row, or Is It? A Hands-On Guide to Transposing Data | Christianna S. Williams | Sometimes life would be easier for the busy SAS programmer if information stored across multiple rows were all accessi ble in one observation, using additional columns to hol d that data. Sometimes it makes more sense to turn a short, wide data set into a long, skinny one -- convert columns into rows. Base SAS provides two primary methods for converting rows into columns or vice versa - PROC TRANSPOSE and the DATA STEP. How do these methods work? Which is best suited to different transposition pro blems? The purpose of this workshop is to demonstrate various types of transpositions using the DATA STEP and to "unpack" the TRANSPOSE procedure. Afterwards, you should be the office go-to gal/guy for reshaping data |
proc transpose, proc step, by group processing, numeric variable, character variable, missing value | source | 16 |
| 31 | A SAS Macro for Applying Multiple Imputation to Multilevel Data | Stephen Mistler | Single-level multiple imputation procedures (e.g., PROC MI) are not appropriate for multilevel data sets where observations are nested within clusters. Analyzing multilevel data imputed with a single-level procedure yields variance estimates that are biased toward zero and may yield other biased parameters. Given the prevalence of clustered data (e.g., children within schools; employees within companies; observations within people), a general approach is needed for handling missing data in multilevel data sets. This paper describes a SAS macro, MMI_IMPUTE, that performs multiple imputation for clustered data sets with two levels. The macro uses a Bayesian implementation of the mixed linear model to generate imputations for lower-level incomplete variables, and uses single-level procedures similar to those used in PROC MI to generate imputations for cluster-level variables |
imputation, multiple imputation, proc mixed, missing value, regression analysis, response variable, macro variable, maximum likelihood estimation, likelihood function, proc step, predictor variable, macro definition, series plot, proc mi, problem solving | source | 8 |
| 32 | A SAS Macro for Computing Pooled Likelihood Ratio Tests with Multiply Imputed Data | Stephen Mistler | For multilevel analyses (e.g., linear mixed models), researchers are often interested in pooling, interpreting, and testing both fixed effects and random effects. PROC MIANALYZE has two shortcomings in this regard. First, it cannot easily pool variance estimates. Second, the significance tests of these estimates are Wald-type tests that are inappropriate for testing variance estimates. Likelihood ratio testing is a more flexible approach, as it can be used to compare models that differ in both fixed and random effects. The likelihood ratio test statistic requires a complex calculation that is not included in PROC MIANALYZE. This paper describes a SAS macro, MMI_ANALYZE, that fits two user-specified models in PROC MIXED, pools the estimates from those models (including variance components), and implements a pooled likelihood ratio test |
likelihood function, imputation, proc mixed, proc step, fixed effects, degrees of freedom, proc mianalyze, multiple imputation, regression analysis, missing value, proc calis, estimate, hypothesis testing, response variable | source | 10 |
| 33 | A SAS Macro for Generating a Set of All Possible Samples with Unequal Probabilities without Replacement | Alan Ricardo da Silva, Igor Ferreira do Nascimento |
This paper considers listing all possible samples o f size n with unequal probabilities without replacement in order to find the sample distribution. The main application of that is to estimate the Horvitz-Thompson (HT) es timator and possibly to know the shape of its sample distributi on to construct confidence intervals. The algorithm computes all possible samples of the population, in contrast wit h PROC SURVEYSELECT which generates any samples of size n, but not all possible samples, and at the end it is possible to plot the sample distribution of the est imator. The equations are encoded in a SAS/IML Macro and the graphics are made using PROC GPLOT |
sample, confidence interval, random sampling, proc step, proc surveyselect | source |
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| 34 | A Simple Approach to Generate Page Numbers in X of Y Format in ODS RTF Output | Amos Shu | Page numbers in X of Y format, such as "Page 18 of 280", "Page 2 of 30", are a common feature of ODS RTF outputs. The "X" indicates the current page number, and the "Y" is the total number of pages. There are some macros available to generate those numbers, but they are not easy to use [1, 2]. SAS itself does not have any functions to do real page count. Instead, SAS borrows Microsoft Word processors to compute those numbers and put them in the final output by using TITLE or FOOTNOTE statements with "{page {\field{\fldinst{page}}} of {\field{\fldinst{numpages}}}}" or "Page ~{thispage} of ~{lastpage}". However, the page numbers generated by Microsoft Word processors contain field code information displayed as "Page {PAGE \MERGEFORMAT} of {NUMPAGES \MERGEFORMAT}" rather than the page numbers when Alt - F9 keys are pressed |
ods rtf, proc step, proc report, proc sql | source | 3 |
| 35 | A Simple Macro to Minimize Data Set Size | Amos Shu | Whenever you submit either SDTM or ADaM datasets to FDA, if any SAS dataset is great than 1 GB in size, FDA will ask you to split the dataset. In fact, since the length of a variable affects both the amount of disk space used and the number of I/O operations required to read and write the dataset, resizing text columns to fit the longest value within the column is applicable to every field that uses SAS datasets in their business. To help save resources and improve data mining efficiency, this paper discusses a simple macro to minimize the size of a SAS dataset | dataset, numeric variable, cdisc | source |
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| 36 | A Tale of Two SAS Technologies: Generating Maps of Topical Coverage and Linkages in SAS User Conference Papers | Barry deVille, Denise Bedford, Richard W. La Valley |
This paper discusses how two SAS technologies - Text Analytics and Content Categorization Suite -- were used to generate comprehensive and dynamic categories and clusters of the entire corpus of SAS user presentations from inception to the present. The goal of the two approaches is to improve access to the conference proceedings for SAS users and conference attendees in particular. The research focuses on the Conference Section access point and considers how text analytics and content categorization might enrich end user access. The findings of this research suggest that both Text Miner and the SAS Content Categorization Suite are powerful tools that can be used independently or in complement to improve access. Similarities w ere observed for Technology Solutions. This was not a surprising result given the dense treatment of technology issues at SAS Conferences and the technical expertise of conference attendees and Global Forum members | business intelligence, goodness of fit, proc freq | source | 21 |
| 37 | A Unique Approach to Create Custom Reports By Leveraging the Strengths of SAS and Excel | Amy Overby Wilkerson, Barbara Bibb, Brett Anderson, Mai Nguyen |
Survey projects often require custom reports to allow project staff to monitor production as well as various statistics from the collected data. At RTI, we've come up with a unique approach for creating custom reports for our projects by leveraging the strengths of SAS and EXCEL. In SAS we use PROC SQL to select and when necessary aggregate data. After processing the data in SAS, results are sent to EXCEL for reporting and graphics. In our paper, we will present a few sample reports, program codes and the detailed explanations of how these reports were created |
excel, proc sql, proc step | source |
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| 38 | Accessing SAS Code via Visual Basic for Applications | Jennifer Davies | SAS software has functionality that applications such as Microsoft Access or EXCEL do not have and vice versa. However, in some situations, Microsoft applications are preferred by the user over SAS for a multitude of reasons. This paper will discuss how to integrate the use of Microsoft applications with the functionality of SAS programs. This becomes very important when SAS Business Intelligence is not available. Depending on how SAS is installed in the user's organization, the programmer may have to access SAS on the PC or a server version of the application. This paper will explain the two methods used for calling SAS code fr om Visual Basic for Applications (VBA) Code (v6.5) |
sas base, lookup, sas enterprise guide, excel | source | 5 |
| 39 | Adaptive In-Memory Analytics with Teradata: Extending Your Existing Data Warehouse to Enable SAS High-Performance Analytics | Greg Otto, Tom Weber |
SAS High-Performance Analytics (HPA) rapidly analyzes big data in-memory. The Initial SAS HPA offering on Teradata co-locates SAS processes with the database processes on the same database nodes, in a separate database appliance that is limited to running the SAS In-memory analytics. To analyze data using SAS In-memory technology on the co-located appliance, data must be copied from the Teradata Enterprise Data Warehouse (EDW) to the appliance. This presents data management challenges, in addition to challenges of managing resources on processing nodes that are running multiple applications at the same time. SAS and Teradata have created a new in-memory analytics architecture that eliminates the need to copy data to a separate appliance, and still delivers the power of SAS HPA to data in the Teradata EDW | teradata, proc sql, in-memory analytics, memory usage, big data, in-database processing, parallel processing, data warehouse, proc step, dataset | source | 10 |
| 40 | Adding Graph Visualization on SAS ODS Output | Goutam Chakraborty, Miriam McGaugh, Shirmeen Virji, Yu Fu |
SAS tools are normally used to produce statistical graphs such as pie charts, bar charts, various plots, dashboards and even geographical maps. However, many SAS users may want to enhance their output by incorporating various diagrams such as networking, cluster, and process flows. In this paper, we introduce a method to add specific graphs onto SAS ODS output by interacting with "Graphviz" (an open source graph visualization software) in BASE |
sas base, put statement, file statement, association rules, data step | source | 4 |
| 41 | Adding New Rows in the ADaM Basic Data Structure: When and How | Mario Widel, Sandra Minjoe |
The ADaM (Analysis Data Model) BDS (Basic Data Structure) has specific rules to follow when adding columns or rows. Because there are limitations to what can be added as a column to a BDS structure, much of our derived content must be added as rows. This HOW uses a Vital Signs example, demonstrating the common BDS dataset need of adding analysis parameters and visits. Attendees will start with a general specification and mock-up, and use that to create metadata content that can be used for both detailed specification and within a define document. The resulting metadata will include both variable-level and parameter-level content and contain SAS code snippets. This is an intermediate-level HOW. Attendees are expected to be familiar with the analysis needs of clinical trials, CDISC, and submissions to FDA | adam dataset, bds, least squares mean, imputation, cdisc | source | 12 |
| 42 | Advanced Project Management beyond Microsoft Project, Using PROC CPM, PROC GANTT, and Advanced Graphics | Lindsey Puryear, Stephen B. Sloan |
The Challenge: Instead of managing a single project, we had to craft a solution that would manage hundreds of higher- and lower-priority projects, taking place in different locations and different parts of a large organization, all competing for common pools of resources. Our Solution: Develop a Project Optimizer tool using the CPM procedure to schedule the projects, and using the GANTT procedure to display the resulting schedule. The Project Optimizer harnesses the power of the delay analysis feature of PROC CPM and its coordination with PROC GANTT to resolve resource conflicts, improve throughput, clearly illustrate results and improvements, and more efficiently take advantage of available people and equipment |
proc step, excel | source | 7 |
| 43 | An Innovative Approach to Integrating SAS Macros with GIS Software Products to Produce County-Level Accuracy Assessments. | Audra Zakzeski, Robert Seffrin |
The National Agricultural Statistics Service produces an annual geospatial informational dataset called the Cropland Data Layer over the United States detailing the land cover over each state while focusing on the vast array of crops grown during the months of April through October. While calculating an accuracy assessment of the land cover over an entire state is a relatively simple process, calculating an accuracy assessment down to a county or crop specific level can be extremely time consuming and arduous. To simplify the process an innovative SAS 9.2 program was created integrating the efficiency of the SAS Macro language with the geospatial analytical capabilities of the GIS program ERDAS Imagine. The procedure is operated using a SAS/AF interface allowing analysts to easily investigate county level information |
bar chart, proc step, scatter plot, horizontal bar chart, proc reg, data cleaning, proc sql, regression analysis, macro variable | source | 8 |
| 44 | An Overview of Syntax Check Mode and Why It Is Important | Thomas E. Billings | The syntax check options direct the SAS system, when a syntax error occurs while compiling source code, to enter a special mode to scan the remainder of the job for syntax errors after the point where the first error occurred. In this mode, only the header portion of some data sets are created, permanent data sets are not replaced, but global commands are executed (also a very few PROCs). The options controlling the mode are explained and illustrated using simple test jobs. The effects of setting and resetting the option within a job are explored, and there are some surprises along the way. The risks of running with the options enabled vs. disabled are discussed | proc step, sas enterprise guide | source | 9 |
| 45 | Analysis and Visualization of E-mail Communication Using Graph Template Language | Atul Kachare | E-mail plays an important role in the corporate world as a means for collaboration and sharing knowledge. Analyzing e-mail information reveals useful behavioral patterns that usually carry implicit information about the sender's common activities and interests. This paper demonstrates how you can use SAS data processing capabilities to extract vital information based on corporate email communication data by reading e-mail data and graphically visualizing different communication patterns using SAS Graph Template Language (GTL). This analysis reveals different usage patterns: time-based e- mail volumes with milestone and response patterns; e-mail exchanges within and across groups; communication preferences such as small size e-mails, image-heavy e-mails, and the words most commonly used in communication | graph template language, sas enterprise guide, bubble plot | source | 14 |
| 46 | Analysis of Change in Sentiments towards Chick-fil-A after Dan Cathy?s Statement about Same-Sex Marriage Using SAS Text Miner and SAS Sentiment Analysis Studio View Paper View Poster | Goutam Chakraborty, Jeffin Jacob, Swati Grover |
Social media analysis along with text analytics is playing a very important role in keeping a tab on consumer sentiments. Tweets posted on twitter are one of the best ways t o analyze customer's sentiments following any post-corporate event. Although there are a lot of tweets, only a fraction of them are relevant to a specific business event. This paper demonstrates application of SAS Text Miner and SAS Sentiment Analysis Studio to perform text mining and sentiment analysis on t weets written about Chick-fil-A before and after the company's president's statement supporting traditional marriage. We find there is a huge increase in negative sentiments immediately following the company's president's statement. We also track a nd show that the change in sentiment persists through an extended period of time | sample, proc freq | source |
12 |
| 47 | Analyzing Partially Confounded Factorial Conjoint Choice Experiments Using SAS/IML | Chin Khian Yong, Song Lin Ng |
Completely Confounded Factorial Conjoint Choice Experiment (CCFCCE) design allows estimation of main effects and interaction effects. Since some of the effects will be confounded with the blocks in completely confounded factorial design, the conjoint choice design was extended to use of partially confounded factorial design, which is call Partially Confounded Factorial Conjoint Choice Experiment(PCFCCE). A 28 partially confounded factorial design with two replicate was applied to CCE in this study. In this study, all the responses were assume to be independent and hence the multinomial logit model follows. The log likelihood is nonlinear and hence the newton-raphson method is needed to estimate the parameters. PROC IML was used to generated the Newton-Raphson procedures. The result showed that all main effects and some of the first-order interaction effects were significant |
main effect, interaction effect, proc iml, proc step, logistic regression, odds ratio | source |
11 |
| 48 | Analyzing the Safewalk Program with SAS: Saving Shelter Dogs One Walk at a Time | Louise S. Hadden, Terri Bright |
The MSPCA (Massachusetts Society for Prevention of Cruelty to Animals) in Boston initiated the Safewalk Program in January 2009. This program was designed to enrich the experience of shelter dogs by providing training to volunteers and staff that allow dogs of varied backgrounds and temperaments to be exercised safely, as well as promoting behaviors encouraging adoption on the adoption floor. Intuitively the program was felt to increase adoption rates, particularly in "hard to adopt" dogs, but proving the benefits of the program was a challenge, especially given the greatly increased numbers of homeless dogs during the Safewalk Program time period. The MSPCA has recorded statistics on all animals in their care compiled with Chameleon software. Records for all dogs for 30 months prior to the initiation of Safewalk and 30 months after were selected from the Chameleon data base. This data extract was analyzed using multiple SAS procedures in SAS/STAT |
proc step, sample, proc freq | source |
12 |
| 49 | Are You Discrete? Patients' Treatment Preferences and the Discrete Choice Experiment | Beeya Na, Eric P. Elkin |
The discrete choice experiment (DCE) was designed for use in economics and marketing research to study consumer preferences. DCE has been increasingly used in health care research as a method to elicit patient preferences for characteristics of different types of treatments. In a DCE, attributes with varying levels are defined for treatments. Respondents are presented with pairs of hypothetical treatments that have different combinations of attribute levels and are asked to choose their preferred treatment. Analyzing the responses allows evaluation of the relative importance of the attributes and the trade-offs that respondents are willing to make between the attributes. This paper explains how to set up the data and discusses how to use the PHREG and LOGISTIC procedures to appropriately analyze the conditional logit model | proc step, proc phreg, proc logistic, logistic regression, conditional logistic regression, likelihood function, logistic distribution, estimate, null hypothesis | source | 6 |
| 50 | Array, Hurray, Array; Consolidate or Expand Your Input Data Stream Using Arrays | William E. Benjamin Jr. | You have an input file with one record per month, but need a file with one record per year. But you cannot use PROC TRANSPOSE because other fields need to be retained or the input file is sparsely populated. The techniques shown here enable you to either consolidate or expand your output data using arrays. Sorted files of data records can be processed as a unit using "BY Variable" groups and building an array of records to process. This technique allows access to all of the data records for a "BY Variable" group and gives the programmer access to the first, last, and all records in between at the same time. This will allow the selection of any data value for the final output record |
proc step, numeric variable, proc print, excel, dataset | source | 11 |
| 51 | Arrays - Data Step Efficiency | Harry Droogendyk | Arrays are a facility common to many programming languages, useful for programming efficiency. SAS DATA STEP arrays have a number of unique characteristics that make them especially useful in enhancing your coding productivity. This presentation will provide a useful tutorial on the rationale for arrays and their definition and use |
data step, variable list, temporary array, numeric variable, array | source | 9 |
| 52 | Assessing Drug Safety with Bayesian Hierarchical Modeling Using PROC MCMC and JMP | Richard C. Zink | Bayesian hierarchical models are advantageous for the analysis of adverse events in clinical trials. First, the models can borrow strength across related events within the MedDRA hierarchy. Second, the models can naturally temper findings likely due to chance. We describe the implementation of two Bayesian hierarchical models (Berry & Berry, 2004; Xia et al., 2010) used for the analysis of adverse events using PROC MCMC. Once models are fit, it is necessary to review convergence diagnostics to ensure that the posterior samples of parameters sufficiently approximate the target distribution. Numerous diagnostics are available within PROC MCMC, and we also present a freely available JMP add-in for MCMC (Markov Chain Monte Carlo) dynamically interactive diagnostics, summary statistics and graphics |
sample, adverse events, proc step, markov chain, credible interval, system organ class, proc means, type i error, logistic regression, posterior probability, treatment effect, logistic distribution, forest plot, histogram, regression analysis, missing value, odds ratio, relative risk | source | 13 |
| 53 | Assessing Model Adequacy in Proportional Hazards Regression | Michael G. Wilson | Proportional Hazards regression has become an exceedingly popular procedure for conducting analysis on right-censored, time-to-event data. A powerful, numerically stable and easily generalizable model can result from careful development of the candidate model, assessment of model adequacy, and final validation. Model adequacy focuses on overall fitness, validity of the linearity assumption, inclusion (or exclusion) of a correct (or an incorrect) covariate, and identification of outlier and highly-influential observations. Due to the presence of censored data and the use of the partial maximum likelihood function, diagnostics to as sess these elements in proportional hazards regression compared to most modeling exercises can be slightly more complicated. In this paper, graphical and analytical methods using a rich supply of distinctive residuals to address these model adequacy challenges are compared | regression analysis, hazard function, likelihood function, proc phreg, survival analysis, cumulative hazard, proportional hazards assumption, insurance risk score, proc calis, influential observation, estimate, correlation, sample, data cleaning, covariance, predictor variable, exponential distribution, numeric functions, frailty model, bootstrap, type ii error, adverse events | source | 36 |
| 54 | Automated and Customized Reports as a Single Image File Using Graph Template Language (GTL): A Case Study of Benchmarking Reports in Medical Research | Eric P. Elkin, Ginny Lai, Monarch Shah |
Site benchmarking reports give us an overview of basic demographic, clinical, and disease characteristics for the individual site with comparison to the study as a whole. A solution was needed for on-going reporting to over 250 study sites participating in a long-term study. The report needed to be concise, present data in both tables and figures, and provide information from both the individual study site and the entire study. (This objective could also arise in other fields, for example comparing each store's performance to the entire chain or each classroom's performance to the school district.) However, creating and combining tables and figures into an EXCEL or PowerPoint file can be challenging and time consuming. Alternatively, through a new graphical procedure available in SAS 9.2, one can customized reports and automate them to generate for each site and for future use |
bar chart, graph template language, proc step, proc means, sample | source | 11 |
| 55 | Automated Testing of Your SAS Code and Collation of Results (Using Hash Tables) | Andrew B. Ratcliffe | Testing is an undeniably important part of the development process, but its multiple phases and approaches can be under-valued. I describe some of the principles I apply to the testing phases of my projects and then show some useful macros that I have developed to aid the re-use of tests and to collate their results automatically. Tests should be used time and again for regression testing. The collation of the results hinges on the use of hash tables, and the paper gives detail on the coding techniques employed. The small macro suite can be used for testing of SAS code written in a variety of tools including SAS Enterprise Guide, SAS Data Integration Studio, and the traditional SAS Display Manager Environment | hash table, dataset, regression analysis, macro definition, sas enterprise guide, proc step | source | 8 |
| 56 | Automating the Flow of Presentations in Coder's Corner or Quick Tips | Erik W. Tilanus | The Quick Tips section (former Coder's Corner) is characterized by a rapid flow of many short presentations. Reading bios and starting presentations by hand is slowing down this flow. So we use SAS to automate this flow | options statement, macro variable | source | 4 |
| 57 | Back Up Your Sources During Development: A Stack of Base SAS Scripts | Hans Sempel | If you're a Base SAS programmer and if you ever lost your code due to system crashes or overwriting your code, this might be the solution. The presented code provides a means of backing up your code during development, you can use it to save increments or you can use it for versioning and you can restore the code you're working on to an earlier version | sas base, sas programmer | source | 7 |
| 58 | Be Customer Wise or Otherwise: Combining Data Mining and Interactive Visual Analytics to Analyze Large and Complex Customer Resource Management (CRM) Data | Aditya Misra, Junyao Ji, Kam Tin Seong |
In this competitive world, there are more companies, such as our project sponsor, a global logistics company, are exploring the potential use of data mining techniques to make informed and intelligent marketing strategies. We conducted a customer segmentation study using a comprehensive set of data extracted fro m their existing operational system which contains both transaction records and customer profiles. This paper aims to report on our experience gained in using the interactive visual analytics an d data mining techniques of SAS JMP to perform customer segme ntation analysis in combination with RFM (Recency, Frequency and Monetary), a method used for determining the Customer Lifetime Value (CLV) . We share our views on how interactive visual analytics and data mining techniques can empower everyday data analysts to gain useful insights and formulate informed decisions by demonstrating the interactive data visualization techniques of JMP such as histogram and parallel plots | cluster analysis, proc freq, data mining, proc fastclus, correlation, customer segmentation, sas visual analytics, outlier, histogram, logistic distribution, sas enterprise guide | source | 11 |
| 59 | Be Prompt! Do it Now! Creating and Using Prompts in SAS Enterprise Guide | Ben Cochran | Several years ago, Enterprise Guide was given the ability to create prompts. The developers at have continually enhanced this process over the last several releases of Enterprise Guide (EG) and today the user can create quite a wide range of prompts to be deployed in the EG environment. This paper takes a look at some of the prompts that can be created | macro variable, sas enterprise guide | source | 12 |
| 60 | Behind the Scenes with SAS: Using Custom Graphics in SAS Output | Louise S. Hadden | SAS provides many opportunities to add customized images to SAS ODS output. This presentation will demonstrate various ways to add custom backgrounds to tabular and graphic output; add custom images to titles, footnotes, ODS text fields and tabular output; and add custom "fills" to SAS/GRAPH maps and graphs. Some possible uses of custom images include a company logo embedded in SAS output, graphic displays of positive or negative outcomes, and watermarks containing "draft" or "confidential". The SAS code to accomplish all these potential uses, and more, will be shown |
sample, proc step | source |
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| 61 | Being Continuously Discrete (or Discretely Continuous): Understanding Models with Continuous and Discrete Predictors and Testing Associated Hypotheses | David J. Pasta | Often a general (or generalized) linear model has both discrete predictors (included in the CLASS statement) and continuous predictors. Binary variables can be treated either as continuous or discrete; the resulting models are equivalent but the interpretation of the parameters differs. In many cases, interactions between discrete and continuous variables are of interest. This paper provides practical suggestions for building and interpreting models with both continuous and discrete predictors. It includes some examples of the use of the STORE statement and PROC PLM to understand models and test hypotheses without repeating the estimation step |
least squares mean, estimate, interval variable, binary variable, proc step, proc plm, proc genmod, response variable, ordinal variable, degrees of freedom, proc glm, main effect | source | 19 |
| 62 | Benchmarking SAS I/O: Verifying I/O Performance Using fio | Spencer Hayes | Input/Output (I/O) throughput is typically the most important computing aspect of a SAS environment. Bandwidth requirements ranging from 25MB/sec/core to 135MB/sec/core are common in a high-performance SAS system. Insuring that the storage subsystem can meet the demands of SAS is critical to delivering the performance required by the business and user community. Ideally, SAS administrators could run real-world SAS jobs to benchmark the I/O subsystem. However, technical and logistical challenges frequently make that option impractical. The open source software tool called "fio" provides a method for accurately simulating I/O workloads. It is configurable to match closely the existing or expected I/O profile for a SAS environment | cpu time, performance tuning | source | 7 |
| 63 | Best Practices for Deploying Your SAS Applications in a High-Availability Cluster | Helen Pan | Are you frustrated when software or hardware failures interrupt your SAS system? A high-availability cluster with high-availability (HA) cluster with HA software can help you by providing failover protection for your SAS applications, thus reduce your system downtime. This paper discusses what you should consider when deploying your SAS applications or servers in an HA cluster, as well as the following best practices of the deployment process: HA cluster architecture with a SAS deployment. SAS installation and deployment in an HA cluster. Dependency configuration of the SAS servers. Consideration about the SAS clients when a failover happens | data cleaning, sas management console, sysrc, sas enterprise guide | source | 8 |
| 64 | Best Practices in Enterprise Data Governance | Nancy A. Rausch, Scott Gidley |
Data governance combines the disciplines of data quality, data management, data policy management, business process management, and risk management into a methodology that ensures important data assets are formally managed throughout an enterprise. SAS has developed a cohesive suite of technologies that can be used to implement efficient and effective data governance initiatives, thereby improving an enterprise's overall data management efficiency. This paper discusses data governance best practices. It explains where and how SAS capabilities (such as the Business Data Network, Reference Data Management, Data Federation, Data Quality, Data Management, and Master Data Management) can be used to ensure that data governance initiatives remain successful, continue to deliver overall return on investment, and gain buy-in across the enterprise | data governance, data cleaning, reference data | source | 9 |
| 65 | Best Practices in SAS Data Management for Big Data | Malcolm Alexander, Nancy A. Rausch |
This paper discusses capabilities and techniques for optimizing SAS data management products for Big Data. It demonstrates how SAS supports Apache Hadoop technologies, including details on the various languages available in the platform and how to use them with SAS. An overview of SAS Data Management integration with the SAS LASR platform is also provided. Practical tips and techniques are presented, including case studies that illustrate how to best use the SAS data management feature set with big data | big data, data cleaning, proc sql | source | 13 |
| 66 | Best Practices: PUT More Errors and Warnings in My Log, Please! | Kirk Paul Lafler, Mary F. O. Rosenbloom |
We all like to see a SAS log that is free from errors and warnings, but did you know that you can add your own errors and warnings to the log with PUT statements? Not only that, but you can incorporate this technique into your regular coding practice to check for unexpected data values. This paper will explore the rationale and process of issuing user- created error and warning messages to the SAS log, along with a number of examples to demonstrate when this is useful. Finally, we will propose an upgrade to the next version of SAS involving a user-specified keyword with its own color in the log | put statement, sas log | source | 5 |
| 67 | Big Data - Dream IT. Build IT. Realize IT. | Paul Kent, Andy Mendelsohn |
Presentation | sas grid, high-performance sas, oracle, sas analyst, sas enterprise guide, in-memory analytics, business intelligence | source | 64 |
| 68 | Big Data Meets Text Mining | Alicia Bieringer, James A. Cox, Russell Albright, Zheng Zhao |
Learning from your customers and your competitors has become a real possibility because of the massive amount of web and social media data available. However, this abundance of data requires significantly more time and computer memory to perform analytical tasks. This paper introduces high-performance text mining technology for SAS High- Performance Analytics. Text parsing, text filtering, and dimension reduction are performed using the new HPTMINE and HPTMSCORE procedures in SAS High-Performance Analytics Server and are accessed conveniently from within SAS High-Performance Enterprise Miner. The paper demonstrates and discusses the advantages of this new SAS functionality and provides real-world examples of the types of performance improvements you can expect | big data | source | 12 |
| 69 | Big Data, Fast Processing Speeds | Kevin McGowan | As data sets continue to grow, it is important for programs to be written very efficiently to make sure no time is wasted processing data. This paper covers various techniques to speed up data processing time for very large data sets or databases, including PROC SQL, DATA STEP, indexes and SAS macros. Some of these procedures may result in just a slight speed increase, but when you process 500 million records per day, even a 10% increase is very good. The paper includes actual time comparisons to demonstrate the speed increases using the new techniques |
proc sql, proc step, grid computing, cpu time, missing value, group by, sas programmer, data step, where statement, sas programming, macro definition, macro variable | source | 17 |
| 70 | Bigger Data Analytics: Using SAS on Aster Data and Hadoop | John Cunningham, Momolue Kiadii |
proc sql, proc step, big data, create view, sample, business intelligence, data step | source | 13 | |
| 71 | Branding Yourself Online | Kirsten Hamstra, Shelly Goodin |
Your online reputation matters. Whether you are using social media for professional or personal reasons, it's important to understand and control your public persona. To further your career, build your business, or enter the workforce, you can maximize your positive exposure by knowing where and how to engage online. This paper highlights some of the best practices for online engagement, provides suggestions for where to engage, and showcases some examples. For anyone engaging in social media who wants to better understand the impact of their contributions and control their online reputation, this paper is for you. Discover how to · Craft an incredible bio Harness the power of search engine optimization (SEO) Strengthen your online reputation Engage-where and how to do it * Make meaningful connections | source | 3 | |
| 72 | Bridging the Gap Between SAS Applications Developed by Business Units and Conventional IT Production | Euwell Bankston, Thomas E. Billings |
Multiple factors are involved in the decision by an enterprise to decide whether to allow a business unit to run its own production versus having SAS applications developed by business units run in conventional IT production. There can be a wide gap between the business unit view of "production-ready" programs vs. core IT standards for production systems. The nature of the gap is discussed here, and also the risks of business-run production. Specific suggestions are made regarding whether IT and business should have joint ownership of critical SAS applications vs. segregated roles, and when/how should SAS-based systems be migrated into a fully controlled IT production environment. Note: This article is a discussion paper and a work-in-progress. It should be interpreted as a starting point for discussions between the business and IT departments and not as the official policy or practices of Union Bank | data cleaning, sas base, sas enterprise guide, sample, production run, relational database | source | 10 |
| 73 | Bringing Churn Modeling Straight to the Source: SAS and Teradata In-Database Model Development | Jonathan Wexler, Karl Krycha |
This paper provides an overview of SAS in-database capabilities available on Teradata and uses a typical business application to illustrate how those capabilities can be used to greatly improve the end-to-end analytical modeling process. We will focus primarily on several benefits of using SAS Analytics Accelerator for Teradata. This product reduces modeling execution time from hours to seconds, allowing users to produce models faster. In addition, it can greatly reduce data duplication and data movement by performing key SAS analytic computations within the Teradata database | sample, proc step, churn prediction, regression analysis, teradata, data wrangling, proc princomp, logistic regression, data mining, in-database processing, churn, proc score, proc reg, raw data, logistic distribution, proc sql, proc sort, churn propensity, customer retention, response variable, data science, stepwise selection, dataset | source | 9 |
| 74 | Build Prognostic Nomograms for Risk Assessment Using SAS | Dongsheng Yang | Nomograms from multivariable logistic models or Cox proportional -hazards regression are a popular visual plot to display the predicted probabilities of an event for decision support. In this paper, we show how to build a prognostic nomograms after fitting a multivariable model , including how to assign points for each predictor under different situations such as main effect, interaction, piecewise linear effects . Furthermore, we also show how to use a power tool, graphic template language to construct a nogogram Finally, a SAS macro is developed to generate a nomogram | regression analysis, main effect, proc sgplot, logistic distribution, logistic regression, dataset, binary variable, proc step | source |
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| 75 | Building a SAS Grid Support Capability in the Enterprise | Andy Birds, Chris Rigden |
Building SAS Grid support capability within an organization's IT support function requires IT managers to consider many different aspects. There is a need for SAS support to fit seamlessly into the Enterprise IT support model and comply with IT policies based on standard frameworks such as ITIL, while maintaining a level of business engagement far beyond that which is required of traditional IT support teams. We will outline a practical framework including the policies and procedures required to support a SAS Grid in a way that provides a solid foundation that meets the immediate and ongoing business requirements. We will discuss how to embed SAS into an organization's standard IT processes and how to ensure that active business engagement is a standard activity | sas grid, capacity planning, business intelligence, batch job, memory usage, sas administrator | source | 12 |
| 76 | Building Traceability for End Points in Analysis Data Sets Using SRCDOM, SRCVAR, and SRCSEQ Triplet | Qunming Dong, Tathabbai Pakalapati, Xiangchen (Bob) Cui |
To be compliant with ADaM Implementation Guide V1.0, traceability feature should be incorporated to possible extent in study analysis datasets. SRCDOM, SRCVAR, and SRCSEQ triplet are used to establish data point traceability in ADaM datasets. It can facilitate the transparency in FDA submission data, build confidence in analysis results, help efficient programming validation, speed up the overall review progress by FDA reviewers, and build a good relationship with FDA reviewers. This paper provides various examples of applying SRCDOM, SRCVAR, and SRCSEQ triplet to establish traceability in efficacy ADaM datasets from Cystic Fibrosis therapeutic area, and shows the art of applying the triplet to different scenarios | adam dataset, sample, adamig, cdisc | source |
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| 77 | Calculating Subset-Weighted Analysis in PROC SURVEYFREQ and PROC GENMOD | David M. Thompson, Jessica Hale, Paul Darden |
Stratum-specific weighted analysis is available in SAS procedures such as PROC SURVEYMEANS & SURVEYLOGISTIC, which include the DOMAIN statement. However, other procedures that can model correlated outcomes, including PROC GENMOD, do not. This presentation demonstrates a method of assigning individual weights to each record in a dataset to perform weighted subset analysis on a correlated outcome without creating domain variables or transferring analysis to a separate program. This demonstration employs data from the National Immunization Survey Teen (NIS-Teen).The NIS-TEEN includes provider & household weights and details the immunization status for several vaccines for each respondent. To compare weighted estimates of vaccination rates, where respondents receive several vaccines and had adequate provider data, a domain variable must be created or analysis moved into a different program |
proc step, proc surveyfreq, proc genmod, proc freq, proc calis, where statement, estimate, sample, repeated measures, sample size, standard deviation | source |
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| 78 | Can You Create Another PowerPoint for Me? How to Use Base SAS and DDE to Automate Snappy PowerPoint Presentations | Mitchell Weiss, Scott Koval |
Your supervisor appreciates your wonderful and informative SAS reports. How many times have you heard, "Great! Now can you compile all the SAS reports into a PowerPoint presentation?" At that moment you wish you could press a button to automate the process because SAS programmers spend way too much time updating PowerPoint slides. This paper offers solutions to make your life easier by building upon techniques from Vyverman (2005 that discussed using SAS to take advantage of Dynamic Data Exchange (DDE) to write through MS EXCEL to MS PowerPoint. The goal is to free data analysts from PowerPoint tyranny by enabling efficient and repeatable PowerPoint presentations |
excel, macro variable, proc step, proc gmap | source | 9 |
| 79 | Cascading Style Sheets: Breaking Out of the Box of ODS Styles | Kevin D. Smith | Although cascading style sheets ( CSS) have been available in various forms in ODS since SAS 9.2, SAS 9.4 is the first version that fully uses the new style engine's architecture. Using the new CSS engine, you can apply custom styles based on column names, BY group variables, and anchor names. You can specify dynamic attribute values using SYMGET, RESOLVE, DYNAMIC, and EXPRESSION functions just like in PROC TEMPLATE styles. If you want to break out of the box of ODS styles and do some truly original styling, the CSS techniques in this paper takes you there |
proc step, by group processing, proc template, ods html, heatmap, ods pdf, proc report, sample, output delivery system | source | 38 |
| 80 | Census Retires PROC COMPUTAB | Christopher J. Boniface, Hung Pham, Nora Szeto |
PROC COMPUTAB is used to generate tabular reports in a spreadsheet like format. PROC COMPUTAB has been around a long time. It has served us well at Census, but it is time to replace it with reporting procedures that are more modern. This paper will show how we converted PROC COMPUTAB to PROC TABULATE and PROC REPORT to create complex Census tables. We will also discuss how we created hundreds of tabular EXCEL tables using ODS TAGSETS EXCELXP. Furthermore, we will show how we used PROC TABULATE as a computing engine to handle overlapping format ranges and PROC REPORT a s the reporting tool to control the appearance of the tabular EXCEL tables including column widths, row heights and formats |
proc step, proc report, excel, proc tabulate, missing value, proc format, proc calis | source | 12 |
| 81 | Checking Out Your Dates with SAS | Christopher J. Bost | Checking the quality of date variables can be a challenge. PROC FREQ is impractical with a large number of dates. PROC MEANS calculates summary statistics but displays results as SAS date values. PROC TABULATE, however, can calculate summary statistics and format the results as dates. This paper reviews these approaches plus the STACKODS option in SAS 9.3 that might make PROC MEANS the preferred method for checking out your dates |
proc step, proc freq, proc tabulate, date variable, missing value, proc means, sample size, sample, dataset, proc print, sysdate9 | source | 5 |
| 82 | Chi-Square and T-Tests Using SAS: Performance and Interpretation | Jennifer L. Waller, Maribeth H. Johnson |
Data analysis begins with data clean up, calculation of descriptive statist ics and the examination of variable distributions. Before more rigorous statistical analysis begins, many statisticians perform basic inferential statistical tests such as chi-square and t-tests to assess unadjusted associations. These tests help to guid e the direction of the more rigorous analysis. How to perform chi -square and t-tests will be presented. We will examine how to interpret the output, where to look for the association or difference based on the hypothesis being tested, and propose next steps for further analysis using example data | sample, nominal variable, chi-square test, interval variable, proc means, proc ttest, proc step, odds ratio, data science, standard deviation, confidence interval, proc freq, relative risk, goodness of fit, normal distribution, homoscedasticity, missing value, inferential statistics, box plot, one-sample t-test, heteroscedasticity, variable list, frequency analysis, ordinal variable, histogram, class variable | source | 12 |
| 83 | Clinician Prescribing Feedback Site: Comparing Clinician Prescribing Habits and Providing Actionable Patient Lists | Michael Nash | Which doctor is prescribing the most non-formulary medications? Which patients are on a brand drug when an equivalent generic is available? These questions and many more can be answered when using the Clinician Prescribing Feedback Site. This secure intranet site at Kaiser Permanente Northwest uses SAS/GRAPH HBAR and VBAR charts to compare clinician prescribing habits. Drill down to compare all clinics, or all departments, or all doctors within a Clinic/Specialty. Drill down even further to find patient lists so pharmacists or clinician staff can perform outreach to members. The Clinician Prescribing Feedback Site also tracks costs and patients month to month. This paper will show you how to create linked html files using SAS PROC GCHART and the HTML= option |
macro call, proc gchart, output delivery system, proc step, macro definition | source | 12 |
| 84 | CLISTS: Improve Efficiency of TSO Applications Using Mainframe SAS | Russell Hendel | Have you been spending a few hours every month submitting several dozen SAS jobs to mainframe systems using an IBM TSO environment with the Interactive System Productive Facility (ISPF)? You know that within SAS, SAS macros can efficiently manage repetitive tasks; but how do you manage repetitive tasks with JCL, the TSO control language? CLIST is precisely what you need: It enables you to automate repetitive tasks that use JCL and SAS. CLIST is an easy language to learn, requiring no former knowledge and using only a handful of basic commands. We present illustrative CLIST code covering basic groups of CLIST commands. People already familiar with JCL and SAS who write jobs using both of them will benefit from this presentation | sample, proc step, string functions, variable, proc means | source | 6 |
| 85 | Coaching SAS Beginner Programmers: Common Problems and Some Solutions | Peter Timusk | This paper will present a number of problems SAS beginner programmers encounter when first writing SAS programs. The paper will cover three cases and show how pointing out patterns to beginner programmers will aid them in avoiding errors in their SAS code. Here are the three problems we will look at in this paper. 1. The IF statement and the order of operations of logic operators and the use of parentheses. 2. The different between speaking a logic expression and how to code a logic expression with an example from setting ranges for displaying a variable's count in five columns. 3. In the final example a complex assignment is presented to the beginning programmer and they do not know how to proceed because they are overwhelmed with the complexity. Then this examples shows how seeing patterns in the specifications can help make the work straight forward for a beginner and make a complex specification simple | if-then-else, sample | source | 4 |
| 86 | Communicating Standards: A Code Review Experience | David A. Scocca | We need ways to pass along good programming practices. All but the smallest companies will have programmers with varying levels of tenure and experience. Standards and best practices change, but in a deadline -driven world, we re-use old programs with minimal revision. Programmers develop habits and can be slow to incorporate new approaches that might simplify code or improve performance. We developed and rolled out an in -house code review process to address these issues. This paper reports our strategy for promoting and performing the reviews and describes the results | clinical programming, clinical trial | source | 4 |
| 87 | Commuting Time and Accessibility in a Joint Residential Location, Workplace, and Job Type Choice Model | Ignacio A. Inoa, Nathalie Picard, André de Palma |
The effect of an individual-specific measure of accessibility to jobs is analyzed using a three-level nested logit model of residential location, workplace, and job type choice. This measure takes into account the attractiveness of different job types when the workplace choice is anticipated in the residential location decision . The model allows for variation in the preferences for job types across individuals and accounts for individual heterogeneity of prefer ences at each choice level in th e following dimensions: education, age, gender and children. Using data from the Greater Paris Area, estimation results indicate that the individual -specific accessibility measure is an important de terminant of the residential location choice and its effect strongly differs along the life cycle. Results also show that the job type attractiveness measure is a more significant predictor of workplace locations than the standard measures | logistic regression, sample, sample size, proc hpsplit | source | 13 |
| 88 | Computing Direct and Indirect Standardized Rates and Risks with the STDRATE Procedure | Yang Yuan | In epidemiological and health care studies, a common goal is to establish relationships between various factors and event outcomes. But outcome measures such as rates or risks can be biased by confounding. Y ou can control for confounding by dividing the population into homogeneous strata and estimating rate or risk based on a weighted average of stratum-specific rate or risk estimates. This paper reviews the concepts of standardized rate and risk and introduces the STDRATE procedure, which is new in SAS/STAT 12.1. PROC STDRATE computes directly standardized rates and risks by using Mantel-Haenszel estimates, and it computes indirectly standardized rates and risks by using standardized morbidity/mortality ratios (SMR). PROC STDRATE also provides stratum-specific summary statistics, such as rate and risk estimates and confidence limits |
confidence interval, ods graphics, proc step, output delivery system | source | 19 |
| 89 | Configure Your Foundation SAS Client Easily and without Risk | Peter Crawford | For your client install of Foundation SAS, don't use the default provided, instead, use the simple features in this paper and presentation, to support the flexibility you want. This method of applying options as SAS starts, eliminates risky techniques of the past when developers would update the configuration file provided by SAS. The technique is described and demonstrated with examples for the Microsoft Windows environment for foundation SAS, but the issues are very similar if launching SAS clients and servers on Unix(including Unix on zOS). There is a small overhead with this proposal, but if you review the paper, I'm sure you will consider it worthy enough to give it a try | proc step, proc contents | source | 7 |
| 90 | Considerations and Techniques for Analyzing Domains of Complex Survey Data | Taylor Lewis | Despite sounding like a straightforward task, making inferences on a domain, or subset, of a complex survey data set is something that is often done incorrectly. After briefly discussing the features constituting "complex" survey data, this paper explains the risks behind simply filtering the full data set for cases in the domain of interest prior to running a SAS/STAT SURVEY procedure such as PROC SURVEYMEANS or PROC SURVEYREG. Instead, it will be shown how one should use the DOMAIN statement or create a domain-specific analysis weight. Also discussed in detail are considerations and approaches to the very common objective of testing whether the difference between two domain means is statistically significant |
sample, proc step, proc surveyreg, degrees of freedom, proc surveymeans, proc calis, regression analysis, estimate, covariance, random sampling, binary variable, response variable, finite population correction, proc reg, sample size, proc ttest | source | 12 |
| 91 | Considerations for Creating an In-House SAS User Group in a Geographically Disbursed Organization | Stefanie Reay | This paper reviews considerations for creating in-house SAS user groups in geographically disbursed organizations, in which in-person user group meetings are not cost-effective or not feasible, but for which an in-house SAS user group would still be beneficial. It defines in-house SAS user groups, and overviews the resources available from SAS for starting and continuing an in-house SAS user group. It discusses benefits and challenges of starting and maintaining an in-house SAS user group, options for organizational structures of in-house SAS user groups, and unique needs for a group of this nature to be successful in national and global organizations. It also reviews technologies that could be beneficial for bridging the communication gap in a user group of this type | sample, sas enterprise guide | source | 4 |
| 92 | Consistent and Organized Analysis: Moving Beyond Pie-in-the-Sky to Actual Implementation via SAS Enterprise Guide | Amber Schmitz | Consistency and timeliness are two goals that every reporting department strives to achieve. SAS Enterprise Guide provides tools that support these goals that many analysts overlook. The goal of this paper is to demonstrate the use of SAS Enterprise Guide to construct a master project template that can be used to report standard metrics for various clients. The template is built around SAS Enterprise Guide tools that allow for consistent analysis methods, project organization, and version control documentation. This paper explores: 1) Utilizing program code for efficient data pulls, 2) Using SAS Enterprise Guide tasks for analysis, 3) Exploiting built-in tools such as Notes and Process Flows for template organization and version control, 4) Demonstrating business intelligence via built-in graphs | sas enterprise guide, proc print, macro variable, keep statement, inner join, line chart | source | 8 |
| 93 | Converting Thousands of Variables from Character to Numeric: The One-Hour Fix | Kamya Khanna, Wen Song |
At the conclusion of many survey-based data collecting projects, recoding the hundreds and thousands of character vari- ables to 'reserved scale' specified numeric variables is a uncomplicated but cumbersome task for SAS | raw data, proc freq, excel, proc step, macro variable, if-then-else | source | 6 |
| 94 | Cool Views | Elizabeth Axelrod | Looking for a handy technique to have in your toolkit? Consider SAS Views, especially if you work with large data sets. After a brief introduction to Views, I'll show you several cool ways to use them that will streamline your code and save workspace | data step, cpu time, proc step, view, proc sort, dataset, proc freq | source | 7 |
| 95 | Cox Proportional Hazard Model Evaluation in One Shot | Polina Kukhareva | Cox proportional hazard models are often used to analyze survival data in clinical research. This article describes a macro that makes producing the correct diagnostics for Cox proportional hazard models fast and easy. The macro has three advantages over performing all the diagnostics one by one. First, it makes it easy to run diagnostics for a long list of similar models. Second, it allows the specification of the variables for which diagnostics should be run. Third, it produces a comprehensive list of plots and tables necessary for evaluation of the Cox proportional hazard model assumptions as recommended in the SAS course "Survival Analysis Using the Proportional Hazards Model." This macro can help save hours of code-writing time for a programmer who performs survival analysis | proportional hazards assumption, predictor variable, proc phreg, binary variable, estimate, survival analysis, macro parameter, macro variable, nominal variable, clinical trial, interval variable, main effect, proc step, proc sql | source | 11 |
| 96 | Create Your First SAS Stored Process | Angela M. Hall, Patricia S. Aanderud |
Learn how to convert a simple SAS macro into three different stored processes! Using examples from the newly released book 50 Keys to Learning SAS Stored Processes, you'll see how to build a stored process that allows users to filter their results for the report of their dreams. You'll learn how to use the SAS Prompt Framework to customize your stored process quickly and efficiently. No experience required! Suitable for beginners. SAS 9.2 and later | macro variable, sas enterprise guide, automatic variable, date variable | source | 10 |
| 97 | Create Your Own Client Apps Using SAS Integration Technologies | Chris Hemedinger | SAS Integration Technologies allows any custom client application to interact with SAS services. SAS Enterprise Guide and SAS Add-In for Microsoft Office are noteworthy examples of what can be done, but your own applications do not have to be that ambitious. This paper explains how to use SAS Integration Technologies components to accomplish focused tasks, such as run a SAS program on a remote server, read a SAS data set, run a stored process, and transfer files between the client machine and the SAS server. Working examples in Microsoft .NET (including C# and Visual Basic .NET) as well as Windows PowerShell are also provided | sas log, sample, proc step, sas enterprise guide, sas output, dataset | source | 15 |
| 98 | Creating a Management-Friendly HTML Report Using SAS ODS Markup, Style Sheets, and JavaScript | Robert Mann, Rosely Flam Zalcman |
Multiple frequency and statistics tables are exported to HTML files using SAS ODS (Output Delivery System) and are integrated into a single portable HTML document. Active links and embedded JavaScript menus facilitate easy access to over a hundred tables while a single cascading style sheet ensures a common display format |
proc freq, frequency analysis, proc step | source |
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| 99 | Creating a Useful Operational Report | Andrew Hummel, Robert Goldman |
Metrics and reports are highly valued by operational decision makers in order to make informed and data driven conclusions. However, there is a balance between presenting useful organized knowledge and displaying page upon page of raw useless data. SAS has the power to produce a wide range of sophisticated graphs that are meaningful. The challenge is to produce a graph that quickly and accurately measures and displays the real world operation while allowing decision makers to make operationally beneficial determinations. There are numerous SAS papers that give step-by-step instructions on how to build a graph; this is not such a paper. The goal of this paper is to show how we approached the challenge of building an operational report and the techniques used | bar chart, proc gchart, proc step, sas enterprise guide | source | 9 |
| 100 | Creating an Interactive SAS E-Textbook with iBooks Author for the iPad | William 'Gui' Zupko II | Mobile media is becoming more popular and prevalent in today's workplace. Even though few apps on the iPad actively run SAS programs, the iPad can be utilized as a teaching tool and reference database. iBooks Author allows for the creation of interactive textbooks from anybody that allow users to learn SAS in a self-paced environment. Widgets allow screenshots to show how programs are run and use reviews to check comprehension. These widgets also allow the inclusion of Keynote slides. These interactive textbooks are especially excellent for SAS conferences, as the text can be applied directly and in- clude PowerPoint presentations, creating a mobile library that can be used in an easily accessible format, perfect for reference or training | sas output, sas function | source | 11 |
| 101 | Creating and Customizing the Kaplan-Meier Survival Plot in PROC LIFETEST | Warren F. Kuhfeld, Ying C. So |
If you are a medical, pharmaceutical, or life sciences researcher, you have probably analyzed time-to-event data (survival data). One of several survival analysis procedures that SAS/STAT provides, the LIFETEST procedure computes Kaplan-Meier estimates of the survivor functions and compares survival curves between groups of patients. Y ou can use the Kaplan-Meier plot to display the number of subjects at risk, confidence limits, equal-precision bands, Hall-Wellner bands, and homogeneity test p-value. Y ou can control the contents of the survival plot by specifying PROCEDURE OPTIONS with PROC LIFETEST. When the PROCEDURE OPTIONS are insufficient, you can modify the graph templates with SAS macros. This paper provides examples of survival plot modifications using PROCEDURE OPTIONS, graph template modifications using macros, and style template modifications |
kaplan-meier plot, macro variable, proc step, macro definition | source | 20 |
| 102 | Creating Clark Error Grid with SAS/GRAPH, the SAS/GRAPH Annotate Facility, and SAS Macro Applications | John Shin, Yongyin Wang |
Clarke Error Grid Analysis has been widely used in the accuracy quantification of blood glucose values obtained from continuous glucose monitoring (CGM) sensor against reference values from meter or YSI instruments. A vivid graphic presentation of clinical accuracy of CGM sensor data is preferred by statisticians and reviewers of regulatory agencies. SAS/GRAPH Annotate facility is a powerful tool for customizing, enhancing, or changing the features of graphic outputs. Clarke Error Grid breaks down a scatterplot of estimated glucose values versus reference values into five zones: A, B, C, D, and E. This presentation demonstrates how to use SAS/GRAPH, the SAS/GRAPH Annotate facility, and SAS macro applications together to create such Error Grid for clinical accuracy determination of CGM data against meter or YSI glucose values |
proc step, proc gplot, sample | source | 11 |
| 103 | Creating Graph Collections with Consistent Colors Using ODS Graphics? | Philip R. Holland | Collections of line graphs or bar charts, where the graph data is grouped by the same value, are frequently used to identify differences and similarities in behavior. Unfortunately, by default, the colors used for each line can change across the graph collection if some group values are not present in every graph. In SAS/GRAPHTM this problem has been solved by generating SYMBOL or PATTERN statements based on the data, or using annotation to create all of the graph lines, bars and legends. Neither of these solutions is readily available in ODS Graphics, so this paper will explore how to solve this problem using macros to implement the graphic "layer" approach in PROC SGPLOT and PROC TEMPLATE, so that the user has complete control over the data colors and styles applied to every graph |
proc step, proc sgplot, macro loop, %if-%then-%else, macro variable, macro definition, sample | source | 14 |
| 104 | Creating Interval Target Scorecards with Credit Scoring for SAS Enterprise Miner | Miguel Maldonado, Naeem Siddiqi, Susan Haller, Wendy Czika |
Credit Scoring for SAS Enterprise Miner has been widely used to develop binary target probability of default scorecards, which include scorecards for application and behavior scoring. Extending the weight-of-evidence binned scorecard methodology to interval-based targets such as loss given default and exposure at default is a natural step in the evolution of scorecard development methodologies. This paper discusses several new methods available in Credit Scoring for SAS Enterprise Miner that help build scorecards that are based on interval targets. These include cutoffs that convert the interval target to a binary form and an algorithm that maintains the continuous nature of the interval target. Each method is covered in detail, and all methods are compared to determine how they perform against known target values | spearman correlation, credit scoring | source | 14 |
| 105 | Creating metadata environment from existing one for testing purpose | Jouni Javanainen | Most organizations have a need for development, test and production environments, either in the same physical platform or on separate platforms. Separate environments may not use the same ports of communication. It is possible to define specific communication ports by using migration packages from existing metadata to avoid disturbing the other environments. In addition to this only a few extra finishing steps are required, such as defining paths of directories or libraries. It is important that this type of environment can be created through a formal process quickly, reliably and efficiently. This paper covers how easily identical environments can be created for development and testing purposes utilizing existing SAS tools | sas management console, business intelligence | source | 9 |
| 106 | Creating ZIP Code-Level Maps with SAS | Barbara B. Okerson | SAS, SAS/GRAPH, and ODS graphics provide SAS programmers with the tools to create professional and colorful maps. Provided with SAS/GRAPH are boundary files for U.S. states and territories, as well as internal boundaries at the county level. While much data and results can be displayed at this level, often a higher degree of granularity is needed. The U.S. Census Bureau provides ZIP code boundary files in ESRI shapefile format (.shp) by state for free download and import into SAS using SAS PROC DATAIMPORT. This paper illustrates the use of these ZIP code tabulation area (ZCTA) files with SAS to map data at a ZIP code level. Example maps include choropleth, distance, and heat maps. Examples included in this paper were created with version 9.2 of SAS on a Windows 64-bit server platform and use Base SAS, SAS/STAT and SAS/GRAPH. The techniques represented in this paper require a SAS/GRAPH license but are not platform-specific and can be adapted by beginning through advanced SAS users |
heatmap, proc step, proc gmap | source | 9 |
| 107 | Creating ZIP Files with ODS | Jack Hamilton | ZIP files are a convenient way to bundle related files together, and can save storage space at the same time. The ZIP format is used internally by SAS for SAS Enterprise Guide projects, but until SAS 9.2 there was no native way to create a ZIP file with your own SAS program. Starting in SAS 9.2, you can create your own ZIP files using ODS PACKAGE statements. This presentation describes how to create simple ZIP archives, and discusses how to create an archive file with an internal directory structure |
output delivery system, sas enterprise guide | source | 4 |
| 108 | Current Directions in SAS/STAT Software Development | Maura E. Stokes | Recent years brought you SAS/STAT releases in rapid succession, and another release is targeted for 2013. Which new software features will make a difference in your work? What new statistical trends should you know about? This paper describes recent areas of development focus, such as Bayesian analysis, missing data analysis, postfitting inference, quantile modeling, finite mixture models, specialized survival analysis, and structural equation modeling. This paper introduces you to the concepts and illustrates them with practical examples. More Frequent Releases of SAS/STAT Software In previous years, SAS/STAT software was updated only when Base SAS software was released, but SAS/STAT is now released independently of Base SAS along with other SAS analytical products. This means these products can be released when enhancements are ready, and the goal is to update SAS/STAT every 12 to 18 months |
source | 22 | |
| 109 | Dashing out a Quick Dashboard of Graphs in SAS | Alan C. Elliott, Linda Hynan |
In a world overwhelmed with data, a challenge often facing a data analyst confronted with a new data set is to produce concise initial comparisons that provide information about data distributions as well as statistical comparisons on primary factors of interest. This paper illustrates how to combine summary analysis graphs that incorporate statistical results in a matrix/dashboard format on a single, concise page. SAS users familiar with basic SAS programming techniques will be able to produce these dashboards of graphic results | histogram, dataset | source |
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| 110 | Data Entry in SAS Strategy Management A New, Better User (and Manager) Experience | David Shubert | Data entry in SAS Strategy Management has never been an especially pleasant task given the outdated user interface, lack of data validation, and little workflow management. However release 5.4 unveils a complete overhaul of this system. The sleeker HTML5-based appearance provides a more modern Web experience. You are now able to create custom data validation rules and attach a rule to each data value. Discover how form workflow is supported via SAS Workflow Studio and the SAS Workflow Services | data cleaning, error message, data validation rules | source | 20 |
| 111 | Data Fitness: A SAS Macro-based Application for Data Quality of Large Health Administrative Data | Mahmoud Azimaee | This paper introduces a SAS macro-based application package as a solution for creating automated data quality assurance reports for large health administrative data. It includes methods and tools for developing metadata for a SAS data holding, for measuring different data quality indicators using a Data Quality Framework, and for generating automated visual data quality reports. Because quality of data documentation should be considered as a usability and interpretability factor for good quality data, this application uses the same metadata developed for data quality purposes to generate an automated web-based data dictionary as well | data dictionary, sas library, proc step, data cleaning, character variable, date variable, proc freq, trend analysis, outlier, sample, dataset, macro definition, proc contents, lookup | source | 12 |
| 112 | Data Merging and Visualization to Identify Associations Between Environmental Factors and Disease Outbreaks | Azhar Nizam, Neeta Shenvi, Xin Zhang |
This paper describes data merging and visualization techniques for epidemiological and environmental surveillance data. The ultimate goal is to learn about associations between specific environmental factors and disease outbreaks. In such studies, environmental and clinical surveys often occur on different timelines. As such, data merging for the purpose of correlating the two data series can be difficult and subjective. Furthermore, scientists are often interested in exploring chronological lags between the series, making merging more complicated. Visualization of the data series by means of overlaid scatterplots and other multi-dimensional graphics, and exploratory quantification of possible lags and correlations is an important first step in building predictive models. We illustrate data merging with PROC SQL to merge environmental and clinical data with chronological lags |
correlation, sample, proc step, proc sql, create table, scatter plot, predictor variable, do loop, macro variable, merge statement, data merging, macro loop | source | 12 |
| 113 | Data Mining of U.S. Patents: Research Trends of Major Technology Companies | Ken Potter, Robert N. Hatton |
Research initiatives are normally closely held corporate secr ets. Insights to research trends are difficult to extract from public information, but data mining of the U.S. Patent and Trademark Offi ce (USPTO) patent grants provides an opportunity to expose interesting trends and areas of interest as indicated by activity in related patent areas. This paper covers assessing the vast USPTO information repository and the analytic al methodology that extracts patent grant information from multiple formats and produces inte resting insights into research trends for several major technology companies. KEYWORDS Data & Text Mining; Advanced Analytics; Research, Development, Test and Engineering, U.S. Patent and Trademark Office, Technology Trends, SAS, SAS Enterprise Guide, SAS Enterprise Miner and Text Miner, cURL | txt, dataset, data mining, macro definition, raw data, hash table, macro variable, sample, sas enterprise guide | source | 11 |
| 114 | Data Review Information: N-Levels or Cardinality Ratio | Ronald J. Fehd | proc freq, key | source | 6 | |
| 115 | Data Set Compression Using COMPRESS= | Srinivas Busi Reddy, Srikar Rayabaram, Musthan Meeran Mohideen |
Due to an increased awareness about data mining, text mining and big data applications across all domains the value of data has been realized and is resulting in data sets with a large number of variables and increased observation size. Often it takes a lot of time to process these datasets which can have an impact on delivery timelines. When there is limited permanent sto r- age space, storing such large datasets may cause serious pro blems. Best way to handle some of these constraints is by making a large dataset smaller, by reducing the number of observations and/or variables or by reducing the size of the variables, without losing valuable information. In this paper we will see how a SAS data set can be compressed using the COMPRESS= system option and also some techniques to make this option more effective | cpu time, options statement, proc step | source |
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| 116 | Dealing with Duplicates | Christopher J. Bost | Variable values may be repeated across observations. If a variable is an identifier, it is important to determine if values are duplicated. This paper reviews techniques for detecting duplicates with PROC SQL, summarizing duplicates with PROC FREQ, and outputting duplicates with PROC SORT |
proc step, proc freq, proc sql, proc sort | source | 5 |
| 117 | Deciphering Emoticons for Text Analytics: A Macro-Based Approach | Chad Atkinson | Emoticons, initialisms, and acronyms present a challenge to text analytics because they convey important information that may evade routine processing. The difference between: "this was a great class :)" and "ZOMG that was the best class ever :-7" may be significant. The approach developed herein converts the emoticon, acronym, or initialism to text that can be parsed by SAS Enterprise Miner or SAS Sentiment Analysis Studio and then analyzed. The result is a SAS macro, emoticon, which is designed to process text prior to text or sentiment analysis | unstructured data, tranwrd function, macro definition, sample, excel, data mining | source | 15 |
| 118 | Demand Forecasting Using a Growth Model and Negative Binomial Regression Framework | Cally Ong Yeru, Michelle Cheong, Murphy Choy |
In this paper, we look at demand forecasting by usi ng a growth model and negative binomial regression framework. Using cumulative sales, we model the sales data for different wristwatch brands and relate it to their sales and growth characteristics. We apply clustering to determine t he distinctive characteristics of each individual c luster. Four different growth models are applied to the clusters to find the most suitable growth model to be used. After determining the appropriate growth model to be appl ied, we then forecast the sales by applying the mod el to new products being launched in the market and continue to monitor the model further | logistic distribution, poisson distribution, binomial distribution, hierarchical clustering, negative binomial regression, regression analysis, negative binomial distribution, poisson regression | source | 17 |
| 119 | Destination Known: Programmatically Controlling Your Output in SAS Enterprise Guide | Aaron Hill | In a SAS Enterprise Guide project with multiple reports and graphics, you can organize output by selectively sending content to different ODS destinations embedded within the project. For example, within a single program you could embed tables in RTF format, graphics in PDF, and other output in Text. The SAS syntax is simple and gives you programmatic control over all output and destinations. The result: a well-organized project with all results in their preferred format |
sas enterprise guide, sas output, output delivery system, proc step, proc print | source | 10 |
| 120 | Detecting Cross-Channel Fraud Using SAS | Krutharth Kumar Peravalli Venkata Naga, Srikar Rayabaram |
In a world where criminals are getting effective in their ability to gain information about a customer of a particular bank, cross channel monitoring and assessment has become very important. As each day passes by, criminals are also getting bolder in terms of engaging beyond a single channel to set in motion the movement of money. In these scenarios, a cross channel review of user activity is very much essential to detect or prevent fraud. In this paper we will analyze data across various channels such as cash, regular wires and FCE wires of a south east bank to detect fraudulent activity along with Money Service Businesses (MSB). Also, we will be creating a predictive model which can be used to predict such activity and discuss how effective the model would have been to detect fraudulent activity in the past | insurance risk score, flat file, proc hpsplit, response variable, dataset, proc means, sample | source | 9 |
| 121 | Developer Reveals: Extended Data Set Attributes | Diane Olson | Have you ever wanted to save non-data information with your data set? Now, with SAS 9.4, you can. Extended attributes allow you to store information related to a data set or to a particular variable in a data set. Do you want to store a description of a variable or the formula used to produce the variable value? Do you need to save a URL that specifies information about your data set? Do you want to store the SAS code that created a particular data set? This presentation by the developer shows you how to do all of that and more with extended attributes | proc step, proc contents, set statement, character variable, proc print, proc sql | source | 9 |
| 122 | Developing Your SDTM Programming Toolkit | David A. Scocca | Data standards such as the Study Data Tabulation Model (SDTM) make programmers' lives simpler but more repetitive. The similarity across studies of SDTM domain structures and relationships presents opportunities for code standardization and re-use. This paper discusses the development and use of tools to simplify the process of creat ing SDTM data sets, with examples of common tasks and the code to implement those tasks . It also discusses the usefulness of a metadata system and presents a general specification for an interface for accessing metadata . Examples will include mapping study visits, parsing dates, and standardizing test codes | clinical data, sdtm domain, macro variable, laboratory data, clinical trial, study day, attrib statement, proc sql, create table, date variable, proc step, adverse events, cdisc | source | 10 |
| 123 | DICHOTOMIZED_D: A SAS Macro for Computing Effect Sizes for Artificially Dichotomized Variables | Aarti P. Bellara, Harold Holmes, Isaac Y. Li, Jeanine L. Romano, Jeffrey D. Kromrey, Patrice Rasmussen, Patricia Rodríguez de Gil, Yi-Hsin Chen |
Measures of effect size are recommended to communicate information on the strength of relationships between variables, providing information to supplement the reject / fail-to-reject decision obtained in statistical hypothesis testing. With artificially dichotomized response variables, seven methods have been proposed to estimate the standardized mean difference effect size that would have been realized before dichotomiz ation. This paper provides a SAS macro for computing these seven effect size estimates b y utilizing data from PROC FREQ output data sets. The paper provides the macro programming language, as well as results from an executed example of the macro. Keywords: EFFECT SIZES, DICHOTOMIZED VARIABLES, BASE SAS, SAS/STAT |
effect size, sample, sample size, interval variable, response variable, odds ratio, proc freq, proc step, dataset, proc calis, macro definition | source | 12 |
| 124 | Do I Need a Migration Guide or an Upgrade Coach? | Donna Bennett, Gerry Nelson, Mark L. Schneider |
Don't bring a hammer when you need a paintbrush! There are many kinds of changes you can make to your SAS deployment. Sometimes, SAS migration tools might provide the best path for making changes to your system. Other times, your changes might need other deployment and management tools. Whether you are an administrator managing the changes or an IT administrator overseeing SAS software, this paper will help you choose the right tools to plan and manage SAS software changes. As an added bonus, the paper includes a glossary of common terms and concepts that often require collaboration between IT and SAS management | source | 24 | |
| 125 | Do Not Let a Bad Date Ruin Your Day | Lucheng Shao | As we have to step out of fairy tale land and into reality when we grow up, we can't always expect the input dates to be good. This paper shows you what SAS does when it runs into input dates that are normally good but have now gone bad, and how those problems can be addressed by code. It is intended for readers who are familiar with SAS BASE but not with bad dates | date variable, sample, cpu time, response variable, missing value, numeric variable, proc step | source | 9 |
| 126 | Do People Still Miss Steve Jobs As the CEO of Apple Inc.? A Text Mining Approach: Comparing SAS and R | Anurag G. Srivastava, Pranav Karnavat |
Poster and Video PresentationsSAS Global Forum 2013 2 | proc freq, data mining | source |
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| 127 | Do SAS users read books? Using SAS graphics to enhance survey research | Barbara B. Okerson | In survey research, graphics play two important but distinctly different roles. Visualization graphics enable analysts to view respondent segments, trends and outliers that may not be readily obvious from a simple examination of the data. Presentation graphics are designed to quickly illustrate key points or conclusions to a defined audience from the analysis of the survey responses. SAS provides the tools for both these graphics roles through SAS/GRAPH and ODS graphics procedures. Using a survey of the Virginia SAS Users Group (VASUG) as the data source, this paper answers the above question and more while illustrating several SAS techniques for survey response visualization and presentation. The techniques presented here include correspondence analysis, spatial analysis, heat maps and others. Results included in this paper were created with version 9.2 of SAS on a Windows 64-bit server platform and use Base SAS, SAS/STAT and SAS/GRAPH |
heatmap, proc step, proc corresp, proc sgplot, outlier, proc princomp | source | 10 |
| 128 | Doctoring Your Clinical Trial with Adaptive Randomization: SAS Macros to Perform Adaptive Randomization | Jenna Colavincenzo | Adaptive randomization schemes have become increasingly common in beginning stages of clinical trials and in small clinical trials. This paper introduces two kinds of adaptive randomization schemes (treatment adaptive randomization and covariate adaptive randomization) and discusses the benefits and limitations of each. In addition, this paper demonstrates how to use SAS macros to perform these adaptive randomization schemes in a clinical setting, and how these macros can be modified to fit your randomization needs | randomization, sample size, sample, adaptive design, clinical trial, group by, treatment effect, proc freq | source | 9 |
| 129 | Don?t Let the Number of Columns Hold You Back! | Douglas B. Liming | Many databases have a column limit of approximately 2,000 columns. Several SAS PROCs, such as PROC NEURAL and PROC TRANSPOSE, produce output that easily exceeds 2,000 columns. This paper presents a technique to code around this business problem when using databases on the back end. Maximize your columns using SAS to talk to the databases via multiple tables, pull them together, and then split them back out |
dataset, proc step | source | 3 |
| 130 | Double-Clicking a SAS File: What Happens Next? | Donna Bennett, Sandy Gibbs |
When you double-click a SAS file on your desktop or in Windows Explorer, which program launches-SAS or SAS Enterprise Guide? Does the SAS program open in a preferred application? Both Microsoft and SAS have introduced changes in how you change the default SAS file type associations, starting with Microsoft Windows Vista and with SAS 9.2. If you have installed SAS 9.2 or SAS 9.3 and are using earlier approaches to change the default file associations (for example, through Windows Explorer), you might encounter problems. This topic has generated a lively discussion among the SAS Deployment Support Community! The recommended method for changing file type associations is described in this paper. Also discussed are ways to troubleshoot problems that might surface during this process | sas enterprise guide, note | source | 14 |
| 131 | Easy Button: A Process for Generating Standardized Safety- and Non-Safety- Related Clinical Trial Reports | Jiannan Hu, Mominul Islam, Sanjiv Ramalingam, Xiangchen (Bob) Cui, Yanwei Han |
Developing a process for standardized reports in terms of Tables, Figures and Listings (TFLs) generations for a clinical study report by developing SAS macro-based either reporting tool or template SAS programs is an ongoing focus of the health care and life science industry. We have developed SAS macros and template SAS programs (macro calls) based on our Biometrics Standard TFLs shells for safety analysis like Adverse Events (AE), LABs, ECG, Vitals and other non-safety analysis, for example, Baseline Characteristics, Disposition, Concomitant Medication, Drug Exposure, Compliance and Figures for Kaplan-Meier plot. Our newly developed process includes development of reporting macros by utilizing the existing department macros to generate these standard reports. The macros have been developed assuming the CDISC ADaM ana lysis data set standards which enable us to minimize the number of macro parameters for efficient use of the macros by the user | clinical trial, macro call, cdisc | source | 14 |
| 132 | Efficient Extraction of JSON Information in SAS Using the SCANOVER Function | Kyong Jin Shim, Murphy Choy |
JSON, otherwise known as JavaScript Object Notation, is a popular data interchange format which provides a human readable format. It is language independent and can be read easily in a variety of computer languages. With the rise of Twitter and other unstructured data, there has been a move to inco rporate such data as a way of disseminating information. Twitter currently provides a simple API for users to extract tweets using JSON format. While SAS does not currently have a direct way of reading JSON, the SCANOVER function in SAS DATA STEP provides user with a simple and effective approach to getting JSON information into SAS datasets. In this paper, we will illustrate two examples using this technique |
data step, json | source | 6 |
| 133 | Employee Wellness Programming Using SAS Enterprise Guide | Sandra Brooks, Tina Hembree, Yehia H. Khalil |
More businesses are utilizing employee wellness programs to improve the health of their employees (improve productivity levels, reduce absenteeism and reduce disability claims) while reducing health care costs. The success of any wellness program depends o n three main rudiments: 1) identifying factors that driving up the healthcare cost within the organization such as: s moking, diabetes, back pain, or other chronic conditions . 2) Engagement level of employees in the wellness programs 3) Barriers to achieving expected engagement level. The real challenge for any wellness program is to incorporate data from different e.g. Health Risk Assessment (HRA), demographics, medical claims data, and qualitative input to build a comprehensive wellness program w hich incorporates the needs of the business population | sas enterprise guide, data science, insurance claims | source | 7 |
| 134 | Enhance Your High Availability Story by Clustering Your SAS Metadata Server in SAS 9.4 | Amy Peters, Bryan Wolfe |
It can be challenging to use clustered file systems, backups, and system tools to ensure that the SAS Metadata Server is always available. In SAS 9.4, you can create and manage a clustered metadata deployment using SAS tools to remove single-point-of-failure concerns for the SAS Metadata Server. The clustered SAS Metadata Server keeps its data in sync, balances its load, and continues to handle requests if a node fails, all while presenting a single face to the outside world. Once it is set up, you do not need to treat a clustered SAS Metadata Server any differently than you do a single, unclustered server | sas management console, sas enterprise guide | source | 9 |
| 135 | Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs | Lora D. Delwiche, Susan J. Slaughter |
This paper is based on the belief that debugging your programs is not only necessary, but also a good way to gain insight into how SAS works. Once you understand why you got an error, a warning, or a note, you'll be better able to avoid problems in the future. In other words, people who are good debuggers are good programmers. This paper covers common problems including missing semicolons and character-to-numeric conversions, and the tricky problem of a DATA STEP that runs without suspicious messages but, nonetheless, produces the wrong results. For each problem, the message is deciphered, possible causes are listed, and how to fix the problem is explained |
sas log, missing value, proc print, put statement, proc step, input statement, error message, dataset, character variable, infile statement, numeric variable, firstobs, options statement, excel, sas output | source | 15 |
| 136 | Escape from Big Data Restrictions by Leveraging Advanced OLAP Cube Techniques | Stephen M. Overton | In today's fast-growing field of business analytics, there are many tools and methods for summarizing and analyzing big data. This paper focuses specifically on OLAP technology and features natively available in OLAP cubes that enable organizations to deploy robust business intelligence reporting when high volumes of data exist. This paper discusses how to use these features to enhance basic OLAP cubes by utilizing member properties, defining dynamic measures and dimensions using the MDX language, and improving performance for high data volumes | big data, raw data, sample, star schema, proc step, bank transaction, sas enterprise guide | source | 14 |
| 137 | Essentials of the Program Data Vector (PDV): Directing the Aim to Understanding the DATA Step | Arthur X. Li | Beginning programmers often focus on learning syntax without understanding how SAS processes data during the compilation and execution phases. SAS creates a new data set, one observation at a time, from the program data vector (PDV). Understanding how and why each automatic or user-defined variable is initialized and retained in the PDV is essential for writing an accurate program. Among these variables, some variables deserve special attention, including variables that are created in the DATA STEP, by using the RETAIN or the SUM statement, and via BY-group processing (FIRST.VARIABLE and LAST.VARIABLE). In this paper, you are exposed to what happens in the PDV and how these variables are retained from various applications |
input statement, automatic variable, dataset, output, by group processing, retain, missing value, sas log, compilation phase, execution phase, _n_, put statement, infile statement, subsetting if, error message, if-then-else, longitudinal data, raw data | source | 12 |
| 138 | Estimates of Personal Revenue from Credit and Sociodemographic Information Combining Decision Trees and Artificial Neural Networks (ANN) | Deybis Florez Hormiga | A typical problem in the information required for the different processes within a bank, is to know the customer revenue, this information is not easy to gather and update because it is highly sensitive, and the accuracy of this information in large amounts of customers is not the best. Therefore, it is very important and of high impact in the processes of the bank, to find a method to estimate the revenue of customers for validation, segmentation, profiling, business strategies, risk mitigation, regulatory compliance, or simply as information. Due to the amount of information and the high volatility of the income reported by different clients, SEMMA methodology was used with Enterprise Miner. Starting from a fine segmentation using decision trees and then using Artificial Neural Networks in each of these segments, it generates a different model for each segment | proc hpsplit, data mining | source | 4 |
| 139 | Estimating Censored Price Elasticities Using SAS/ETS: Frequentist and Bayesian Approaches | Christian Macaro, James Lemieux, Jan Chvosta, Kenneth Sanford |
The number of rooms rented by a hotel, spending by "loyalty card" customers, automobile purchases by households-these are just a few examples of variables that can best be described as "limited" variables. When limited (censored or truncated) variables are chosen as dependent variables, certain necessary assumptions of linear regression are violated. This paper discusses the use of SAS/ETS tools to analyze data in which the dependent variable is limited. It presents several examples that use the classical approach and the Bayesian approach that was recently added to the QLIM procedure, emphasizing the advantages and disadvantages that each approach provides |
source | 14 | |
| 140 | Estimating Harrell's Optimism on Predictive Indices Using Bootstrap Samples | Irena Stijacic Cenzer, W. John Boscardin, Katharine Kirby, Yinghui Miao |
In aging research, it is important to develop and validate accurate prognostic models whose predictive accuracy will not degrade when applied in external data sources. While the most common method of validation is split sample, alternative methods such as cross-validation and bootstrapping have some significant advantages. The macro that we present calculates Harrell's optimism for logistic and Cox regression models based on either the c-statistic (for logistic) or Harrell's c (for Cox). It allows for both stepwise and best subset variable selection methods (and can handle class variables for both methods), and uses both traditional and .632 bootstrapping methods. In addition, we present and discuss the advantages of using Best Subsets regression for model selection instead of stepwise procedures. The uses of Best Subsets regression and our Harrell_Optimism macro are demonstrated using data on post-hospitalization mortality | bootstrap, best subsets, regression analysis, sample, proc step, proc phreg, feature selection, logistic regression, binary variable, proc score, nominal variable, macro call, logistic distribution, data cleaning, class variable, stepwise selection | source | 12 |
| 141 | Estimating Patient Adherence to Medication with Electronic Health Records Data and Pharmacy Claims Combined | Beinan Zhao, Eric C. Wong, Latha Palaniappan |
Estimating patient adherence to medication is critical to comparative effectiveness, patient centered outcomes research, and epidemiological studies. Organizations may have varying availability of medication data. Some may have one or a subset of three possible data sources: physician prescriptions from Electronic Health Records (EHR), fill data from pharmacies or claims data from insurers. Using data from a comprehensive EHR system (EpicCare) in an ambulatory care practice for 11 years with over 1 million patients, we evaluated the prevailing adherence metrics (e.g. medication possession ratio, proportion days covered). However, these metrics cannot be estimated when a patient does not fill a medication order at all (primary non-adherent) or only fills it once (early stop). With just a little more effort, we can incorporate additional clinical information from the EHRs to obtain refined estimates of adherence | group by, proc sql | source | 7 |
| 142 | Evaluating System-Wide Process Improvement in a Health-Care System: Data Through Analysis | Dorothy Hung, Eric C. Wong, Lubna Qureshi, Pragati Kenkare |
Disruptive system changes are required for sustaining high-quality and affordable health-care delivery systems. Successful, transformative health-care system changes are few and even fewer have been rigorously evaluated. Electronic health records and changes in health IT provide an opportunity to leverage an explosion of data in measuring the impact of process improvement initiatives. This paper provides an example of assessing the impact of a system-wide change in a large, multi-specialty health-care system serving two million patients with a 13-year history of using electronic health records. Lessons from ETL all the way to statistical analysis are detailed including relevant SAS procedures | dimension table, time series analysis, regression analysis, proc step | source | 6 |
| 143 | Examining Mediator and Indirect Effects of Loneliness in Social Support on Social Well-Being Using the Baron and Kenny Method and a Bootstrapping Method | Abbas S. Tavakoli, Sue P. Heiney |
This study presentation examines mediator effect and indirect effect of loneliness in social support on social well being by using two methods: Baron & Kenny, and Bootstrapping. A cross-sectional data were used here from the longitudinal study randomized trial design in which 185 participants were assigned to the therapeutic group (n=93) who received by teleconference with participants interacting in real time with each other and control group (n=92) who received usual psychosocial care (any support used by the patient in the course of cancer treatment. Baron and Kenny (1986)steps and Hayes (2004) we reused to examine mediation effect. Results of Baron indicated that the relationship between social support and social well being was significant (c =.634 (total effect) (p=.0001)) and that there was significant relationship between mediator and predictor variable (α =-2.11 (p=.0001)) | regression analysis, predictor variable, sample, response variable, correlation, proc means, bootstrap | source | 12 |
| 144 | Exploring Health Trends and Risk Behavior Analysis in American Youth Using PROC SURVEYFREQ and PROC SURVEYLOGISTIC | Deanna Naomi Schreiber-Gregory | The current study looks at recent health trends and behavior analyses of youth in America. Data used in this analysis was provided by the Center for Disease Control and Prevention and gathered using the Youth Risk Behavior Surveillance System (YRBSS). This study outlines demographic differences in risk behaviors, health issues, and reported mental states. Interactions between risk behaviors and reported mental states were also analyzed. Visual representations of frequency data for the national results are also provided and discussed. A final regression model including the most significant contributing factors to suicidal ideation is provided and discussed. Results included reporting differences between the years of 1991 and 2011. All results are discussed in relation to current youth health trend issues. Data was analyzed using SAS 9.3 | odds ratio, likelihood function, correlation, proc freq, proc step, regression analysis | source | 11 |
| 145 | Exploring the PROC SQL _METHOD Option | Charles E. Shipp, Kirk Paul Lafler |
The SQL Procedure contains powerful options for users to take advantage of. This presentation and poster explores the fully supported _METHOD option as an applications development and tuning tool, and learn how to use this powerful option to understand and control how a query processes |
proc sql, proc step, sas log, where statement, error message, options statement, data merging | source |
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| 146 | Exploring Time Series Data Properties in SAS | Aude Pujula, David Maradiaga, Hector Zapata |
Box and Jenkins popularized graphical methods for studying time series properties of time series data. Dickey-and Fuller did the same for unit-root tests. Both methods seek to understand the non-stationary properties of data and SAS is a popular software used by applied researchers. The purpose of this paper is to provide a series of steps using SAS MACRO Language, PROC SGPANEL, ARIMA, AUTOREG, and %DFTEST to diagnose non-stationary properties of data. A comparison among three competing SAS procedures is presented with SAS capabilities highlighted using simulated time series |
time series analysis, proc step, proc arima, regression analysis, time series data, stationarity, unit root, proc autoreg, monte carlo, sas base, null hypothesis, proc reg, proc sgpanel, proc sgplot, sas output, homoscedasticity | source | 12 |
| 147 | Extended SAS GIFANIM Device Usage on Table Reporting and Template-Based Graphics | Azhar Nizam, Neeta Shenvi, Xin Zhang |
Including dynamic, rather than static, graphs and tables in presentations can make them more effective, interactive and audience-engaging. The SAS GIFANIM device enables analysts to create GIF file-based slide shows for web and PowerPoint presentations, but it only supports device-based graphics and does not support SG procedure graphics. The GIFANIM device also does not directly allow for animation of summary tables. In this paper, several ways of animating stand-alone summary tables, SG procedure graphics, and graphics with embedded tables are explored, using combinations of the SAS DATA STEP Graphics Interface (DSGI), printer-based methods, and Annotate data sets. The advantages and disadvantages of each method are evaluated |
macro definition, proc step, proc sql | source | 9 |
| 148 | Extending SAS Reports to Your iPhone | Koketso Moeng | You have jumped through all of the hoops of creating the perfect dashboards for executives, marketing, human resources, finance, and the project office teams, but they hardly ever get used because, frankly, your users dont have enough time in the day to go through the reports. This is even more true if they have to be tethered to the servers in the office to do so. Luckily, a solution that suits the users with an iPad or iPhone is available. Introducing Roambi-a mobile business intelligence (BI) platform that runs on Apples iOS platform and can be easily integrated into your existing SAS Enterprise BI platform | business intelligence, sas management console | source | 10 |
| 149 | Extending the Power of Base SAS with Microsoft Excel | Shilpa Khambhati | The SAS Macro Language is an invaluable SAS tool that can be used for iterative SAS data processing, eliminating redundancy in SAS code. Using the SAS Macro Language with Microsoft EXCEL makes programming tasks even easier. This paper describes how to use the SAS Macro Language and Microsoft EXCEL to automatically generate customized reports. The proposed method uses EXCEL macros to drive SAS macros without having to open SAS programs and manually upgrade parameters for each site's data when the data become available. The process eliminates manually editing SAS programs and improves data quality by reducing programming error and program maintenance time |
excel, macro definition, macro variable, macro facility, data cleaning, sas base | source |
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| 150 | Extension Node to the Rescue of the Curse of Dimensionality via Weight of Evidence (WOE) Recoding | Andrew Cathie, Goutam Chakraborty, Satish Garla |
Predictive models in business data mining applications often involve very large data sets with numerous variables many of which are categorical with large number of levels. Predictive models with categorical variables with large number of levels often suffer from the curse of dimensionality. Enhanced Weight of Evidence (WOE) methods can be used to effectively incorporate high dimensional categorical inputs into a data mining model. Weight of evidence technique converts a nominal input into an interval input by using a function of the distribution of a target variable for each level in the nominal input variable. While WOE recoding can be done via SAS macros and SAS programming, there is no easy way for business users with little programming background to use this technique. However, SAS Enterprise Miner has a facility to create extension nodes which work in the same way as a usual node which can be easily configured via the properties panel using point-and-click interface | data mining, response variable, training set, macro variable, histogram, sas programming, nominal variable, macro definition, %if-%then-%else | source | 9 |
| 151 | Extraction, Transformation, and Loading (ETL) for Outcome Measures of Workers? Compensation Benefits | Mike Maier | Base SAS was used to create a data sub-system for measuring outcomes, added to a data system (coded in SAS) of benefit costs and employment. One claim per injured worker per fiscal year is extracted as a study or control record, using business-rule code. Disability benefits and employment data are transformed to time-series records for claims, which are transformed to time-series statistics by fiscal year. Programs are run remotely on a UNIX data warehouse, and SAS data sets and metadata are loaded to the warehouse and downloaded to a LAN. Quarterly generations are kept for analysis of claim development | workers compensation, date variable, proc step, data validation rules, proc sql, sas base, sysdate9, sample, insurance claims, claims development, flat file, data cleaning, proc sort, if-then-else | source | 10 |
| 152 | Fast Dashboards Anywhere with SAS Visual Analytics | Rick Styll | Dashboards can be the best starting point to provide a high-level view of the most relevant information to monitor, analyze, and collaborate around business performance. A growing number of organizations are creating dashboards to improve fact-based decision making, but if the dashboards fail to return results quickly or are difficult to understand, user adoption will soon wane. SAS Visual Analytics includes an authoring interface called Designer that lets you build responsive, well-formatted, and effective dashboards that enable users to: Place, size, and style objects precisely. Interact between objects. Drill and expand. Collaborate. * Share results on the Web and mobile devices. SAS Visual Analytics provides fast, effective dashboards anywhere, regardless of how big your data might be | line chart, bar chart, time series analysis, series plot, big data, waterfall chart, order by | source | 27 |
| 153 | FCMP - Why? | Lisa Eckler | PROC FCMP allows a SAS programmer the opportunity to create user -defined functions in SAS. Prior to the availability of FCMP in SAS 9, SAS macros or linked routines were often used to achieve a similar - but less elegant - effect. This paper examines the advantages of FCMP over the earlier alternatives and why it is therefore so valuable to the programmer |
proc fcmp, proc step, sas function | source | 5 |
| 154 | Feature Extraction and Rating of a Smartphone Photosharing Application Using SAS Sentiment Analysis Studio | Anil Kumar Pantangi, Goutam Chakraborty, Sahithi Ravuri, Siddhartha Reddy Mandati |
Smartphone users often have to read many reviews available online to know about a particular feature of an application. Online reviews often provide a numeric overall rating using the Likert or semantic scales but these do not fully reveal sentiments of customers about a product's feature s. For this project we have considered the website Google play, which is a dedicated portal for all An droid paid/free applications. Google Play captures both structured and unstructured reviews generic to each application by Helpfulness, Rating and Newest . This leaves the user with no clue about a particular feature and its rating. In this paper we illustrate feature extraction and feature ranking of the Photosharing application using SAS Sentiment Studio. The reviews are manually categorised as Positive and Non -Postive by multiple experts. Sentiment Studio is used to predict a new review as either Positive or Non-positive | normalization, relative frequency, proc freq, type ii error, data cleaning | source |
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| 155 | Feature-Based Sentiment Analysis on Android App Reviews Using SAS Text Miner and SAS Sentiment Analysis Studio | Goutam Chakraborty, Jiawen Liu, Mantosh Kumar Sarkar |
Sentiment analysis is a popular technique for summarizing and analyzing consumers' textual reviews about products and services. There are two major approaches for performing sentiment analysis; statistical model based approaches and Natural Language Processing (NLP) based approaches to create rules. In this study, we first apply text mining to summarize users' reviews of Android Apps and extract features of the apps mentioned in the reviews. We then use NLP approach for writing rules. We use reviews of two recent apps; a widget app from Brain& Puzzle category and a game app from Personalization category. We extracted six hundred textual reviews for each app from Google Play Android App Store. SAS Enterprise MinerTM 7.1 is used for summarizing reviews and pulling out features, and SAS Sentiment Analysis Studio 12.1 is used for performing sentiment analysis | proc freq, customer experience | source |
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| 156 | Finally, a Tool for Business Users! A Step-By-Step Practical Approach to Pharma Sales Reporting Using SAS Enterprise Guide 4.3 | Airaha Chelvakkanthan Manickam, Ramya Purushothaman |
SAS Enterprise Guide can be looked at as the Integrated Development Environment (IDE) for SAS which can be used by business users even with minimal SAS programming experience to derive meaningful reports. This paper explains a step-by-step representation of a real-time scenario in the Pharmaceutical industry addressed through SAS Enterprise Guide. The example illustrated here covers importing the raw sales data purchased from an external vendor, perform business transformations, summarization and creating visual representation like charts, reports for Executive dashboard presentation, scheduling and e-mailing the reports to the end users without manual intervention. All these needs are easily accomplished using a series of simple clicks in SAS Enterprise Guide | sas enterprise guide, proc freq, distribution channel, proc means, inner join | source | 18 |
| 157 | Finding the Gold in Your Data: An Overview of Data Mining | David A. Dickey | logistic regression, logistic distribution, regression analysis, data mining, neural network, proc hpsplit, roc curve, validation set, data cleaning, training set, likelihood function, anova, odds ratio, response variable, binomial distribution | source | 16 | |
| 158 | Floating on Cloud 9.3: Leveraging the Cloud with SAS and Google Drive | William G. Roehl | Our organization has been utilizing Google Drive (previously: Google Docs) to keep project documentation centrally located for ease of access by any user on any platform. Up until this point, SAS developers had to manually import and/or export datasets to/from flat files or Microsoft EXCEL in order to update data stored in the cloud. This paper provides a powerful macro toolkit which facilitates direct access to Google Spreadsheets through its published API allowing uploading, downloading, deletion and live data manipulation of cloud-based data. This provides an opportunity for a significant reduction in the amount of manual work and time required for SAS developers to perform these basic functions. Code was developed with SAS 9.3 and HTMLTidy (25MAR2009) running under Microsoft Windows 7 |
macro call, proc http, sas programmer, excel, proc step, dictionary tables | source | 5 |
| 159 | For All the Hats You Wear: SAS Enterprise Guide Has Got You Covered | Chris Hemedinger | Are you new to SAS and trying to figure out where to begin? Are you a SAS programmer already comfortable with code but unsure about new tools? Are you a statistician seeking to apply your techniques in a new way? Are you a data manager just trying to get your data in shape? Perhaps you are a Jack (or Jill) of all trades trying to manage work in the simplest way possible. Regardless of your background, SAS Enterprise Guide is full of features and techniques to help you achieve your objective. In this paper, we show how you can turn SAS Enterprise Guide into your tool to get work done without conforming to an entirely new way of working just to become productive | sas enterprise guide, data science, sas programmer, logistic distribution, logistic regression, proc freq, regression analysis | source | 8 |
| 160 | Forecasting Net Job Creation Using SAS | Casey Sperrazza | Our organization has been utilizing Google Drive (previously Google Docs) to keep project documentation centrally located for ease of access by any user on any platform. Up to this point, SAS developers had to manually import or export data sets to or from flat files or Microsoft EXCEL in order to update data stored in the cloud. This paper provides a powerful macro toolkit that facilitates direct access to Google Spreadsheets through its published API, allowing uploading, downloading, deletion, and live data manipulation of cloud-based data. This provides an opportunity for a significant reduction in the amount of manual work and time required for SAS developers to perform these basic functions. Code was developed with SAS 9.3 and HTMLTidy (25MAR2009) running under Microsoft Windows 7. No paper was submitted for publication |
flat file, excel | source | 367 |
| 161 | Free Expressions and Other GTL Tips | Prashant Hebbar, Sanjay N. Matange |
The Graph Template Language (GTL) provides many powerful features for creating versatile graphs. The Statistical Graphics Engine in GTL provides novel ways of using expressions to simply the task of data preparation. This presentation covers some new ways of using DATA STEP functions for creating grouped plots based on conditions and to select a subset of the observations. It also illustrates usage of PROC FCMP functions in GTL expressions. Novel uses of non-breaking space for creating chunked graphs and graphs with indented text, as well as workarounds for passing Unicode characters in data columns are discussed. Learn how to express yourself with ease, graphically! |
scatter plot, graph template language, sas base, macro variable, user-defined format, proc step, adverse events, proc fcmp | source | 14 |
| 162 | FREQ Out - Exploring Your Data the Old School Way | Stephanie R. Thompson | The tried-and-true FREQ procedure just doesn't get the attention it deserves. But, as they say, it is an oldie but a goodie. Sometimes you just need a quick look at your data and a few simple statistics. PROC FREQ is a great way to get an overview of your data with a limited amount of code. This hands-on workshop explores everything from the basic framework of the procedure to how to customize the output. It also presents an overview of some of the options that are available |
proc freq, proc step, output, sas base | source | 13 |
| 163 | From Factbook to Dashboard in T Minus No Time and Counting! | Alicia Betsinger, Annette Royal |
The University of Texas (UT) System has been publishing detailed data on institutional performance for years using static PDFs and Microsoft EXCEL documents. With requests for more data increasing, this approach was unsustainable. The Office of Strategic Initiatives (OSI) was spending too much time collecting and processing data for The Chancellor, Board of Regents, and media - there was no time for in-depth research or analysis. Instead of us using the data to help support better management, the data was managing us. What grew from an internal office need morphed into a larger UT System need for a Business Intelligence (BI) System which would support the Chancellor's Framework by providing an accessible, customizable tool for monitoring institutional performance and progress towards transparency and accountability goals |
business intelligence, sas consultant, data warehouse, sas base, phase i, excel, sas enterprise guide | source | 9 |
| 164 | From SDTM to ADaM | Bob Lan, Regan Li, Sai Ma, Suwen Li |
Clinical Data Interchange standards Consortium (CDISC) Study Data Tabulation Model ( SDTM) and Analysis Data Model ( ADaM) datasets are designed with highly desirable data and dataset standards for regulatory submission datasets, as referenced in United States (US) Food and Drug Administration ( FDA) guidance. S ponsors are increasingly submitting both SDTM and ADaM datasets to regulatory agencies. Whereas SDTM datasets source raw data, ADaM datasets are derived from SDTM datasets and may contain both raw and derived data . The distinctive structure to SDTM datasets brings forth some problems when deriving ADaM data. This paper describe s the problems encountered when we derive ADaM data and illustrate how to resolve them using examples | adam dataset, sdtm domain, sas log, missing value, cdisc | source |
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| 165 | Fueling the Future of an Energy Company | Stijn Vanderlooy | EDF Luminus is a producer and supplier of energy in Belgium. The company is active in several markets for trading commodities including electricity, natural gas, and oil-related products. The market data modeling team is responsible for all data related to these markets. Besides a daily collection and verification of published prices, the team performs a diverse set of data transformations and manipulations in order to extract valuable information from the markets. Examples are numerous and include volatility estimation and price forecasts. In this paper we present a successful application of an in-house developed SAS tool that is used by the market data modeling team to support its core tasks. The tool runs autonomously three times a day | best estimate liability, data standard | source | 11 |
| 166 | Gee! No, GTL! Visualizing Data With The SAS Graph Template Language | Ted Conway | When you need to produce a grid of related graphs with minimum coding, PROC SGPANEL is hard to beat. But eventually you'll run into a situation that demands more precise control over the output. Perhaps there are unusual scaling/formatting requirements. Or information needs to be presented in a specific order. Or things need to be clarified via annotations or other markup. That's where the Graph Template Language (GTL) can help. In this paper, we'll see how GTL can be used to create a customized grid of time series plots from segments and measures found in the TOTARRESTS sample data set. This may be of interest to all skill levels. It requires Base SAS, SAS GTL, and the SAS Macro Facility on UNIX or the PC |
graph template language, proc step, sample | source |
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| 167 | GEN_ETA2: A SAS Macro for Computing the Generalized Eta-Squared Effect Size Associated with Analysis of Variance Models | Anh P. Kellermann, Jeanine L. Romano, Jeffrey D. Kromrey, Patrice Rasmussen, Patricia Rodríguez de Gil, Thanh Pham, Yi-Hsin Chen |
Measures of effect size are recommended to communicate information on the strength of relationships between variables. Such information supplements the reject / fail -to-reject decision obtained in st atistical hypothesis testing. The choice of an effect size for ANOVA models can be confusing because indices may differ depending on the research design as well as the magnitude of the effect. Olejnik and Algina (2003) proposed the generalized eta - squared effect size which is comparable across a wide variety of research designs. T his paper provides a SAS macro for computing the generalized eta -squared effect size associated with analysis of variance models b y utilizing data from PROC GLM ODS tables . The paper provides the macro programming language, as well as results from an executed example of the macro. Keywords: GENERALIZED ETA-SQUARED, EFFECT SIZES, ANOVA, BASE SAS, SAS/STAT |
effect size, anova, main effect, proc glm, proc step, sample, predictor variable, proc genmod, response variable, dataset, data step, proc anova, macro definition | source |
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| 168 | Getting an Overview of SAS Data in Three Steps | Goutam Chakraborty, Miriam McGaugh, Shirmeen Virji, Yu Fu |
For SAS programmers, one of the most important steps before manipulating the dataset for further analysis is to get an overview of it. In order to get an idea of the dataset, normally three areas are looked into: variable names, statistical description, and relationship of one dataset with other datasets within a library. The macro that we have generated writes out the names of all the variables present in a file of a particular library, gives descriptive statistics of all the variables that are classified as numeric, and draws a diagram to show the relationships among the datasets. All three steps are performed by running just one code | macro variable, proc step, numeric variable, put statement, ods output, dataset, output delivery system, proc means, %if-%then-%else | source |
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| 169 | Getting Started with SAS Visual Analytics Administration and Deployment | Gary Mehler, Meera Venkataramani |
Presentation | high-performance sas, big data, sas visual analytics, sas management console, dataset, access control, proc append | source | 49 |
| 170 | Getting Started with the SAS/IML Language | Rick Wicklin | Do you need a statistic that is not computed by any SAS procedure? Reach for the SAS/IML language! Many statistics are naturally expressed in terms of matrices and vectors. For these, you need a matrix language. This paper introduces the SAS/IML language to SAS programmers who are familiar with elementary linear algebra. The focus is on statements that create and manipulate matrices, read and write data sets, and control the program flow. The paper demonstrates how to write user-defined functions, interact with other SAS procedures, and recognize efficient programming techniques |
proc iml, proc step, sample, likelihood function, maximum likelihood estimation, estimate, dataset | source | 21 |
| 171 | Give the Power of SAS to Excel Users Without Making Them Write SAS Code | William E. Benjamin Jr. | Merging the ability to use SAS and Microsoft EXCEL can be challenging. However, with the advent of SAS Enterprise Guide, SAS Integration Technologies, SAS BI Server software, JMP software, and SAS Add-In for Microsoft Office; this process is less cumbersome. EXCEL has the advantages of being cheap, available, easy to learn, and flexible. On the surface, SAS and EXCEL seem widely separated without these additional SAS products. But wait, BOTH SAS AND EXCEL CAN INTERFACE WITH THE OPERATING SYSTEM. SAS can run EXCEL using the command and EXCEL can run SAS as an "APPLICATION." This is NOT DDE; each system works independently of the other. This paper gives an example of EXCEL controlling a SAS process and returning data to EXCEL |
excel, xls, data step, proc printto, conditional do loop, proc step, sas base, macro variable | source | 27 |
| 172 | Go Mobile with the ODS EPUB Destination | David W. Kelley, Ed Summers, Julianna Langston |
The Base SAS Output Delivery System (ODS) makes it easy to generate reports for viewing on desktops. What about mobile devices? If you need on-the-go reports, then the new SAS 9.4 ODS EPUB destination is the ticket. With ODS EPUB, you can generate your reports as e-books that you can read with iBooks on the iPad, or, you can write an e-book from scratch if you like. This paper provides an introduction to writing e-books with ODS EPUB. Please bring your iPad, iPhone, or iPod to the presentation so that you can download and read the examples |
proc step, proc freq, proc print, cloud storage, sas output, bar chart, sas base | source | 23 |
| 173 | Good as New or Bad as Old? Analyzing Recurring Failures with the RELIABILITY Procedure | Bobby Gutierrez | Y ou can encounter repeated failure events in settings ranging from repairs of equipment under warranty to treatment of recurrent heart attacks. When a system fails repeatedly, the risk of failure can change with each subsequent failure-a unit once repaired or a patient once treated might not be "good as new." Analysis with the RELIABILITY procedure focuses primarily on the mean cumulative function (MCF), which represents either the average number of failures per unit over time or some related cost measure. This paper describes how you can use PROC RELIABILITY to estimate and compare MCFs. Y ou can choose either a nonparametric or a parametric approach. Features added to the procedure in SAS/QC 12.1 are highlighted |
output delivery system, piecewise exponential model | source | 10 |
| 174 | Grand Designs: Why It Pays to Think About Technical Architecture Design Before You Act | Simon Williams | The sustainability of a business is built not on short term objectives, but on effective planning and considered thinking. The same principle of sustainability applies for IT systems, including those leveraging technologies from SAS. Getting the best out of SAS technology is easy once all the IT and business requirements and constraints are clearly defined, understood, and agreed upon as being testable. This paper describes the effective thinking and systematic approach that can be applied to creating a sustainable SAS solution. The paper also outlines the advantages to this approach and describes a simple checklist that can be advantageous when designing a new SAS environment | data cleaning, raw data, sample | source | 18 |
| 175 | Graph Your SAS Off | Karena Kong | This paper demonstrates three different SAS procedures for creating graphs. For illustration purposes, the bubble plot, which is created by these procedures, shows the ratio of broadband users (DSL, Cable, Other) ranked by population ("List of countries"). The three procedures are SAS/GRAPH - GPLOT, Statistical Graphics (SG) - SGPLOT and Graphics Template Language (GTL) - PROC TEMPLATE with SGRENDER. This paper will discuss the advantages and disadvantages between each one. Based on the comparisons, it recommends which procedure should be used to create a similar graph |
bubble plot, proc step | source | 5 |
| 176 | Growth Spline Modeling | Eric Day, Matthew Schuelke, Robert Terry |
Although researchers in many fields have long theorized about how the contributions of antecedents to outcomes might change across time, only recently have statistical tools necessary to examine such relationships become commonplace. In these situations the flexibility to simultaneously model ever smaller groups of observations both cross-sectionally (i.e., how the relative contribution of the antecedents differs among individuals) and longitudinally (i.e., how the relative contribution of the antecedents differs through time) is desirable (Singer, 1998). The SAS MIXED proceedure is a relatively new tool which provides this requisite flexibility. In this paper we will present an extensible, hybrid statistical approach comprised of spline modeling and growth modeling which allows for an examination of how the relative antecedent contributions to an outcome change through time while simultaneously controlling for past effects | proc mixed, covariance, standard deviation, likelihood function, fixed effects, proc step | source |
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| 177 | GTL to the Rescue! | Lelia McConnell | You just produced some graphs using the SGPLOT and SGPANEL procedures. Now you want to modify the structure of your graphs in order to make them more meaningful. You are looking for options that enable you to split the axis values across multiple lines, add a table under a graph, or create a template with which you can conditionally execute statements and dynamically assign variables. However, you cannot find any options within the procedure that enable you to put these final touches on your graphs. When all seems hopeless, ODS Graphics Template Language (GTL) comes to the rescue! |
bar chart, proc step, scatter plot, proc sgplot, sample, proc sgpanel | source | 20 |
| 178 | Hands-On SAS Macro Programming Tips and Techniques | Kirk Paul Lafler | The SAS Macro Language is a powerful feature for extending the capabilities of the SAS System. This presentation highlights a collection of techniques for constructing reusable and effective macros tools. Attendees are introduced to the techniques associated with building functional macros that process statements containing SAS code; design reusable macro techniques; create macros containing keyword and positional parameters; utilize defensive programming tactics and techniques; build a library of macro utilities; interface the macro language with the SQL procedure; and develop efficient and portable macro language code |
macro variable, macro facility, proc step, autocall macro, macro definition, macro function, macro parameter, %macro parameters, proc sql, dictionary tables, options statement, macro loop, &variable | source | 9 |
| 179 | Hardening a SAS Installation on a Multi Tier installation on Linux | Jan Bigalke | The security requirements of today require in some use cases the hardening of a SAS Installation. This paper describes the practical steps of securing the SAS web applications and the impact to the Base SAS Services on the SAS compute tiers. The SAS Enterprise BI Server will be the object of this explanation. The principals of a secure architecture will be described and the options to secure the individual components presented | sas base, sas enterprise guide | source | 6 |
| 180 | Hashing in PROC FCMP to Enhance Your Productivity | Andrew Henrick, Donald Erdman, Stacey M. Christian |
Hashing is an important tool often used to improve performance of operations such as merging, filtering and searching. Hashing has been supported in the DATA STEP for more than a decade, and beginning with SAS 9.3 is available to user-defined subroutines through the FCMP Procedure. Subroutines have always encapsulated and modularized code, making programs reusable, and the addition of hashing allows users to extend the scope of their programs, tackling larger problems without sacrificing simplicity. This paper compares existing hashing support in the DATA STEP with the new implementation in the FCMP Procedure. Examples are provided demonstrating how hashing in user-defined subroutines is used to improve performance and streamline an existing program |
hash table, hash iterator, proc fcmp, sysrc, proc step, where statement, cpu time, dataset, numeric variable, hashing function | source | 15 |
| 181 | Have it Your Way: Creating Reports with the DATA Step Report Writing Interface | Peter J. Lund | SAS provides some powerful, flexible tools for creating reports, like PROC REPORT and PROC TABULATE. With the advent of the Output Delivery System (ODS) you have almost total control over how the output from those procedures looks. But, there are still times where you need (or want) just a little more and that's where the Report Writing Interface (RWI) can help. The Report Writing Interface is just a fancy way of saying you're using the ODSOUT object in a DATA STEP. This object allows you to layout the page, create tables, embed images, add titles and footnotes and more - all from within a DATA STEP, using whatever DATA STEP logic you need. Also, all the style capabilities of ODS are available to you so that your DATA STEP created output can have fonts, si zes, colors, backgrounds and borders to make your report look just like you want |
data step, ods pdf, proc report, proc step, calendar year, output delivery system, proc template, set statement, create table | source | 27 |
| 182 | Having an EFFECT: More General Linear Modeling and Analysis with the New EFFECT Statement in SAS/STAT Software | Jill Tao, Kathleen Kiernan, Phil Gibbs, Randy Tobias |
Linear models relate a response to a linear function of a design matrix X. General linear models, long available in standard SAS/STAT 9.3 procedures such as GLM and MIXED, incorporate classification, interaction, and crossproduct effects to define X. The new EFFECT statement, which is available in many SAS/STAT 9.3 procedures, extends how you can define X. It enables you to fit models with nonparametric regression effects, crossover and carryover effects, and complicated inheritance effects. This paper first shows how the EFFECT statement fits into the general architecture of SAS/STAT linear modeling tools and then explains and demonstrates specific effect-types. You will see how this powerful new feature easily enhances the statistical analyses that you can perform |
polynomial effect, estimate, proc step, proc glm | source | 14 |
| 183 | Healthcare Claims Processing with Base SAS through Denormalization of ANSI 837 Format | Roberto Valverde, Victor Shigaev |
Sometimes dealing with healthcare claims can be messy. As a result of HIPAA, all health insurance claims must be submitted to insurance payers using the ANSI X12 837 messaging standard. This standard creates a compact hierarchical file for quick transmission between trading partners but because of the really complex nested structure of the data this standard is not always easy to read in and be analyzed. The paper will give a brief introduction to the X12 837 messaging standard, provide users a simple way to divide raw claims data by claims through de- normalization, and a way to use SAS as a main tool to process and analyze the claims data | insurance claims, lookup, proc sql, health insurance, normalization, proc step | source | 7 |
| 184 | Here Comes Your File! File-Watcher Tool with Automated SAS Program Trigger | Rajbir Chadha | In a typical warehousing environment, users are notified about file availability by system generated e-mails. Users then run their SAS processes. There was a need for a file-watcher tool to trigger the user processes as soon as input files become available. This paper talks about a file-watcher tool that searches for files and looks to see when they were last updated. The tool uses UNIX shell scripting to search for the file. The parameters to the file-watcher tool are supplied using a 'wrapper' script that can be modified by the user as and when required. It can be programmed to look for the file within a specified time period. The script is scheduled using a 'CRON' scheduler in UNIX. The file-watcher searches for the updated file based on the specified parameters. Once the updated version is found, the SAS program execution begins. If the file is not found or not updated within the specified time period, the script terminates | sysrc, sample | source |
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| 185 | Here Is How We Do It: Teaching SAS at Community Colleges | Meili Xu | Data is everywhere today and SAS skills are in high demand. Providing community college students with SAS skills is extremely valuable in preparing them for real-world job positions. In this paper, we will describe our experiences and approaches to teaching SAS at Ohlone College. With the paper presentation at the conference, we wish to share ideas and resources with other educators teaching SAS so that we may all better train our students with strong SAS skills to face the real world challenges | data science, sas base, life sciences | source |
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| 186 | High Performance Statistical Modeling | Bob Rodriguez, Robert A. Cohen |
The explosive growth of data, coupled with the emergence of powerful distributed computing platforms, is driving the need for high-performance statistical modeling software. SAS has developed a group of high-performance analytics procedures that perform statistical modeling and model selection by exploiting all the cores available-whether in a single machine or in a distributed computing environment. This paper describes the various execution modes and data access methods for high-performance analytics procedures. It also discusses the design principles for high-performance statistical modeling procedures and offers guidance about how and when these procedures provide performance benefits | significance level, proc hpgenselect, proc step, poisson distribution, regression analysis, grid computing, proc hpsplit, response variable, proc genmod, logistic distribution, proc hplogistic | source | 19 |
| 187 | Horizontal Data Sorting and Insightful Reporting: A Useful SAS Technique | Amanda Lin, Justin Jia |
Sorting and ordering of data is a fundamental skill in SAS data analysis. Data sorting can be vertical sorting, across rows, or horizontal sorting, across columns. Compared to vertical sort, horizontal sort is used less frequently, and it requires the user to employ multiple sophisticated SAS skills such as Transpose, Rotate, Array, Macro, etc. It is also an important and useful technique for advanced data analysis and reporting in customer profiling and metrics, which can significantly enhance the format and layout of data reporting, and thus provide informative insights into data. This paper will discuss the different approaches and methods of performing horizontal sorting and presentation of SAS data, which can also expand our horizon on data manipulation and SAS programming skills | proc step, call sortn, missing value, call sortc, proc transpose, data science, proc freq, proc print, proc sort, excel, sas programming, order by, business intelligence, character variable, proc means, input statement, proc sql, sample, character functions | source | 14 |
| 188 | Hot off the Press: SAS Marketing Automation 6.1 | Brian Chick, Mark Brown |
SAS Marketing Automation 6.1 is a major release that introduces a new browser -based user interface for business analysts. The new look and feel deliver a highly interactive and intuitive user experience while delivering key customer-driven features, including support for control groups, live seeds, enhanced campaign definitions, and improved scheduling and execution. This paper introduces new navigation, improved search capabilities and easier sharing of information; it also presents easier reuse of treatments, campaign components, and scheduling and control group methodologies. Various control group techniques will be presented, including: Hold-out control groups A/B testing control groups o Champion/challenger o Challenger/challenger Business case studies based on customer examples illustrate the matching of campaign business goals with the appropriate control group technique and why they are needed for robust marketing measurement | a/b testing, data standard | source | 16 |
| 189 | How Am I Driving - My Business? (Techniques, from the Insurance Industry That Can Be Applied to Other Business Areas to "Drive" Better Performance) | Guy R. Garrett, Steve Morton |
Business Intelligence is widely accepted as being a crucial piece in the organizational performance puzzle. Many companies ranging from Small to Medium Enterprises up to and including Large Corporate Organizations , in both the private and public sector , use BI to measure and report how well the business is performing against it 's targets. With the "bigness" of Big Data growing, and the "highness" of High Performance Analytics also increasing one may wonder what the future of traditional MI might be? HPA and Big Data is a definite-game changer regarding being able to analyze billions of data items in a matter of seconds. Directors of Analytics can sit around the boardroom table with their Executive peers exploring opportuni ties for growth and identifying areas for cost saving s, in a way that can affect corporate strategy instantaneously | run-off triangle, claims development, loss ratio, business intelligence, development age, insurance, management reporting, excel, sas enterprise guide, proc sql | source | 15 |
| 190 | How Mobile Changes the BI Experience | Murali Nori | The advent of a new generation of tablets catapulted corporations' use of mobile devices. With SAS Mobile BI for tablets, anyone who uses BI for work and decision making has a new way to experience BI content. This paper presents some end-to-end use cases to demonstrate how revolutionary the user experience is with SAS Mobile BI. It also demonstrates how easy it is to access and navigate BI content. Discover how BI on mobile devices changes the user experience and the reach of BI content for productivity, decision making, and extracting better ROI | customer satisfaction, business intelligence | source | 11 |
| 191 | How to Automate Security Filters for SAS OLAP Cubes Using Users Groups Information Available in SAS Management Console | Plinio Faria | In order to limit the data that a user can access in a OLAP Cube, it is required to use MDX conditions and those expressions must be customized for every OLAP Cube because each one has a different structure. This paper will focus on showing how to automate the creation of Multidimensional Expressios (MDX) conditions using the users groups information available in the metadata server and in a way that is possible to change the data subset that the users are allowed to see only changing the user group in SAS Management Console. It will also be demonstrated how to implement OLAP Cube security adding directly the users login to a cube dimension | proc sql, proc step, sas management console, create table | source | 5 |
| 192 | How to Become a GTL/PROC Template Jedi | Christopher Battiston | This tongue-in-cheek paper will bring together Star Wars and SAS, answering (at least potentially) how would SAS have been used a long time ago in a galaxy far, far away? Using PROC TEMPLATE, GTL, and ODS, examples will be shown of reports that could have been used by the Empire and the Rebel Alliance. Topics will include creating reports for mobile devices, bringing in images into th e reports, and creating dynamic reports - without using Jedi mind tricks on anyone! |
proc step, proc template, bar chart | source | 21 |
| 193 | How to Choose the Best Shared File System For Your Distributed SAS Deployment | Barbara Walters, Ken Gahagan, Leigh A. Ihnen, Vicki Jones |
A shared file system is an integral component of all SAS Grid Manager deployments, Enterprise Business Intelligence deployments with load balanced servers on multiple systems, and other types of distributed SAS applications. This paper explains how SAS software interacts with the file system and how a shared file system behaves differently from a non-shared file system. It describes what factors will determine whether a particular file system is a good fit for your environment and how to choose the file system that best meets your needs | options statement, metadata management | source | 8 |
| 194 | How to Do a Successful MDM Project in SAP Using SAS MDM Advanced | Casper Pedersen | Master Data Management (MDM) made me wonder how difficult it would be to embark on MDM projects at a large SAP organization. Typically, SAP organizations come with large numbers of people with opinions, large project plans, and large amounts of complexities and politics. Don't go with the BIG BANG approach, but do a controlled evolution. Let me show you what one approach to the KNA1 (Customer Master) table and MARA (Material Data) table might be | data profiling, data validation rules, data cleaning | source | 6 |
| 195 | How to Improve Your Risk-Adjusted Return on Capital: Pricing Optimization in Lending | Boaz Galinson | Lending is the core business of most banks. One may think that a bank should opt for a lending pricing strategy of seeking the highest price that the credit officer can obtain from his borrower. This paper claims that following a "maximal price" strategy will eventually lead to an inferior credit portfolio and poor performance compared to competitors. In this paper, I describe how to price a loan to meet at least the return required by the stock holders and to improve risk adjusted return on capital RAROC. Following that strategy may be a challenge as in some assets classes, for example, in corporate credits. It can be difficult to agree a price which includes the minimal credit risk premium which compensates the risks. A suggested solution which takes account of all activities of the borrower with the bank is presented | credit portfolio, credit risk | source | 6 |
| 196 | How to List All Users That Have Access to a SAS Information Delivery Portal 4.3 Page | Bernt Dingstad | This paper describes how to access SAS Metadata from Base SAS client and make simple listings of often very urgent information and in the end distribute this information utilizing the SAS EBI framework | sas base, data step | source | 6 |
| 197 | How to Use ARRAYs and DO Loops: Do I DO OVER or Do I DO i? | Jennifer L. Waller | Do you tend to copy DATA STEP code over and change the variable name? Do you want to learn how to take those hundreds of lines of code that do the same operation and reduce them to something more efficient? Then come learn about ARRAY statements and DO loops, powerful and efficient data manipulation tools. This workshop covers when ARRAY statements and DO loops can and should be used, how to set up an ARRAY statement with and without specifying the number of array elements, and what type of DO loop is most appropriate to use within the constraints of the task you want to perform. Additionally, you will lear n how to restructure your data set using ARRAY statements and DO loops |
do loop, do over, end, array | source | 11 |
| 198 | Identifying and Addressing Post-Marketing Pharmaceutical Safety Surveillance and Spontaneous Reported Events | Carrie Boorse, Kathy Schaan, Stuart B. Levine |
While pharmaceutical medications and medical devices must undergo clinical trials to determine their safety, often their long-term side effects will not be recognized until the medication or device has been approved and consumed by a larger population of patients for a longer period of time. Signals that an adverse event is brewing need to be identified by using post-marketing data as expeditiously as possible. Using SAS analytical platform and foundation products, a solution was implemented that incorporates recognized statistical procedures and enables users to incorporate new and enhanced procedures for signal determination. The solution also alerts users that a signal has been triggered, while also managing the workflow and results of the signal's investigation | source | 14 | |
| 199 | If You Have Programming Standards, Please Raise Your Hand: An Everyman's Guide | Dianne Louise Rhodes | This paper goes through a step-by-step process of developing programming standards, classifying them, and entering them into a database. This database can then be used to develop style sheets and check lists for peer review and testing. Through peer reviews and in preparation for them, programmers learn good programming practices. We describe in detail the most common standards, and why and how they should be applied | macro variable, by group processing | source | 8 |
| 200 | Impact of London Olympics According to Tweeters | Goutam Chakraborty, Shirmeen Virji, Yu Fu |
The theme of our paper is the impact of London Olympics 2012 on the economy of England as stated by people on social media. Overspending money on Olympics by the host country with the hope of giving a huge boost to the economy is an age old phenomenon. With an estimated cost of £9.3 billion ($14.8 billion), the economy of England is expected to get a boost, according to the British government, as there were 300,000 people in London to attend the games on top of 600,000 tourists. Only time will tell the real economic impact, but for now we are more interested in knowing what people have to say about the spending on the Olympics. For the purpose of analyzing sentiments of the people, we collected tweets between July 20 and August 15 by putting a filter of "Olympics AND economy". We have used Text Mining node on SAS Enterprise Miner 7.1 and SAS Sentiment Analysis Studio to build the models and analyze the results | data mining, data cleaning | source |
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| 201 | Implementation of Slowly Changing Dimension to Data Warehouse to Manage Marketing Campaigns in Banks | Lihui Wang, Michelle Cheong, Murphy Choy |
Banks faces the challenges of managing marketing campaign leads in its data warehouse. The management of marketing campaign leads in data warehouse with real time updating and recording is complex especial ly when multiple campaigns are active simultaneously. To prevent the customers from being contacted too often for sales- based marketing contacts, the concept of Recency Frame is introduced to suppress the customers who have been targeted in Sales -based campaign for a specified time period. During the Recency Frame, the customer cannot be targeted by other Sales -based campaign catego rized under the same channel. The introduction of Recency Frame has increased the difficulties of campaign management and data management. The difficulties of data management include timely update and robust storage system of campaign leads | scd type 2, slowly changing dimension, data warehouse, dimension table, data governance, surrogate key | source | 8 |
| 202 | Implementing CDISC, SDTM, and ADaM in a SAS Environment | Pankaj Bhardwaj | Pharmaceutical companies require speedy regulatory approval for their medicines, to ensure faster time to market and to remain competitive. Regulatory bodies are inundated with large amounts of clinical trial data from various organizations, in non-standard format, resulting in delays in the review and approval process. The existing situation has served as a catalyst in furthering initiatives that focus on establishing standards for clinical trial data submissions, leading to a streamlined approval process. Organizations are implementing frameworks that offer support for this standardization | clinical trial, clinical data, sas base, data science, raw data | source |
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| 203 | Implementing Metadata-driven Reports with SAS Stored Processes | Toby Hill | As more organizations that use SAS software are implementing the full Business Intelligence reporting suite, many SAS programmers are becoming familiar with developing Stored Processes to deliver reports for the business. Developers are often required to implement content security in the reports or provide additional functionality for users with specific roles. How can all this be done? One approach is to make use of the SAS metadata. This paper demonstrates some techniques that you can apply to your SAS code in order to make use of the SAS metadata. This will allow you to implement security and role-based access in your Stored Process reports and minimize the amount of changes required as new users access the platform | macro variable, structured data, macro definition | source | 10 |
| 204 | Improving Your Relationship with SAS Enterprise Guide: Tips from SAS Technical Support | Jennifer Bjurstrom | SAS Enterprise Guide has proven to be a very beneficial tool for both novice and experienced SAS users. Because it is such a powerful tool, SAS Enterprise Guide has risen in popularity over the years. As a result, SAS Technical Support consultants field many calls from users who want to know the best way to use the application to accomplish a task or to obtain the results they want. This paper encompasses many of those tips that SAS Technical Support has provided to customers over the years. These tips are designed to improve your proficiency with SAS Enterprise Guide in the areas of connection and configuration, workflow preferences, logging, data source management, project files, operating-system commands, and custom tasks | sas enterprise guide, sas base, sql pass-through, excel, implicit sql pass-through, record count, sas programmer, proc sql, dataset | source | 20 |
| 205 | In-Database Data Quality: Performance for Big Data | Charlotte Crain, Mike Frost, Scott Gidley |
Data quality and high performance have joined forces. Today is an era of big data, extremely large data warehouses and potential security issues for moving data. Traditional data quality is performed with an ETL-like operation of extracting, processing, and publishing back to the source. Performance, or a potential for security issues associated with moving data, requires a call for a new approach. This paper explains how SAS data quality functions can be invoked in-database, eliminating the need to move data and thus delivering data quality that meets the need for near real-time performance for today's business. Graphic results comparing performance metrics of traditional data quality operations against in-database data quality will be presented along with details illustrating how these results scale with database resources | data cleaning, big data, in-database processing, record count, data governance, data warehouse | source | 12 |
| 206 | Increase Your Productivity by Doing Less | Arthur S. Tabachneck, Joe Whitehurst, Robert Virgile, Xia Ke Shan |
Using a keep dataset option when declaring a data option has mixed results with various SAS procedures. It might have no observable effect when running PROC MEANS or PROC FREQ but, if your datasets have many variables, it could drastically reduce the time required to run some procs like PROC SORT and PROC TRANSPOSE. This paper describes a fairly simple ma cro that could easily be modified to use with any PROC that defines w hich variables should be kept and, as a result, make your programs run 12 to 15 times faster |
proc step, proc transpose, proc sort, cpu time, macro definition, by group processing | source | 5 |
| 207 | Incremental Response Modeling Using SAS Enterprise Miner | Laura Ryan, Ruiwen Zhang, Taiyeong Lee, Xiangxiang Meng |
Direct marketing campaigns that use conventional predictive models target all customers who are likely to buy. This approach can lead to wasting money on customers who will buy regardless of the marketing contact. However, incremental response models that use a pair of training data sets (treatment and control) measure the incremental effectiveness of direct marketing. These models look for customers who are likely to buy or respond positively to marketing campaigns when they are targeted but are not likely to buy if they are not targeted. The revenue generated from those customers is called incremental revenue. This paper shows how to find that profitable customer group and how to maximize return on investment by using SAS Enterprise Miner | data mining | source | 13 |
| 208 | Information Retrieval in SAS: The Power of Combining Perl Regular Expressions and Hash Objects | Fagen Xie, Lingxiao Qi |
The volume of unstructured data is rapidly growing. Effectively extracting information from these huge unstructured data is a challenging task. With the introduction of Perl regular expressions and hash objects in SAS 9, the combination of these two tools is very powerful in information retrieval. Perl regular expressions is useful in searching and manipulating various complex string patterns. The hash object provides an efficient and convenient way for fast data storage and retrieval. By leveraging both tools on the electronic medical data, we are able to demonstrate how pattern searching on free text can be simplified while reducing coding effort and running time | hash table, hash iterator, lookup, do loop | source | 6 |
| 209 | Integrated Framework for Stress Testing in SAS | Jimmy Skoglund, Wei Chen |
Stress testing is an integrated part of enterprise risk management and is a regulatory requirement. Stress testing is especially useful for integrating forward-looking views into risk analysis. Indeed, stress tests can provide useful information about a firm's risk exposure that statistical risk methods, calibrated on the basis of history, can miss. However, traditional stress testing is done on a stand-alone basis. This makes the interpretation of risk obtained from stress events vs. from risk analysis with statistical models difficult to interpret. We consider a Markov model and innovative implementation in SAS that integrates rare stress events into regular statistical risk models. The model allows a consistent integration of the information in backward-looking historical data | stress testing, correlation, sas risk engine, normal distribution, stress scenario, time series analysis, confidence level, model calibration, sas risk, portfolio risk | source | 11 |
| 210 | Integrating SAS into Your Operational Environment: SOA: A Means to an End | Rob Stephens, Saravana Chandran |
The impacts of business analytic models have proven value for enterprises. Many SAS customers have highly valuable analytical assets ranging from Analytical Models to Analytical Services specific to a domain. We have seen a significant stream of requests from customers for assistance in taking the next step and deploying these models into their primary operational business applications. SOA (Service Oriented Architecture) is an architectural style designed to enable Flexibility, Reusability and Interoperability and provides one of the primary means for integrating SAS with your operational application environment. The paper walks through the Integration, Runtime Environment, Governance and Best Practices all in the context of SOA and SAS Business Analytics | web service, data mining, sas model manager, data cleaning | source | 8 |
| 211 | Internationalization 101: Give Some International Flavor to Your SAS Applications | Mickael Bouedo, Steve Beatrous |
Do you have SAS users worldwide? Do you want your SAS application to be usable in many languages? SAS 9.4 internationalization features will get you there efficiently. If you want to adapt your SAS application for different cultures, SAS internationalization is the step that generalizes your product to be language independent. Internationalization features in SAS include the ENCODING, LOCALE, and TIMEZONE options; the SASMSG function; the NL formats; and many more features that help you write code once, so it can run in different cultural environments with low maintenance. This paper describes how to successfully internationalize your SAS programs and make them ready for the world | dataset, macro function, index, sas log, options statement, string functions, macro facility, macro variable, proc format, proc sort, proc step | source | 21 |
| 212 | Introducing and Producing Thunderstorm or Rain-drop Scatter Plots Using the SAS/GRAPH Annotate Facility | Charlie Liu | A new type of plot, the thunderstorm or rain-drop scatter plot is introduced. Such a plot allows for viewing data with 2 or more values on the y-axis corresponding to one value on the x-axis for each of several subjects in a population. The resulting plot looks like rain-drops, with each rain-drop representing data for a single subject. When data for many subjects are plotted, it resembles a thunderstorm, hence the name. A thunderstorm/rain-drop scatter plot is a useful tool for data visualization and outlier detection. It has applications in many areas including clinical research, agriculture, and finance, etc. Using examples from clinical research, this paper shows how to create thunderstorm/rain-drop scatter plots using the SAS/GRAPH annotate facility, a convenient tool to connect the 2 or more values on the y-axis of the same subject to form rain-drops. The dataset structure is described and the SAS programs producing the sample plots are provided and discussed |
scatter plot, call symput, sample, clinical research, data visualization, macro variable, proc step, outlier | source | 13 |
| 213 | Introducing the New ADAPTIVEREG Procedure for Adaptive Regression | Warren F. Kuhfeld, Weijie Cai |
Predicting the future is one of the most basic human desires. In previous centuries, prediction methods included studying the stars, reading tea leaves, and even examining the entrails of animals. Statistical methodology brought more scientific techniques such as linear and generalized linear models, discriminant analysis, logistic regression, and so on. In this paper, you will learn about multivariate adaptive regression splines (Friedman 1991), a nonparametric technique that combines regression splines and model selection methods. It extends linear models to analyze nonlinear dependencies and to produce parsimonious models that do not overfit the data and thus have good predictive power. This paper shows you how to use PROC ADAPTIVEREG (a new SAS/STAT procedure for multivariate adaptive regression spline models) by presenting a series of examples that show the relationship between adaptive regression models and other statistical modeling techniques |
proc step, proc adaptivereg, regression analysis, feature importance, logistic distribution, poisson distribution | source | 18 |
| 214 | Introduction to ODS Graphics | Chuck Kincaid | SAS has a new set of graphics procedures called ODS Graphics. They are built on the Graph Template Language (GTL) in order to make the powerful GTL easily available to th e user. PROC SGPLOT and PROC SGPANEL are two of the procedures that can be used to produce powerful graphics that previously required a lot of work. This upgrade is similar to the ease -of-use upgrade in output manipulation when ODS was first published. Thi s hands-on workshop teaches you how to use PROC SGPLOT and PROC SGPANEL and the new capabilities they provide beyond the standard plot. By using these new capabilities, anyone can tell the story better |
proc step, series plot, proc sgplot, proc sgpanel, numeric variable | source | 18 |
| 215 | Inventory Your Files Using SAS | Brian K. Varney | Whether you are attempting to figure out what you h ave when preparing for a migration or you just want to find out which files or directories are taking up all of you r space, SAS is a great tool to inventory and repor t on the files on your desktop or server. This paper intends to prese nt SAS code to inventory and report on the location you want to inventory | dataset, proc sql, sas enterprise guide | source | 5 |
| 216 | Investigating the Impact of Amazon Kindle Fire HD 7? on Amazon.com Consumers Using SAS Text Miner and SAS Sentiment Analysis View Paper View Poster | Goutam Chakraborty, Hari Hara Sudhan Duraidhayalu, Srihari Nagarajan |
This pape r demonstrates the application of text mining techniques to collect, group and summarize positive and negative opinions on a product. Unfortunately for popular products often there are far too many reviews that make it difficult to read through all reviews and make a decision. For the purpose of this poster, I have developed a tool using ASP.NET to extract (1674) customer reviews for Kindle Fire HD 7" from Amazon.com. On the EXCEL data set thus generated, text mining can be performed to summarize customer comments by grouping related reviews into clusters. The Text Parsing, Text Filter, Text Topic and Text Cluster nodes are used and outputs from every node are discussed. Sentiment Analysis is performed on the data set to develop a model for classifying positive and negative reviews |
proc hpsplit, excel, parsing, dataset, data cleaning, data mining, proc freq | source |
14 |
| 217 | Is the Legend in your SAS/Graph Output Still Telling the Right Story? | Alice M. Cheng, Justina M. Flavin |
In clinical studies, researchers are often inter ested in the effect of treatment over time for mult iple treatments or dosing groups. Usually, in a graphical report, the measur ement of treatment effect is on the vertical axis a nd a second factor, such as time or visit, on the horizontal axis. Multipl e lines are displayed in the same figure; each line represents a third factor, such as treatment or dosing group. It is critical that the line appearance (color, symbol and style) are consistent throughout the entire clinical report, as well as, across clinical reports from related studies. Flavin and Carpenter (2004) showed that the GPLOT p rocedure, by default, did not guarantee consistency in line appearances. They provided macro and non-macro sol utions to this problem. With the introduction of S tatistical Graphics (SG) in SAS v9.3, there are multiple approaches to resolve this problem |
proc sgplot, class variable, proc gplot, graph template language, proc step | source | 18 |
| 218 | ISO 101: A SAS Guide to International Dating | Peter W. Eberhardt, Xiaojin Qin |
For most new SAS programmers SAS dates can be confusing. Once some of this confusion is cleared the programmer may then come across the ISO date formats in SAS, and another level of confusion sets in. This paper will review SAS date, SAS DATETIME, and SAS time variables and some of the ways they can be managed. It will then turn to the SAS ISO date formats and show how to make your dates international |
sysdate9, datetime variable, date variable, time variable, datetime, informat statement, macro variable, time, proc sql, relational database, sas function, sas programmer | source | 22 |
| 219 | Joint Modeling of Mixed Outcomes in Health Services Research | Joseph C. Gardiner | Outcomes with different attributes, of continuous, count and categorical types are often encountered jointly in many settings. For example, two widely used measures of healthcare utilization, length of stay (LOS) and cost can be analyzed jointly with LOS as a count and cost as continuous. Occurrence of an adverse event (binary) would impact both outcomes. For fitting marginal distributions and assessing the impact of explanatory variables on outcome SAS offers a number of procedures. Correlation and clustering are additional features of these outcomes that must be addressed in analyses. We survey some SAS procedures, GLIMMIX, COPULA, PHREG and QLIM that can be applied to modeling multivariate outcomes of mixed types. Examples from the extant literature are used to demonstrate the application of the procedures | proc step, likelihood function, sample, probability distribution, maximum likelihood estimation, correlation, normal distribution, adverse events, scatter plot, degrees of freedom, logistic distribution, kendall correlation, cumulative hazard | source | 16 |
| 220 | Kerberos and SAS 9.4: A Three-Headed Solution for Authentication | Stuart J. Rogers | Kerberos is a network authentication protocol designed to provide strong authentication for client/server applications by using secret-key cryptography. With the release of SAS 9.4, there are three ways Kerberos can be used with the SAS Business Analytics Framework. Kerberos provides Integrated Windows authentication from a range of clients to a range of servers. This paper reviews how Kerberos is used with the SAS Business Analytics Framework. It explores the considerations and constraints when using Kerberos and summarizes solutions for some common issues | error message, sas enterprise guide, data mining | source | 15 |
| 221 | Key Aspects to Implement A Perfect SAS BI Platform | Gaurav K. Agrawal | A perfect SAS architecture is not defined just by successful installation of a SAS platform but also by ensuring good performance, easy maintenance, compliance to all security, Secured environment, scalability, good administration practices, proper monitoring, seamless Integration with Interfacing system etc. SAS provides lot of flexibility in order to integrate with other interfacing systems. However a perfect SAS Enterprise Implementation requires matured implementation of other interfacing platforms. SAS user experience starts from the first click on the SAS client and driven from SAS environment capabilities and its integration to interfacing systems. User experience can be further enhanced only if systems around SAS environment provide seamless integration. Hence interfacing systems also have a key role to play in order to get perfect SAS architecture | sas grid, access control, capacity planning, in-database processing | source | 12 |
| 222 | Know Thy Data: Techniques for Data Exploration | Andrew T. Kuligowski, Charu Shankar |
Get to know the #1 rule for data specialists: Know thy data. Is it clean? What are the keys? Is it indexed? What about missing data, outliers, and so on? Failure to understand these aspects of your data will result in a flawed report, forecast, or model. In this hands-on workshop, You will learn multiple ways of looking at data and its characteristics. You will learn to leverage PROC MEANS and PROC FREQ to explore your data. You will learn how to use PROC CONTENTS and PROC DATASETS to explore attributes and determine whether indexing is a good idea. You will learn to employ powerful PROC SQL's dictionary tables to easily explore aspects of your data |
proc step, dataset, proc sql, proc contents, dictionary tables, proc freq, proc print, cpu time, sas output, proc datasets, proc univariate, sas log, output, variable list, proc calis | source | 19 |
| 223 | Knowing Me, Knowing My UI (ah-haa): Understanding SAS 9.3 and SAS 9.4 Desktop and Web Client Application Usage Within Your Organization | Edoardo Riva, Simon Williams |
Understanding which groups of users are using which sets of desktop and web applications can help your organization increase efficiency while reducing the risk to your operations. This paper details the importance of understanding users and desktop application interactions, and the methods by which these interactions can be defined and reported on. Working examples using SAS desktop and web applications such as SAS Enterprise Guide, SAS Data Integration Studio, SAS Management Console, and SAS Web Report Studio are presented | sas enterprise guide, sample, dataset, sas management console, data mart, audit dataset, create table | source | 15 |
| 224 | Knowing When To Start, Where You Are, and How Far You Need To Go: Customized Software Tracks Project Workflow, Deliverables, and Communication | Eric Vandervort | In a clinical trials environment, projects can have multiple statisticians and statistical programmers working on tables, listings, and figures, or "displays", for project deliverables. Communication between the various team members regarding when to program, validate, review, or revise these displays is vital to the success of a project. This paper describes a custom web-based application that stores relevant data about displays, tracks programming and reviewing workflow, and provides a tool for project-level management overview | adam dataset, data cleaning, statistical programmer, clinical programming, cdisc | source | 14 |
| 225 | Leveraging Big Data Using SAS High-Performance Analytics Server | Priya Sharma | With an increasing amount of information being collected by organizations, SAS High-Performance Analytics Server enables decision-makers to create greater business value by leveraging big data. This paper describes some best practices and techniques to use when dealing with big data analytics. The following topics are discussed: (1) different methods of loading data for Teradata and Greenplum appliances (2) checking the distribution of loaded data on the appliance, and (3) using the new HPDS2 procedure in SAS 9.3. This paper also gives a brief overview of how SAS High-Performance Analytics Server enables you to use 100% of your data to get more precise insights and to build complex models at breakthrough speed, using results from an example model |
big data, proc step, proc sql, proc append, feature selection, parallel processing, regression analysis, data mining | source | 9 |
| 226 | Line-Sampling Macro for Multistage Sampling | Charley Jiang, James Lepkowski, James Wagner, Richard Valliant |
The Sample Survey is today one of the most important methods for collecting information about the social and economic world. The success of the method can be attributed in part to probability selection of the sample. Probability samples can be complex in design, and require careful attention to detail to assure correct execution. In the SAS world, one tool, PROC SURVEYSELECT, is widely used for probability sample selection (see http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#surveyselect_toc.htm). However, the procedures implemented in SURVEYSELECT are basic selection tools that must be assembled into larger systems for complex probability samples, particularly multistage samples. This paper describes the development and operation of a set of sampling macros built around SURVEYSELECT for sampling the ultimate stage units in a multistage sample. Examples of several situations where the macro can be most beneficial are also given |
sample, macro variable, sample size, proc surveyselect, proc step, cluster sampling, sample weight | source | 10 |
| 227 | Linking Laboratory Data To Submission Documents Using SAS Technologies | Dongmin Shen, Miu Ling Lau |
Merck is a global pharmaceutical company and so the sources of our data are global. Having the ability to link and transfer massive amounts of analytical data from various data sources into submission documents in an efficient and reproducible way is critical to producing successful regulatory submissions. SAS technologies have been used to create various solutions ranging from data extractions, to data transformations, to documents generated in support of simultaneous worldwide new drug applications. 1. Connecting Laboratory Data to Submission Document Capabilities have been developed using SAS technologies, which enable the connection of laboratory data (across the globe) to our submission documents, generating standard outputs with the flexibility for customization within the different submissions/regions. Using electronic systems instead of manual documents allows us to gain efficiency, reproducibility and enhanced compliance (i.e. reduction in transcription errors) | macro definition, extract, in=, laboratory data, put statement, macro parameter | source |
3 |
| 228 | Linking Strategy Data in BI Applications | Bharat Trivedi, Christiana Lycan |
SAS Enterprise BI is a key part of every strategy management implementation, but it is not always easy to link strategy data with the correct views of information in sas Enterprise BI. And even when the linking is straightforward, users may not be authorized to see all of the information. As a result, several views of the same report must be created for linking. Users need an easier way to link and display the correct view of strategy management content in SAS Enterprise BI. This paper reveals how to link information created in SAS Enterprise BI that is context-aware and secure | business intelligence, excel | source | 12 |
| 229 | Macro Basics for New SAS Users | Cynthia L. Zender | Are you new to SAS? Do you look at programs written by others and wonder what those & and % signs mean? Are you reluctant to change code that has macro variables in the program? Do you need to perform repetitive programming tasks and don't know when to use DO versus %DO? This paper provides an overview of how the SAS macro facility works and how you can make it work in your programs. Concrete examples answer these and other questions: Where do the macro variables live? What does it mean when I see multiple ampersands (&&)? What is a macro program, and how does it differ from other SAS programs? What's the big difference between DO and IF and %DO and %IF? | macro variable, sas enterprise guide, macro facility, macro definition, sas log, &variable, put statement, macro put, if-then-else, proc step, macro function, macro trigger, proc print, title statement, %if-%then-%else, global symbol table, keyword parameter, %macro parameters, proc contents, symbol table, macro loop, error message, options statement, upcase function | source | 26 |
| 230 | Macro Quoting to the Rescue: Passing Special Characters | Arthur L. Carpenter, Mary F. O. Rosenbloom |
We know that we should always try to avoid storing special characters in macro variables. We know that there are just too many ways that special characters can cause problems when the macro variable is resolved. Sometimes, however, we just do not have a choice. Sometimes the characters must be stored in the macro variable whether we like it or not. And when they appear we need to know how to deal with them. We need to know which macro quoting functions will solve the problem, and even more importantly why we need to use them. This paper takes a quick look at the problems associated with the resolution and use of macro variables that contain special characters such as commas, quotes, ampersands, and parentheses | macro variable, macro quoting function, macro call, call execute, macro trigger, proc print, where statement, proc step, macro function, macro facility, macro loop, do loop, autocall macro, %macro parameters, &variable | source | 12 |
| 231 | MACUMBA: Modern SAS GUI Debugging Made Easy | Michael Weiss | MACUMBA is an in-house-developed application for SAS programming. It combines interactive development features of PC-SAS, the possibility of a client-server environment and unique state-of-the-art features that were always missing. This presentation covers some of the unique features that are related to SAS code debugging. At the beginning, special code execution modes are discussed. Afterwards, an overview of the graphical implementation of the single-step debugger for SAS macros and DATA STEP is provided. Additionally, the main pitfalls of development are discussed |
macro call, macro variable, macro definition, proc sql, proc step, proc report, options statement | source | 9 |
| 232 | Maintaining Formats when Exporting Data from SAS into Microsoft Excel | Colleen E. McGahan, Nathaniel Derby |
Data formats often get lost when exporting from SAS into EXCEL using common procedures such as PROC EXPORT or the ExcelXP tagset. In this paper we describe some tricks to retain those formats. KEYWORDS: SAS, EXCEL, export, formats |
excel, proc step, proc export, missing value | source | 11 |
| 233 | Make a Good Graph | Sanjay N. Matange | A graph is considered effective if the information contained in it can be decoded quickly, accurately and without distractions. Rules for effective graphics - developed by indus try thought leaders such as Tufte, Cleveland and Robbins - include maximizing data ink, removing chart junk, reducing noise and clutter, and simplifying the graph. This presentation covers these principles and goes beyond the basics, discussing other feat ures that make a good graph: the use of proximity for magnitude comparisons, nonlinear or broken axes, small multiples, and reduction of eye movement for easier decoding of the data. We also examine ways in which information can be obscured or misrepresented in a graph | bar chart, series plot, data visualization, output delivery system | source | 16 |
| 234 | Making do with less: Emulating Dev/Test/Prod and Creating User Playpens in SAS Data Integration Studio and SAS Enterprise Guide | David Kratz | Have you ever required a Dev / Test / Prod environment but found yourself, for whatever reason, unable to lay down another SAS Installation? Have you ever discovered that your results have been overwritten by a team member? Our ability to use SAS is shaped by the environment in which the software is installed, but we often don't have as much control over that environment as we'd like. However, we can often emulate the setup we'd prefer by configuring the one we have. This paper explores this concept using techniques which can be applied to development in SAS Data Integration Studio and SAS Enterprise Guide | sas enterprise guide, libname statement, automatic macro variable | source | 8 |
| 235 | Managing and Analyzing Financial Risk on Big Data with High-Performance Risk and Visual Analytics | Cary Orange, Donald Erdman, Stacey M. Christian |
SAS High-Performance Risk is a distributed in-memory analysis engine for financial risk used to compute non- linear risk calculations such as Value at Risk (VaR). Traditional solutions for evaluating risk in a portfolio have suffered from obvious issues of scale. Millions of financial positions valued at thousands of simulated market states quickly yield billions of fine-grained results. Until now, reducing aggregation and reporting flexibility has been accepted as an unfortunate but necessary trade-off to obtain basic results. However, recent advances in both hardware and software for distributed computing make it practical to rethink the accepted architecture of risk solutions. This paper presents the fundamentally new approach to risk analytics used by SAS High-Performance Risk and SAS Visual Analytics | source | 10 | |
| 236 | Managing and Monitoring Statistical Models | Nathaniel Derby | Managing and monitoring statistical models can present formidable challenges when you have multiple models used by a team of analysts over time. How can you efficiently ensure that you're always getting the best results from your models? In this paper, we'll first examine these challenges and how they can affect your results. We'll then look into solutions to those challenges, including lifecycle management and performance monitoring. Finally, we'll look into implementing these solutions both with an in-house approach and with SASModel Manager. KEYWORDS: SAS, manage, monitor, statistical models. All data sets and SAS code in this paper are available athttp://nderby.org/docs/SGF190-2013.zip | proc logistic, logistic distribution, proc step, gain chart, confusion matrix, training set | source | 13 |
| 237 | Markov Chains and Zeros in My Data: Bayesian Approaches in SAS That Address Zero Inflation | Matthew Russell | There has been an increase in development over the past decade on methods that estimate count data. In recent releases of SAS/STAT software, a number of procedures that perform Bayesian methods have recently been incorporated. A common modeling problem across many disciplines is that of addressing zero-inflation or larger-than- expected proportions of zeros in count data. This challenge is exacerbated when count means and probabilities of structural zeros are heterogeneous. Zero-inflated models are commonly fit using maximum likelihood methods. This paper uses examples from the ecological literature to perform Bayesian analyses on discrete data with zero-inflation. We focus primarily on the PROC MCMC, but also address use of Bayesian methods in the FMM and GENMOD procedures. We fit zero-inflated count models under conditional binomial, Poisson, and negative binomial assumptions, and without random intercept effects on structural zero and/or stochastic count components |
binomial distribution, bayesian statistics, poisson distribution, markov chain, proc step, sample, zero-inflated poisson, regression analysis, poisson regression | source | 9 |
| 238 | Maximizing the Power of Hash Tables | David J. Corliss | Hash tables provide a powerful methodology for leveraging bid data by formatting an n -dimensional array with a single, simple key. This advancement has empowered SAS programmers to compile exponentially more missing data points than ever before, creating tables with hundreds of fields of all types in which the majority of data in this vast array is empty. However, the hash structure also supports analytics to calculate maximum likelihood estimates for missing values, leveraging extensive data resources available for each individual. An important application of this is in sentiment analysis, where social media text expresses likes or dislik es for particular products. Customer data, including sentiments for other products, are used to model sentiment where an individual's preference has not been made known. Keywords: Hash, Sentiment, Imputation | hash table, imputation, sample, likelihood function, missing value, lookup, proc step, regression analysis | source | 8 |
| 239 | Measuring Medication Adherence with Simple Drug Use and Medication Switching | Seth Traubenberg, Stacy Wang, Zhongwen Huang |
Medication adherence measures the extent to which a patient takes medications as prescribed by their health care provider. Published studies note that up to half of chronically ill patients do not adhere to their medication regimens. The financial impact of non-adherence is estimated at $100 billion annually due to avoidable hospitalizations with other health factors adding to that cost. To increase medication adherence and decrease overall healthcare spend, it is vital that health care providers be able to accurately measure adherence and identify potential gaps in care. In this paper, we will demonstrate SAS-based solutions which allow providers to calculate adherence across a range of prescribing patterns. The code provided allows PDC to be calculated at both the therapeutic class level and the patient disease level. Refining existing methodologies has increased the efficiency of the calculations | calendar year, group by | source | 9 |
| 240 | Medical Versus Pharmacy Insurance: Which Is More Cost-Effective for Providing the Prescription? Solving the Problem Via SAS Enterprise Guide. | Amber Schmitz | Data-driven decisions that provide strategic solutions. These are buzzwords and phrases we have all heard before, but actually applying those words to deliver actionable data is less commonplace than it should be. This paper explores how to use both pharmacy and medical insurance claims data in order to assess drug utilization behavior across the medical and pharmacy insurance benefits. The final result provides actionable data to assess moving drug fills to the more cost-effective insurance benefit. This paper explores: 1) Utilizing macro variables for efficient data pulls, 2) Using Tasks to create data sets, merge tables, and calculate variables, 3) Demonstrate business intelligence via graph options, and 4) Exploiting built-in tools for template organization and version control | sas enterprise guide, insurance claims, proc print, macro variable, sas enterprise guide project, lookup | source | 7 |
| 241 | Missing No More: Using the MCMC Procedure to Model Missing Data | Fang K. Chen | Missing data are often a problem in statistical modeling. The Bayesian paradigm offers a natural model- based solution for this problem by treating missing values as random variables and estimating their posterior distributions. This paper reviews the Bayesian approach and describes how the MCMC procedure implements it. Beginning with SAS/STAT 12.1, PROC MCMC automatically samples all missing values and incorporates them in the Markov chain for the parameters. Y ou can use PROC MCMC to handle various types of missing data, including data that are missing at random (MAR) and missing not at random (MNAR). PROC MCMC can also perform joint modeling of missing responses and covariates |
missing value, posterior probability, prior probability, normal distribution, proc step, likelihood function, response variable, treatment effect, sample, regression analysis, box plot, correlation | source | 23 |
| 242 | Modeling Change over Time: A SAS Macro for Latent Growth Curve Modeling | Pei-Chin Lu, Robert Pearson |
In recent years, latent growth curve (LGC) modeling has become one of the most promising statistical techniques for modeling longitudinal data. The CALIS procedure in SAS 9.3 could be used to fit an LGC model. As one application of structural equation modeling (SEM), LGC modeling relies on indices to evaluate model fit. However, it has been pointed out that when you are obtaining incremental fit indices, the default baseline model used in many popular SEM software packages, including PROC CALIS, is generally not appropriate for LGC models (Widaman and Thompson 2003). This paper illustrates the basic framework of an LGC model and introduces a SAS macro, %LGCM, that fits a latent growth model and computes incremental fit indices based on more appropriate baseline models |
covariance, proc step, proc calis, covariance structure, macro definition, longitudinal data, random intercept | source | 12 |
| 243 | Models for Ordinal Response Data | Robin High | Ordinal responses are commonly collected for data analysis, yet the computations of results possible with response data having ordered categories with SAS procedures are not well known. Although the cumulative logit model is readily available with several statistical procedures, other types of ordinal response models can also be evaluated through statements entered into the NLMIXED procedure. This paper introduces several models for ordinal response data including the partial proportional odds, adjacent logit, continuation ratio, and stereotype models along with the statements to estimate them with PROC NLMIXED. The process requires no restructuring of the input data set, as required with SAS procedures that can produce a subset of these models. Interpretation of ordinal logistic regression models depends on the coding of both the response and explanatory data and whether formats are applied |
odds ratio, predictor variable, proc step, proc logistic, logistic distribution, response variable, logistic regression, data science, estimate, likelihood function, regression analysis, proc nlmixed, data transformation, hazard ratio, maximum likelihood estimation, proc phreg, proc genmod, binomial distribution | source | 16 |
| 244 | Modern SAS Programming: Using SAS Grid Manager and SAS Enterprise Guide in a Global Pharmaceutical Environment | David Edwards, Gregory S. Barnes Nelson |
sas grid, sas enterprise guide, clinical research, clinical programming, data governance, latency, production run | source | 14 | |
| 245 | Monitoring 101: New Features in SAS 9.4 for Monitoring Your SAS Intelligence Platform | Amy Peters, Bob Bonham, Zhiyong Li |
Ever needed an alert on SASWORK storage usage at 80 percent? Or known when a SAS user account has been locked out due to failed login attempts? Or understand the memory and swap usage of a computer hosting the SAS Stored Process Server? What if you could see a complete listing of process resource consumption for all physical machines hosting a given SAS deployment? Learn about options for answering these questions, including new tools in SAS 9.4 that auto-discover software resources (including SAS servers and web application servers) of your platform for SAS Business Analytics. Discover how to use agents to collect metrics that reflect availability, performance, utilization, and throughput, giving you a more proactive understanding of the operational state of your SAS deployment | web service, access control | source | 8 |
| 246 | Multilevel Reweighted Regression Models to Estimate County-Level Racial Health Disparities Using PROC GLIMMIX | Lucy D'Agostino, Melody S. Goodman |
The agenda to reduce racial health disparities has been set primarily at the national and state levels. These levels may be too far removed from the individual level where health outcomes are realized, and this disconnect may be slowing the progress made in reducing these disparities. This paper focuses on establishing county-level prevalence estimates of diabetes among Non-Hispanic Whites and Non-Hispanic Blacks. These estimates are produced using multilevel reweighted regression models through the GLIMMIX procedure with 2006-2010 Behavioral Risk Factor Surveillance System data and 2010 census data. To examine whether racial disparities exist at the county level, the paper estimates the risk difference of prevalence estimates between races. It subsequently ranks counties and states by the magnitude of disparities |
regression analysis, proc glimmix, histogram, proc step, sample | source | 7 |
| 247 | n Ounce of Prevention Is Worth a Pound of Cure: How SAS Helps Prevent Financial Crime with an Analytical Approach to Customer Due Diligence | Scott Wilkins | The increasing regulatory expectations on the risk rating of high-risk clients and the emphasis on identification of foreign relationships with existing customers has driven financial institutions to enhance their Customer Due Diligence (CDD) processes. This paper outlines how organizations leverage SAS to deploy on-boarding and ongoing Customer Due Diligence programs. It explores analytical techniques for risk ranking customers, best practices for deploying these programs, as well as how the SAS approach incorporates a proactive, analytically driven triggering of new investigations based on detected customer events or behavior. SAS can provide organizations "The Power to Know" their customers and the risk they may represent to their financial institutions | customer due diligence, kyc | source | 12 |
| 248 | Need for Speed - Boost Performance in Data Processing with SAS/Access Interface to Oracle | Svein Erik Vralstad | Big Data is engulfing us. The expectations of users increase, and analytics is getting more and more advanced. Timely data and fast results have never had greater value. In data warehousing, analytics, data integration, and reporting, there is an ever-growing need for speed. When operating in environments where performance is of importance, it is of great value to fully understand the interaction between the different components of the environment. Hence, the importance of in-database execution is accelerating. To know when to let SAS process data, and when to use Oracle to perform the task is then of great value. This paper explores ways to achieve substantial gains in performance when processing (read, transform, calculate, and write) data in an effective manner. The audience for this paper is system developers and architects working with SAS in collaboration with data held in Oracle, where performance for at least one of the reasons stated above is of importance | proc sql, proc step, create table, sql pass-through, cpu time, select, sas function, libname statement, sas programmer, character functions, macro facility | source | 12 |
| 249 | Next-Generation Detection Engine for Fraud and Compliance | Ryan Schmiedl | SAS' next generation approach to fraud detection represents a pivotal shift in how financial institutions assess and govern customer risk. This paper discusses how companies can aggregate, sum and understand patterns in huge volumes of data; run more proactive what -if scenarios to identify and focus efforts on the most critical investigations ; and understand the impacts and oppor tunity costs across different detection methods | outlier, data validation rules, financial crime, link analysis, aml, machine learning, predictive analytics | source | 6 |
| 250 | Nifty Tips For Data Change Tracking | Julie A. Kilburn, Rebecca Ottesen |
Best practices for databases include keeping detailed audit trail information about the data. These audit trail tables vary in complexity as well as size. Generally speaking, the larger the database in tables (as well as in observations), the larger the audit trail. We have discovered that leveraging audit trail information in our automated reporting has been a huge resource saver in terms of which observations need to be reprocessed for a report. Even with minimal audit information (such as created by and modified by dates at the data table level), automation processing time can be greatly reduced by taking advantage of a new way of thinking and a few handy SAS functions | dataset, audit trail, macro definition, cpu time, batch job | source | 7 |
| 251 | Nontemporal ARIMA Models in Statistical Research | David J. Corliss | Mathematical models employing an autoregressive integrated moving average (ARIMA) have found very wide applications following work by Box and Jenkins in 1970, especially in time series analysis. ARIMA models have been very successful in financial forecasting, forming the basis of such things as predicting how much gas prices will rise. However, no mathematical requirement exists requiring the data to be a time series: only the use of equally spaced intervals for the independent varia ble is necessary. This can be done by binning data into standard ranges, such as income by $10,000 intervals. This paper reviews the fundamental statistical concepts of ARIMA models and applications of non -temporal ARIMA models in statistical research. Examples and applications are given in biostatistics, meteorology, and econometrics as well as astrostatistics. Keywords: ARIMA, Non-Temporal, Time Proxy, Mixed ARIMA Model | moving average, predictor variable, ma, time series analysis, proc reg, regression analysis, autoregressive model, sas base, proc step | source | 10 |
| 252 | Not Dividing by Zero: The Last of the Low-Hanging Efficiency Fruit | Bruce F. Gilsen | As SAS Institute has improved the efficiency of its code, some of the old ways for users to improve efficiency, such as using WHERE or WHERE= instead of IF in the DATA STEP, no longer make much difference. Current user efforts to improve efficiency tend to focus on more sophisticated techniques such as indexes or hashing. However, one of the classic methods, not dividing by zero in the DATA STEP, can still offer a large efficiency improvement. This paper shows some sample code that divides by zero and some benchmark results from changing the code to test for zero denominators to avoid dividing by zero. Efficiency in "the old days" and now Longtime SAS users can recall that there were some fairly easy ways to improve SAS code efficiency. Perhaps most common was to use a WHERE statement or WHERE= clause instead of a subsetting IF statement, which was extolled frequently in conference papers, including Gilsen (1999) |
missing value, if-then-else, subsetting if | source | 4 |
| 253 | On a First-Name Basis with SAS: Creating Personalized Error Messages Using SAS 9.2 | Andrew W. Clapson, Valerie Hastings |
In the interests of creating a user-friendly SAS system, it's often a good idea to include code that checks for common errors, notifies the user and suggests possible solutions. Apart from simply delivering this information to the user, the use of customized message windows is also a good opportunity to express congratulations upon a successful run or even deliver a light-hearted finger wagging in the case of unexpected errors. Using SAS 9.2, this paper will detail the steps necessary to include basic error messaging functionality in your SAS programs, covering both error notification and confirmation of successful program execution. In addition, through the use of system macro variables these feedback messages can surprise the user by 'knowing' their name and addressing them directly | macro variable, error message | source | 4 |
| 254 | Opinion Mining and Geo-positioning of Textual Feedback from Professional Drivers | Goutam Chakraborty, Mantosh Kumar Sarkar |
The widespread adoption of mobile applications has tremendously expanded the scope of obtaining timely customer feedback. While many companies collect feedback from their customers via mobile apps, often they restrict their analysis to numeric data and ignore analyzing customer feedbacks and sentiments from textual data because of perceived difficulties associated with analyzing text data. In this paper, we analyze customer feedbacks by professional drivers sent via a mobile app that the drivers use to locate a store, check their reward points etc. The drivers are customers of a retail & energy company which offers a variety of services such as gas stations and convenience stores and also offer amenities such as food from national restaurant chains, trucking supplies, showers and RV dump stations. The company experts currently manually classify these textual feedbacks into positive and negative groups | bubble plot, data cleaning | source | 11 |
| 255 | Optimize SAS/IML Software Codes for Big Data Simulation | Chao Huang, Goutam Chakraborty, Yu Fu |
Data volume keeps growing dramatically in the past decade . At a number of occasions, s imulation also creates large vectors and matrices. To speed up the processing of large data sets, vectorization is a popular optimization skill for many matrix languages, such as R, Matlab and SAS/IML. In this paper, a few examples in SAS/IML will be illustrated to introduce the vector-wise operation and other optimization strategies. Concerns regarding the computer memory are addressed to accommodate big vectors or matrices in SAS/IML. The result shows that optimized SAS/IML codes will significantly improve the computing efficiency and greatly simply the coding effort |
proc iml, proc step, do loop, memory usage, sum, choose | source |
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| 256 | Optimize Your Delete | Brad Richardson | Have you deleted a data set or two from a library that contains thousands of members using PROC DATASETS? If so, you probably have witnessed some wait time. To maximize performance, we have reinstated PROC DELETE as a procedure supported by SAS. One of the main differences between PROC DELETE deletion methods versus PROC DATASETS DELETE is that PROC DELETE does not need an in-memory directory to delete a data set. So what does this mean exactly? This paper will explain all |
proc step, proc delete, proc copy, cpu time, proc datasets, sas library | source | 7 |
| 257 | Ordinal Response Modeling with the LOGISTIC Procedure | Bob Derr | Logistic regression is most often used for modeling simple binary response data. Two modifications extend it to ordinal responses that have more than two levels: using multiple response functions to model the ordered behavior, and considering whether covariates have common slopes across response functions. This paper describes how you can use the LOGISTIC procedure to model ordinal responses. Before SAS/STAT 12.1, you could use cumulative logit response functions with proportional odds. In SAS/STAT 12.1, you can fit partial proportional odds models to ordinal responses. This paper also discusses methods of determining which covariates have proportional odds. The reader is assumed to be familiar with using PROC LOGISTIC for binary logistic regression |
logistic distribution, proc logistic, proc step, likelihood function, logistic regression, null hypothesis, maximum likelihood estimation, regression analysis | source | 20 |
| 258 | OUT= Is on the Way Out - Use ODS OUTPUT Instead | Stanley E. Fogleman | There are, as a general rule, two methods to create a SAS dataset from procedural output . The more traditional one is OUT=. This feature is being replaced by the ODS OUTPUT statement as new capabilities are added to procedures . In the future, only existing variables (generally pre 9.1) will be availa ble on the OUT= statement. Therefore, it behooves the day-to-day SAS programmer to become familiar with the new syntax |
ods output, output, dataset, out=, file statement | source | 4 |
| 259 | Parallel Multistart Nonlinear Optimization with PROC OPTMODEL | Edward P. Hughes, Tao Huang, Yan Xu |
Nonlinear optimization has many compelling applications, including finance, manufacturing, pricing, health care, telecommunications, engineering, and statistics. Often a nonlinear optimization problem has many locally optimal solutions, making it much more difficult to identify a globally optimal solution. That's why the multistart feature in PROC OPTMODEL selects a number of initial points and starts optimization from each one, significantly improving your chances of finding a global optimum. In SAS/OR 12.1, the multistart feature adds parallel execution. This paper explores the multistart feature and its parallel optimization feature, illustrating with examples drawn from research and industry |
sample, odds ratio, sas log, proc step | source | 12 |
| 260 | Patient Profile Graphs Using SAS | Sanjay N. Matange | Patient profiles provide detailed information for a specific subject participating in a study. The report includes relevant data for a subject that ca n help correlate adverse events, concomitant medications, and other significant events as a narrative or a visual report. This presentation cover s the creation of the graphs that are useful for visual reports from CDISC data. It includes a graph of the adv erse events by time and severity, graphs of concomitant medications, vital signs, and labs. All the graphs are plotted on a uniform timeline, so the adverse events can be correlated correctly with the concomitant medications, vital signs and labs. These graphs can be easily incorporated with the demographic and personal data of the individual patient in a report | adverse events, proc sgplot, concomitant medications, proc sgpanel, vital signs, adverse event severity, proc freq, class variable, cdisc | source | 14 |
| 261 | PC and UNIX SAS Reunited | Sarwanjeet Singh, Shiva Kalidindi |
Have you ever wondered how you can use the best of PC and UNIX SAS together and make a perfect world (well, almost perfect)? SAS/CONNECT allows you to use the Enhanced Editor and submit the code on UNIX. You can submit one DATA STEP or PROC at a time, view the log in the Log window as well as create data sets in the Work directory. It is a one-time setup, and you do not have to compromise the PC Enhanced Editor ever. This paper will provide you with step-by-step instructions on how you can connect and automate the PC-to-UNIX connection by using SAS. You will never have to leave the Enhanced Editor again |
sas log, proc step | source | 6 |
| 262 | Performance Predictability By Using Social Profile in Online P2P Lending Market | Harshal Darade, Siming Li, Zhangxi Lin |
Online peer-to-peer (P2P) lending markets enable individual consumers to borrow from, and lend money to, one another directly. We study the borrower-, loan- and group- related determinants of performance predictability in an online P2P lending market by conceptualizing financial and social strength to predict borrower rate and whether the loan would be timely paid. The result of our empirical study, conducted using a database of 9479 completed P2P transactions in calendar year 2007, provide support for the proposed conceptual model in this study. The results showed that combing financial files with social indicators can enhance the performance predictability in P2P lending market. Although social strength attributes do affect the borrower rate and status, their effects are very small in comparison to financial strength attributes | interest rate, regression analysis, calendar year, response variable | source |
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| 263 | Pi in the Sky: Building a Private SAS Cloud | Andrew Macfarlane, Frank Schneider |
In today's climate, cloud computing is a defacto term used in IT and cloud capability a mandatory requirement for all software vendors. Based on real life experience, this paper will discuss challenges, opportunities and options for developing and implementing a private SAS cloud using SAS 9.3 | sas grid, sas enterprise guide | source | 11 |
| 264 | Popular Tips and Tricks to Help You Use SAS Web Report Studio More Efficiently | Keith Myers | For more than six years, SAS Web Report Studio has enabled users at all skill levels to create, view, and explore centrally stored reports. This paper discusses tips and tricks for reporting techniques that have been most popular with customers over the years. The paper also explains new features that shipped with the second maintenance release of SAS Web Report Studio 4.31. As with the tips and tricks, these new features offer more efficient methods for tasks related to conditional highlighting and to content enhancement in reports that are sent via e-mail. The techniques and features that are discussed cover tasks in the following key areas: performance, filtering, scheduling, and distribution, report design, and sending reports via e-mail | txt, sample | source | 13 |
| 265 | Predicting Application Review Rating with SAS Text Miner | Jonghyun Kim, Tianxi Dong, Zhangxi Lin |
With the wide proliferation of text-based data on the Internet, there is a need for dealing with the information overload. The large amount of online user reviews may pres ent an obstacle to developers who want to know users' feedback and potential customers who are interested in applications . Here we employ text analysis provided in SAS Text Miner to predict the overall and feature -based ratings for online application reviews. We use exampl es from Android Market and Apple Store , the real world of online application stores . The findings may aid in promoting the sales of applications by better satisfying customer demands. Keywords: online reviews, application store, text mining, Android Market and Apple Store | data mining, proc freq | source |
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| 266 | Predicting Health Care Expenditures with the MCMC Procedure | Gregory Watson | Substantial variation, excess zeros, skew and extreme outliers make fitting and predicting health care expenditures rather difficult. This paper presents a Bayesian model that uses the first year of the fourteenth panel (2009-2010) of the nationally representative Medical Expenditures Panel Survey (MEPS) to predict health care expenditures for individuals in the second year. The merits of a Bayesian approach are examined and compared to classical alternatives. Implementation in the MCMC procedure is presented in detail, and model diagnostics and validation are discussed |
markov chain, posterior probability, gamma distribution, regression analysis, likelihood function, proc step, mcmc, health insurance, prior probability, standard deviation, monte carlo, bayesian statistics, normal distribution, probability distribution, sample, maximum likelihood estimation, estimate, correlation, covariance, proc mixed, logistic distribution | source | 12 |
| 267 | Predicting Women's Department Purchases in a Retail Store By Using the SEMMA Methodology | Michael Soto | As one of the main retail companies in Chile, we are currently facing interesting challenges. Nowadays customers have so many choices than ever to select what, where, how much and how to purchase. For this reason, it is crucial to leverage what technology has to offer in terms of analytics in order to improve value of average tickets and avoid inactivity of customers. For example, it is possible to know with a high degree of certainty which products offer to customers based on buying patterns to improve the cross -selling process. It is made through propensity predictive models, a set of mathematical and stat istical techniques, that allows to know what customers can buy in function of historical data. One of our focus to improve the business is the Women Department because it is currently our most powerful department in terms of transactions originated by cust omers | sample, data cleaning, proc hpsplit, factor analysis, data mining, logistic regression, logistic distribution, regression analysis, response variable | source | 12 |
| 268 | Predictive Modeling in Sports Leagues: An Application in the Indian Premier League | Goutam Chakraborty, Pankush Kalgotra, Ramesh Sharda |
The purpose of this article is to develop models that can help team selectors build talented teams with minimum possible spending. In this study, we build several predictive models for predicting the selection of a player in the Indian Premier League, a cricket league, based on each player's past performance. The models are developed using SAS Enterprise Miner 7.1. The best-performing model in the study is selected based on the validation data misclassification rate. The selected model provides us with the probability measure of the selection of each player, which can be used as a valuation factor in the bidding equation. The models that are developed can help decision- makers during auction set salaries for the players | proc princomp, data cleaning, validation set, neural network, proc hpsplit, training set, network optimization, response variable, regression analysis, polynomial regression, roc curve, sample | source | 10 |
| 269 | Presenting Business Cases That Contain Complex, Technical Information to a Varied Audience | Lori A. Guido, Stephen Moore |
The US Census Bureau has a SAS deployment of approximately 2600 active users, which the Software Application Branch (SADB) with in the Applications Services Division supports. Identification of appropriate SAS support resources is a difficult task considering the diverse use of SAS amongst the user community. We had to gather information, enlist help from many sources and get everyone involved in the effort on the same level of understanding with respect to the challenges faced, and agreement on a proposal to address the issues. This paper describes the method SADB used to justify the evolution of the Census SAS Support paradigm. This paper will focus on the following topics: Census's SAS support model Issue Identification Issue Definition/Leveling Communication Strategy | business intelligence, data mining | source |
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| 270 | Price and Cross-Price Elasticity Estimation Using SAS | Dawit Mulugeta, Jason Greenfield, Lisa Conley, Tison Bolen |
The relationship between price and demand (quantity) has been the subject of extensive studies across many product categories, regions, and stores. Elasticity estimates have also been used to improve pricing strategies and price optimization efforts, promotions, product offers, and various marketing programs. This presentation demonstrates how to compute item -level price and cross- price elasticity values for two products with and without promotions. We used the midpoint formula, the OLS linear model, and the log -log model to measure demand response to change in price using six -month transaction-level data. Limitations and prospects of the methods used are discussed. The inclusion of promotions and prices of other products as covariates provides a better understanding of the dynamics of price-demand relationships | price elasticity, estimate, regression analysis | source | 17 |
| 271 | PROC DATASETS: The Swiss Army Knife of SAS Procedures | Michael A. Raithel | This paper highlights many of the major capabilities of PROC DATASETS. It discusses how it can be used as a tool to update variable information in a SAS data set; provide information on data set and catalog contents; delete data sets, catalogs, and indexes; repair damaged SAS data sets; rename files; create and manage audit trails; add, delete, and modify passwords; add and delete integrity constraints; and more. The paper contains examples of the various uses of PROC DATASETS that programmers can cut and paste into their own programs as a starting point. After reading this paper, a SAS programmer will have practical knowledge of the many different facets of this important SAS procedure |
dataset, proc step, constraint, audit trail, run, key, libname statement, foreign key, sas library, proc datasets, modify statement, sas log, proc contents, sas programmer, proc sql, attrib statement, data integrity, proc append | source | 25 |
| 272 | Product Affinity Segmentation That Uses the Doughnut Clustering Approach | Darius S. Baer, Goutam Chakraborty |
Product affinity means natural liking of customers for products. Product affinity segmentation divides customers into groups based on purchased products. While conceptually appealing to marketers and business analysts, it is difficult to implement in practice. Use of product-level data often yields inappropriate solutions such as one large segment and many tiny segments. Standard transformations such as logarithms or quantiles have not solved this problem. In this paper, we demonstrate how a combination of Softmax transformations with a "Doughnut" clustering approach (single central cluster) results in more evenly-sized product affinity segments for 30,000 customers of a B2B (business-to-business) company. The affinity segments show meaningful differences in product buying patterns across the customer base, and can be used for identifying cross-selling and up-selling opportunities | product affinity, proc freq, standard deviation, proc step, do loop, proc fastclus, outlier | source | 17 |
| 273 | Programs? How to Process Your Inputs Faster | Jason Wachsmuth | The purpose of this paper is to demonstrate how to process multiple input files and routine programs with one click through the use of two macros. These macros combined with the %INCLUDE statement, CALL SYMPUT, and SCAN functions in a control program will eliminate updating various %LET statements, and physically opening, running, and closing each program. Most people who process data for routine operations will appreciate this solution |
macro variable, call symput, character functions, macro call, macro definition | source | 4 |
| 274 | Projecting Prison Populations with SAS Simulation Studio | Bahadir Aral, Emily Lada, Ginny Hevener, Jeff Day, Tamara Flinchum |
The majority of the states in the USA have a process to project prison populations for the purpose of planning adequate capacity. Typical time series methods are ineffective because they do not take into account factors like sentence length, prior criminal history, revocations of community supervision, and legislative changes. Discrete event simulation has proven to be a viable alternative. The North Carolina Sentencing and Policy Advisory Commission collaborated with SAS to build a model in SAS Simulation Studio that projects the number of prison beds needed for the next ten years. The model uses current prison population data, recent court convictions, revocations of community supervision, and estimates of growth to play out the admissions and releases of inmates over the time horizon of the model. The prison projections are updated by the Sentencing Commission on an annual basis | dataset, sample | source | 10 |
| 275 | Propensity Score-Based Analysis of Short-Term Complications in Patients with Lumbar Discectomy in the ACS-NSQIP Database | Yubo Gao | Lumbar discectomy is the most common spinal procedure performed, and can be done on an outpatient basis. In this study, we want to compare the incidence of complications in patients undergoing single level lumbar discectomy between the inpatient and outpatient settings, to determine baseline 30-day complication rates, and to identify independent risk factors for complications. To achieve those, patients undergoing lumbar discectomy between 2005 and 2010 were selected from the ACS NSQIP database, based on a single primary CPT code. Thirty-day post- operative complications and pre-operative patient characteristics were identified and compared. Propensity score matching and multivariate logistic regression analysis were used to adjust for selection bias and identify predictors of 30-day morbidity. All analyses are performed via SAS software | propensity score, proc step, regression analysis | source |
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| 276 | Pulling Data from the Banner Operational Data Store with SAS Enterprise Guide: Not Only Fast but Fun! | Claudia W. McCann | The assessment of learning and of services in higher education is crucial for continued improvement. Administrators and faculty, therefore, require data for their decision-making processes. There are many data input experts on campus and, unfortunately, far fewer who can extract the data in the aggregate form required by administrators, accreditors, and other institutional stakeholders. The SAS Enterprise Guide interface with the Banner Operational Data Store is a very powerful combination of softwares that enable the end user to quickly access the institution's data and produce reports. More powerful still is the ability to bring other relational databases, such as EXCEL spreadsheets, into the SAS Enterprise Guide environment, thereby allowing variables not available in the Operational Data Store to be used in comparative analyses |
sas enterprise guide, sas enterprise guide project, excel | source | 12 |
| 277 | Quantile Regression in Pharmaceutical Marketing Research | George Mu | In pharmaceutical marketing research, healthcare data heterogeneity presents a challenge to researchers. It is not always possible to identify a simple function that fits well for all individuals data points. Quantile Regression (QR) is an efficient and robust way to tease out the different patterns existing in the healthcare market. This paper demonstrates the value of applying QR to solve pharmaceutical marketing research problems. To illustrate the methodology, we use SAS QUANTREG procedures to compare physicians' new products uptake patterns. Quantile regression, when applied to the right studies, can distill more market insights from the same data sets | regression analysis, proc quantreg, estimate, predictor variable, proc reg | source | 4 |
| 278 | Quick and Easy Techniques for Fast Data Extraction | Chris Greni, Deepa Sarkar, Jason Yang, Mythili Rajamani |
Working with large amounts of data is a challenging, especially time consuming job. How can one reduce the amount of CPU usage to retrieve specific data? This paper tells some of the easy useful data extraction tips using SQL procedure. The following subjects are discussed in this paper: (1) Creating a temporary table with the Key Column in the database(both DB2, TERADATA) and extract the data from the database. (2) Extracting data only for specific day or specific week (3) Automating the date parameter for repetitive/scheduled tasks |
proc sql, proc step, select, extract, create table | source | 7 |
| 279 | Quick Hits - My Favorite SAS Tricks | Marje Fecht | Are you time-poor and code-heavy? It's easy to get into a rut with your SAS code and it can be time-consuming to spend your time learning and implementing improved techniques. This presentation is designed to share quick improvements that take 5 minutes to learn and about the same time to implement. The quick hits are applicable across versions of SAS and require only BASE SAS knowledge. Included are: - simple macro tricks - little known functions that get rid of messy coding - dynamic conditional logic - data summarization tips to reduce data and processing - generation data sets to impr ove data access and rollback | macro variable, macro function, sysdate9, proc step, proc sql, date variable, sas function, sas programmer, missing value, character functions, macro loop | source | 13 |
| 280 | Reading an Excel Spreadsheet with Cells Containing Line Endings | Larry Hoyle | The creative ways people enter data into EXCEL spreadsheets can cause problems when trying to import data into SAS data sets. This paper addresses the problem encountered when spreadsheet cells contain multiple lines (that is, the cells have embedded line endings). Several approaches to reading such data are described and compared |
excel, character variable, dataset, sas enterprise guide, macro variable, proc step, missing value, proc sql | source | 6 |
| 281 | Reading Data from Microsoft Word Documents: It's Easier Than You Might Think | John E. Bentley | SAS provides the capability of reading data from a Microsoft Word document, and it's easy once you know how to do it. Using the FILENAME statement with the DDE engine makes it unnecessary to export to EXCEL or work with an XML map. This paper goes through the steps of having SAS read a Word document and shares a an example that demonstrates how easy it is and how you can leverage the capability. All levels of SAS users may find this paper useful |
excel, filename statement, macro variable, txt, proc sql, input statement | source | 10 |
| 282 | Ready to Become Really Productive Using PROC SQL? | Sunil Kumar Gupta | Using PROC SQL, can you identify at least four ways to: select and create variables, create macro variables, create or modify table structure, and change table content ? Learn how to apply multiple PROC SQL programming options through task -based examples. This hands -on workshop reviews topics in table access, retrieval, structure and content, as well as creating macro variables. References are provided for key PROC SQL books, relevant webinars, podcasts as well as key SAS technical papers |
proc sql, macro variable, proc step, order by | source | 20 |
| 283 | Recovering SAS User Group Proceedings for the SAS Community | Kirk Paul Lafler, Lex Jansen, Richard W. La Valley |
Kirk Paul Lafler, Software Intelligence Corporation For many years, SAS User Groups held conferences which had printed only proceedings that were available only to those who attended or those who knew that copies existed in the SAS Library in Cary, NC. Over the past couple of years, there has been a project to digitize the printed proceedings of SAS User Groups International, SAS European Users Group International, North East SAS Users Group, SouthEast SAS Users Group, Western Users of SAS Software, SouthCentral SAS User Group, MidWest SAS Users Group, and the Pacific NorthWest SAS Users Group. This paper will provide an overview of the project and the progress that has been made on this effort | proc genmod, proc step | source |
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| 284 | Regression of NASCAR: Looking into Five Years of Jimmie Johnson | Alex Herrington, Liuchun Yang, Yun Gao |
In this paper, we investigate the winnings associated with different factors for NASCAR drivers. We want to predict the winnings that a driver can earn in a season given other, related factors, such as the number of races the driver competes in, the average finish position, or the make of car. We obtained 190 observations with 15 factors and randomly split the data into learning data and test data. Using the learning data set, we conducted multiple regression analyses to build a predictive model. Then we examined the final model with the test data set to see how well the model would work in the future. The model shows a high degree of accuracy in predicting the future | regression analysis, proc reg, simple linear regression, scatter plot, predictor variable, interaction effect, anova, homoscedasticity, multiple regression, durbin-watson, main effect, null hypothesis, histogram, response variable, outlier, correlation, backward selection, residual | source | 20 |
| 285 | Relate, Retain, and Remodel: Creating and Using Context-Sensitive Linguistic Features in Text Mining Models | Janardhana Punuru, Lane Surratt, Russell Albright |
Text mining models routinely represent each document with a vector of weighted term frequencies. This bag-of-words approach has many strengths, one of which is representing the document in a compact form that can be used by standard data mining tools. However, this approach loses most of the contextual information that is conveyed in the relationship of terms from the original document. This paper first teaches you how to write pattern-matching rules in SAS Enterprise Content Categorization and then shows you how to apply these patterns as a parsing step in SAS Text Miner. It also provides examples that improve on both supervised and unsupervised text mining models | sample, parsing, regression analysis, proc freq | source | 17 |
| 286 | Renovating Your SAS 9.3 ODS Output: Tools for Everything from Minor Remodeling to Extreme Makeovers | Bari Lawhorn | The SAS 9.3 HTML output that is generated with the ODS HTML statement and the new HTMLBLUE style looks great with the default settings. But realistically, there are always pieces of your output that you want to change. Sometimes you just need a little remodeling to get the results you want; other times, the desired changes call for an extreme makeover. This paper shows how you can use certain ODS statements (for example, ODS SELECT and ODS TEXT) and ODS statement options (for example, STYLE=) for minor remodeling. The paper also illustrates how to use the REGISTRY, TEMPLATE, and DOCUMENT procedures for a more extreme makeover. These makeovers apply to HTML as well as other ODS destinations |
proc step, sample, proc template, proc univariate, by group processing, select, sas output, output delivery system, proc tabulate, sas log, ods pdf, proc sgplot, macro variable, macro definition, sashelp, macro facility, dataset, format statement, proc format, ods html | source | 20 |
| 287 | Reordering Columns after PROC TRANSPOSE (or Anytime You Want, Really) | Sau Yiu | There are times when we want to rearrange the columns of a SAS data set in a particular order. This occurs most often after a PROC TRANSPOSE procedure, when the newly transposed columns do not appear in the order that we want. This paper shows how users can either sort the columns by their names, or put the columns in any custom order |
proc step, retain, dataset, proc sql, proc transpose, macro variable, variable list, proc contents, proc sort | source | 4 |
| 288 | Replacing Manual Coding of Customer Survey Comments with Text Mining: A Story of Discovery with Textual Data in the Public Sector | Jared Prins | A common approach to analyzing open-ended customer survey data is to manually assign codes to text observations. Basic descriptive statistics of the codes are then calculated. Subsequent reporting is an attempt to explain customer opinions numerically. While this approach provides numbers and percentages, it offers little insight. In fact, this method is tedious and time-consuming and can even misinform decision makers. As part of the Alberta Government's continual efforts to improve its responsiveness to the public, the Alberta Parks division transitioned from manual categorization of customer comments to a more automated method that uses SAS Text Miner. This switch allows for faster analysis of unstructured data, and results become more reliable through the consistent application of text mining | data mining, proc freq, parsing, unstructured data, dataset, proc tabulate, sample, proc step | source | 19 |
| 289 | Reporting Tips for No Observations | Wuong Jodi Auyuen | Good SAS application designs include reporting accurate information, delivering in a timely manner, meeting business needs, and presenting in an easily grasped manner. We design reports to meet these goals and hopefully to cover potential questions. One of the frequently asked questions is: I used to receive a session, say tourists from Japan, why I don't see that session for the week of March 14, 2011? Even though we don't need to code "No visitors from Japan due to Tsunami on March 11, 2011", we could at least provide a generic message like "No data returned for this session." so business users won't wonder whether they missed the page or question the accuracy of the development work | excel, proc step, proc sql, macro variable, macro definition | source | 6 |
| 290 | Resources for Getting the 2010 US Census Summary Files into SAS | Rebecca Ottesen | At first glance, accessing the 2010 US Census Summary Files data with SAS seems like a daunting task. The main limitation is that for the 2010 Summary Files it seems that the Census has gravitated toward supporting data access via Microsoft Access rather than SAS as they did in the past. However, there are several tactics that can be deployed to make accessing this data with SAS much easier. A thorough understanding of the Census Summary File data structure and documentation can be used to leverage both SAS code from programs that the Census previously supported and Census 2010 versioned SAS programming available through other public sources. Knowledge of the available resources can assist SAS analysts in taking advantage of this rich data set | raw data, dataset, sas programming, sample | source | 5 |
| 291 | RUN_MACRO Run! With PROC FCMP and the RUN_MACRO Function from SAS 9.2, Your SAS Programs Are All Grown Up | Dylan Ellis | When SAS first came into our life, it comprised but a DATA STEP and a few procedures. Then we trained our fledgling programs using %MACRO and %MEND statements, and they were able to follow scripted instructions. But with SAS 9.2 and 9.3, your macros are now wearing the clothes of a PROC FCMP function; you no longer need to feed every parameter with a spoon. These functions are independent programming units, and this paper shows how they can be put to use for handy calculations, standardizing and simplifying code, and adding dynamic new capabilities that may change the way you program |
proc fcmp, proc step, dictionary tables, macro variable, proc freq, sas function, call symput, standard deviation, macro call, proc sql, macro definition, macro function | source | 10 |
| 292 | Running SAS Programs Using Skype | Romain Miralles | Skype is a well -known method used to talk to friends, family members and co -workers. It is one of the best applications available to make voice calls over the Internet. In this paper we will present a new, innovative way to use SAS with Skype. Here, we have developed a solution that allows users to run SAS remotely through Skype. After installing the DLL from the API on the application website, programmers can c reate scripts to control Skype. By sending a specific message to a pre-defined user, programmers can execute SAS on demand. This paper will explain how to use Skype to run SAS programs. It will describe the installation and configuration of the DLL, provid e the VB script needed to communicate with Skype, and illustrate a real -case scenario in which this technique is used | clinical database, clinical trial | source | 6 |
| 293 | SAS 9.3 Administration Guilty Pleasures: A Few of My Favorite Things | Diane Hatcher | Over the evolution of SAS 9.3, SAS has continued to enhance and augment its administration capabilities. Most of these capabilities are well-known and welcome additions for SAS administrators, but there are some hidden jewels that you might not be aware of. This paper reveals some "guilty pleasures," features that make the SAS environment easier to manage and more robust, including stored process reports, metadata-bound libraries, the authdomain option for LIBNAME, and special tricks with metadata folders |
libname statement, sas management console, sas base, proc step, sas enterprise guide | source | 7 |
| 294 | SAS and Hadoop: The BIG Picture | Paul Kent | Presentation | libname statement, proc sql, proc means, group by, proc step, high-performance sas, input statement | source | 37 |
| 295 | SAS and the New Virtual Storage Systems | Margaret A. Crevar, Tony Brown |
Storage providers are offering a wave of new and advanced storage subsystems. These offerings promise virtualized, thin-provisioned, tiered, and intelligent storage that is easy to manage and will reduce costs. For many random and mixed workload applications, the promises deliver well and with good performance. Unfortunately, the SAS I/O workload profiles tend to violate some of the primary design assumptions underlying the configuration of these new systems. This paper addresses the SAS workload-specific issues that need to be considered when configuring these new storage systems for expected performance | dataset, data governance | source | 11 |
| 296 | SAS BI Dashboard: Interactive, Data-Driven Dashboard Applications Made Easy | Scott Sams | The latest release of SAS BI Dashboard, 4.31 M2, gives you powerful new functionality for designing "Dashboard Applications." Dashboard Applications are a set of interactive dashboards that present a data-driven story to the user. With 4.31 M2, dashboards can link to other dashboards and pass the current click context as parameters, guiding your users through their data with customized dashboard presentations. You can also integrate custom Stored Process, and have its generated data presented by a custom dashboard. This paper will show you several techniques for building Dashboard Applications, using key enhancements in SAS BI Dashboard 4.31 M2 | where statement, default parameter | source | 9 |
| 297 | SAS Certification: Understand the Benefits of SAS Certification, Which SAS Certifications Are Available, and What SAS Certification Can Do for You | Andrew Howell | SAS has long had certification available for its programming language and for its flagship data mining product, SAS Enterprise Miner. More recently with the rele ase of the SAS9 platform suite have come certifications in SAS Data Integration Studio, SAS Business Intelligence, and SAS Platform Administration. But what are the benefits of (and some of the differing opinions about) SAS Certification? What is available, and what's in it for organizations, their staff and for SAS consultants to become SAS Certified? | sas 9, data cleaning, data mining, sas programming | source | 9 |
| 298 | SAS Code to Make Excel Files Section 508 Compliant | Christopher J. Boniface, Hung Pham, Nora Szeto |
Can you create hundreds of great looking EXCEL tables all within SAS and make them all Section 508 compliant at the same time? This paper will examine how to use ODS TAGSETS EXCELXP and other BASE SAS features to create fantastic looking EXCEL worksheet tables that are all Section 508 compliant. This paper will demonstrate that there is no need for any outside intervention or pre or post-meddling with the EXCEL files to make them Section 508 compliant. We do all with simple BASE SAS code |
excel, sas base, proc format, proc step | source | 5 |
| 299 | SAS Data Integration Studio: The 30-Day Plan | John Heaton | Deciding whether to use an Extraction, Transformation and Loading tool on your Business Intelligence project or to use code is normally a much debated topic. Once this decision has been made and you have completed some form of training you should be ready to begin defining you jobs and extracting data into your solution, Correct? Well maybe not, you may want to spend a little time planning your next steps before you start to develop a large number of jobs. The paper outlines the typical steps needed to design your ETL architecture in order to be successful when implementing an ETL tool like SAS DI Studio | sas data integration studio, staging area, surrogate key, business intelligence, natural key, sas base, star schema, fact table, relational database | source | 12 |
| 300 | SAS Data Management Techniques: Cleaning and Transforming Data for Delivery of Analytic Data Sets | Christopher W. Schacherer | The analytic capabilities of SAS software are unparalleled. Similarly, the ability of the Output Delivery System to produce an endless array of creative, high-quality reporting solutions is the envy of many analysts using competing tools. But beneath all of the glitz and glitter is the dirty work of cleaning, managing, and transforming raw source data and reliably delivering analytic datasets that accurately reflect the processes being analyzed. Although a basic understanding of DATA STEP processing and PROC SQL is assumed, the present work provides examples of both basic data management techniques for profiling data as well as transformation techniques that are commonly utilized in the creation of analytic data products. Finally, examples of techniques for automating the generation and delivery of production-quality, enterprise-level datasets are provided |
missing value, proc step, input statement, proc sql, macro variable, character variable, user-defined format, data dictionary, substr function, raw data, data governance, proc format, date variable, data transformation, dataset, data cleaning, sysdate9, index, numeric variable, put statement, automatic variable, if-then-else, retain, proc freq, audit dataset, nominal variable, datetime variable, class variable, mdy, record count, sas programmer, macro facility, string functions, sas log, excel, datepart, proc printto | source | 38 |
| 301 | SAS Enterprise Business Intelligence Deployment Projects in the Federal Sector: Best Practices | Jennifer Parks | Systems engineering life cycles (SELC) in the federal sector embody a high level of complexity due to legislative mandates, agency policies, and contract specifications layered over industry best practices âall of which must be taken into consideration when designing and deploying a system release. Additional complexity stems from the unique nature of ad-hoc predictive analytic systems that are at odds with traditional, unidirectional federal production software deployments to which many federal sector project managers have grown accustomed. This paper offers a high-level roadmap for successful SAS EBI design and deployment projects within the federal sector. It's addressed primarily to project managers and SAS administrators engaged in the SELC process for a SAS EBI system release | data governance, data validation rules, sas administrator, predictive analytics, sas enterprise guide | source | 13 |
| 302 | SAS Enterprise Guide - Implementation Hints and Techniques for Insuring Success with Traditional SAS Programmers | Roger D. Muller | There are many configuration options available in SAS Enterprise Guide for both the product itself and the included enhanced editor. There are also numerous software products from SAS that may or may not be licensed at your site and greatly affect your workflow. Workflow options while developing the code are numerous and range from simple line-by-line execution up to and including the running of an entire process flow or project. Storage of SAS code under development also deserves careful thought. All of these topics and more are addressed to enable users to have a very thorough non-frustrating first-time experience with SAS Enterprise Guide. The presentation is aimed at users who have experience coding and running SAS programs. This presentation is a video presentation created using SAS Enterprise Guide 5.1 and SAS 9.3 | sas enterprise guide, sas programmer, proc step, proc freq | source | 11 |
| 303 | SAS Enterprise Guide 5.1: A Powerful Environment for Programmers, Too! | Marje Fecht, Rupinder Dhillon |
Have you been programming in SAS for a while and just aren't sure how SAS Enterprise Guide can help you? This presentation demonstrates how SAS programmers can use SAS Enterprise Guide 5.1 as their primary interface to SAS, while maintaining the flexibility of writing their own customized code. We explore: navigating and customizing the SAS Enterprise Guide environment using SAS Enterprise Guide to access existing programs and enhance processing exploiting the enhanced development environment including syntax completion and built-in function help using Code Analyzer, Report Builder, and Document Builder adding Project Parameters to generalize the usability of programs and processes leveraging built-in capabilities available in SAS Enterpri se Guide to further enhance the information you deliver. Audience: SAS users who understand the basics of SAS programming and want to learn how to use SAS Enterprise Guide | sas enterprise guide, bar chart, sas programmer, proc step, macro variable, excel, dataset, sas log, sashelp, work library, missing value, temporary dataset | source | 32 |
| 304 | SAS Enterprise Guide Best of Both Worlds - Is it Right for You? | Sunil Kumar Gupta | Whether you are new to SAS or a seasoned SAS Programmer, you still face the same dilemma. Does SAS Enterprise Guide represent the best of both worlds to make the transition to SAS easier with a point -n-click interface or enhance your productivity with over 90 tasks? Do you follow the same traditional path taken by millions who learned SAS many decades ago or do you take the yellow brick road to directly analyze your data? This presentation explores the vast differences between these two cultures and how they impact your programming environment. While there are numerous benefits to using SAS Enterprise Guide, there are also some caveats to keep in mind to make the transition smoother | sas enterprise guide, sas programmer, dataset, numeric variable, proc step, proc report, sas log | source | 9 |
| 305 | SAS Enterprise Guide: More Than a Gift from Outer Space | Patricia S. Aanderud | SAS Enterprise Guide seem alien to you? Let's walk through the many SAS Enterprise Guide features using some UFO sightings data. During the presentation, you will walk through some basics, learn how to change the advanced options, and explore some newer features of SAS Enterprise Guide | sas enterprise guide, excel, proc sql | source | 9 |
| 306 | SAS Enterprise Guide: What's in It for the Long-Term Highly Experienced SAS Programmer | Roger D. Muller | What are the benefits of SAS Enterprise Guide as the developmental platform for highly experienced SAS programmers who have been writing code for a long time? This paper demonstrates a number of features that are available in SAS Enterprise Guide for not only programming, but viewing SAS data sets, creating multiple report outputs, improving code storage, providing project organization and management, and more. The techniques will emphasize the importance of the work flows in SAS Enterprise Guide. All of these are in a state-of-the-art Microsoft Windows environment with full copy, cut, and paste capabilities. This presentation will focus more on the benefit to the programmer rather than on the feature itself. The bottom line will be "What is in it for me?" This presentation is a video presentation created using SAS Enterprise Guide 5.1 and SAS 9.3 | sas enterprise guide, sas programmer, sas programming, data cleaning, proc sql | source | 7 |
| 307 | SAS Essentials: Maximize the Efficiency of Your Most Basic Users | Julie Kezik, Melissa Hill |
If programming and research assistants were taught SAS essentials, job efficiency could be maximized with the ability to use SAS as a tool to do their own preparatory work for assigned tasks. This paper summarizes a supplemental training program which teaches basic SAS programming skills to enable support staff to be more independent | proc step, by group processing, proc print, data step, proc contents, proc freq, dataset, sas output, proc sort, proc means | source |
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| 308 | SAS Grid Job Submission and Monitoring from the SAS Information Delivery Portal | Adolfo Lopez | As part of the implementation of SAS Grid Computing at Valence Health, w e realized that users would require a simple and straightforward way to batch submit SAS programs to the grid from their desktops. While the SAS Grid Manager Client Utility provides this functionality it requires that additional software be installed on the client computer and that the user is comfortable with a command line interface. In order to save time and effort we decided to provide users with the ability to batch submit jobs to the grid and monitor them via the SAS Information Delivery Portal. This method provided the required functionality with minimal work and significantly reduce d the maintenance required to ensure the delivered solution met the needs of the majority of our users | grid computing, business intelligence | source |
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| 309 | SAS Metadata Reporting: Extracting Invaluable Information from SAS Metadata | Jugdish Mistry | Within the past few releases of SAS Software; there has been a trend in fact a big emphasis to use and move to using more and more metadata. It is now, the one stop place for SAS configuration, SAS DI/BI, most SAS applications, and GRID developments. This wonderful method of storing data and managing SAS has no nice GUI for getting this information out. So if we wanted a u ser list, the name of the last person to update a DI flow, or list new jobs created in the past week, we have to use the appropriate GUI interface for interrogating and reporting on this information. This paper provides practical examples and discusses h ow using SAS one could extract and generate useful reports from metadata. This paper provides practical examples and discusses various methods of extracting SAS metadata from the SAS Metadata Server to generate useful reports | proc step, sas base, sas data integration studio | source | 9 |
| 310 | SAS ODS Graphics Designer - The Next Step in Amazing Data Visualization | Christopher Battiston | Admit it. You are swamped, overwhelmed, and desperate to find more efficient ways of doing things. But who has the time to learn something new? This poster won 't be able to help for the majority of these issues. It will help you become a more effective and efficient data visualization expert, freeing up at least enough of your time to get a sandwich (and maybe even eat it). SAS ODS Graphics Designer is highlighted, showing various examples with a generic step-by-step approach. Not as basic as Graph -N-Go and not nearly as complex as SAS Enterprise Guide, ODS Graphics Designer is a tool that appeals to both novice and expert users |
scatter plot, survival analysis, series plot, bar chart, box plot, proc step, sas enterprise guide, proc template, histogram, proc sql | source |
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| 311 | SAS Release Management and Version Control | John Heaton | Release Management is the process of managing multiple different software releases between environments in a controlled, auditable and repeatable process. This paper looks at the capabilities of the SAS 9.3 toolset to build an effective Release Management process within your organization to support the migration and maintenance of SAS code through your product lifecycle | sas data integration studio, sas enterprise guide | source | 28 |
| 312 | SAS Skill Learning and Certification Preparation in a Graduate School Setting | Christine Bonney, Michael Keith |
A semester-long course was created with the goal of teaching graduate students SAS programming skills and to prepare them to take the SAS Base Programming for SAS9 exam. Course activities and materials include: weekly lectures; in-class labs; take-home problem sets; virtual (online) labs; assigned readings from the SAS Certification Prep Guide: Base Programming for SAS9; midterm and final exams; and access to SAS OnDemand for Academics. This paper covers the details of the course development and design, as well as preliminary results from the course and plans for future developments | ods html, sas programming, proc step | source | 7 |
| 313 | SAS Stored Processes Are Goin? Mobile!: Creating and Delivering Mobile-Enabled Versions of Stored Process Reports | Michael Drutar | ods html, proc step, stored procedure, proc print, sas output | source | 10 | |
| 314 | SAS Stored Processes Logging | Bhargav Achanta | You and your colleagues work very hard to create stored processes and deliver them to various departments in your organization to review the reports on regular basis and on time. Have you ever wondered how many of the reports you provide to the audience are actually being used? This paper presents a neat way to identify who ran the stored processes and what time they have run the stored processes by scanning all the stored process server log files and generates a list report and a frequency report | proc freq, macro variable, sample, proc sql | source |
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| 315 | SAS Tools for Transparent and Reproducible Research: Medication History Estimator | Brian Sauer, Tao He |
The Medication History Estimator (MHE) is designed to output data at the course -level; i.e., one row of data per drug course. Course and period proportion of days covered (PDC) are calculated for each medication. Reports that describe the frequency and percent of users for each medication product, average duration of medication courses, medication possession ratios and Kaplan -Meier based persistency curves are automatically generated and formatted for professional reports and journal publicat ions | macro definition, standard deviation | source | 10 |
| 316 | SAS Treatments: One to One Marketing with Customized Treatment Processes | Amy Glassman, Dave Gribbin |
The importance of sending the right message to the right person at the right time has never been more relevant than in today's cluttered marketing environment. SAS Marketing Automation easily handles segment level messaging with out-of-the-box' functionality. But how do you send the right message and the appropriately valued offers to the right person? And, how can an organization efficiently manage many treatment versions across distinct campaigns? This paper presents case studies of companies across different industries that send highly personalized communications and offers to their clientele using SAS Marketing Automation. It describes how treatments are applied at a segment and a one-to-one level. It outlines a simple custom process that streamlines versioning treatments for reuse in multiple campaigns | master data management, dataset | source | 14 |
| 317 | SAS Virtual Desktop Deployment at the U.S. Bureau of the Census | Lori A. Guido, Michael Bretz, Stephen Moore |
The United States (US) Census Bureau supports a SAS user base of approximately 2600 users. In the past this required the deployment of many SAS client solutions on individual desktops. Using new deployment strategies, Census reduced service delivery time while increasing installation quality and standardization. Census investigated implementation options of an enterprise-wide virtual desktop solution for use by the SAS user community. Client Virtualization is a technique for simplifying the configuration, deployment and support of the end- user computers needed by an organization. Client virtualization looks at changing the one end-user to one desktop computer paradigm for SAS software installation and finding ways of reducing the administrative complexities associated with the one end user desktop computer while gaining operational efficiencies and a more robust deployment model | sas enterprise guide, excel, stored macro, data mining, macro catalog | source | 9 |
| 318 | SAS Visual Analytics (SASVA) and SAS High-Performance Analytics Server (SASHPAS) - Network Considerations and Data Management/Governance | Nicholson E. Warman | SAS Visual Analytics (SAS VA) is an entirely new approach to information analysis and management of large volumes of data, using an in-memory analytics engine to provide lightning-fast responses to analytical questions. With this new class of products come opportunities for your organization, but to make the most of these opportunities, there is a need for planned choices on data networking, data provisioning strategies, plus the need for the appropriate level of computing power, all in accordance with your corporate information governance policies and practices. As you soon realize, this isn't just Foundation SAS anymore, but a rich analytical environment that is able to rapidly analyze large amounts of data | sas visual analytics, traffic data | source | 9 |
| 319 | SAS-Oracle Options and Efficiency: What You Don't Know Can Hurt You | John E. Bentley | SAS/ACCESS engines allow us to read, write, and alter almost any relational database. Using the engine right out of the box works OK, but there are a host of options and techniques that if properly used can improve performance, sometimes greatly. In some cases though an incorrect option value can degrade performance and you may not even know it. This paper will review SAS/ACCESS for Oracle engine options that can impact performance. Although the focus is on Oracle, most of the options reviewed are cross-database. All levels of SAS programmers, Enterprise Guide users, and Oracle and non-Oracle database users should find the paper useful |
sas enterprise guide, oracle | source | 17 |
| 320 | SASY Codes for Lazy people | Prashanthi Selvakumar | "I choose a lazy person to do a hard job, because a lazy person will find an easy way to do it." - Bill Gates. Everyone wants to save time. While hard work is useful, smart work is a pre requisite. Are you tired of typing codes? Read this paper, it gives you the ways to shorten your codes. The topics discussed in this paper include, array, do loops, macros, functions. It also discusses the procedures and data steps where macros can save your time. The other techniques like, combining the macros while creating a html, pdf, rtf output, to produce professional reports. The possible ways of saving time in programming are addressed in this paper | character variable, proc freq, proc step | source | 5 |
| 321 | SCAD: Development of Statistical Information Systems for the Provision of Census Data | Greg Pole | The Statistics Centre - Abu Dhabi (SCAD) was founded in 2008 and seeks to join the world's leading statistical organisations in statistical collection, production , and dissemination. In October 2011, SCAD conducted its first census of population and households. In addition to using innovative enumeration technologies (e.g. iPads), SCAD is also advancing the development of inventive and flexible tools for accessing rich census data. This is a positive shift towards greater access to public data in the Emirate. The tools SCAD has developed for the 2011 Census use SAS as a foundation and include: on-line Thematic Mapping, on-line Community Tables, and on-line Table Builder. These tools will be released to the Abu Dhabi government and public in 2012 and 2013 as web-based applications | web service, oracle, excel | source | 18 |
| 322 | Segmentation and Classification Analysis Using SAS | Rachel Poulsen | An idiom in the customer service industry is "the customer is always right". However, in many instances the customer will not speak up and another popular idiom must be used "Actions speak louder than words". Customer actions can be measured to infer what they will not say. Once measured, segmentation analysis can be used to make sense of the large amount of behavioral data by placing customers into various segments. Classification models are then used to assign new customers to a segment. Statistical algorithms used to segment and classify observations include Collaborative Filtering and Machine Learning Models. This paper will illustrate how SAS can be used to segment and classify observations using the FASTCLUS and DISCRIM procedures | cluster analysis, proc step, proc princomp, proc discrim, hierarchical clustering, training set, data cleaning, proc cluster, outlier, sample, proc fastclus, multivariate analysis, proc freq, discriminant analysis, machine learning, uniform distribution, group by | source | 14 |
| 323 | Self-Service Data Management: Visual Data Builder | Malcolm Alexander, Sam Atassi |
Successful data preparation is the key to extracting meaningful knowledge from data. SAS Visual Data Builder allows you to access data from enterprise sources and transform it for use in business intelligence, data visualization, and data mining tasks. This paper discusses self-service data management techniques available using SAS Visual Data Builder as well as its unique features enabling it to load data into SAS LASRTM Analytic Server | big data, excel, proc sql | source | 16 |
| 324 | Set Yourself Free - Use ODS Report Writing Technology in SAS Enterprise Guide Instead of Dynamic Data Exchange in PC SAS Part II SAS Code Revealed | Robert Springborn | The ability to prepare custom designed reports and convey your message in a clear and concise manner is very important in today's sophisticated business environment. Easy to read clearly designed reports can enhance your organization's credibility and reputation. Traditional use of Dynamic Data Exchange (DDE) in PC SAS to produce custom designed reports is the result of widespread and popular use of Microsoft EXCEL. However with most business organizations transitioning to SAS Enterprise Business Intelligence (EBI), where DDE does not function, ODS Report Writing technology is a powerful alternative to create custom designed reports in SAS Enterprise Guide. The driving force for this topic was the need to create hospital level data discrepancy reports which compare California CABG Outcome Reporting Program (CCORP) clinical data, to OSHPD's hospital administrative data, to verify risk factors used in a risk-adjusted operative mortality model |
excel, sas enterprise guide, dataset, business intelligence, clinical data, output delivery system, macro definition | source | 21 |
| 325 | Sharpening Your Skills in Reshaping Data: PROC TRANSPOSE vs. Array Processing | Arthur X. Li | A common data managing task for SAS programmers is transposing data. One of the reasons for performing data transformation is that different statistical procedures require different data shapes. In SAS, two commonly used methods for transposing data are using either the TRANPOSE procedure or array processing in the DATA STEP. Using PROC TRANSPOSE mainly requires grasping the syntax and recognizing how to apply different statements and options in PROC TRANSPOSE to different types of data transposition. On the other hand, utilizing array processing in the DATA STEP requires programmers to understand how the DATA STEP processes data during the DATA STEP execution. In this talk, these two methods will be reviewed and compared through various examples |
proc transpose, array, proc step, by group processing, do loop | source | 15 |
| 326 | Short-Term Costs of Smoking during Pregnancy: A Geometric Multidimensional Approach | Violeta Balinskaite | Smoking during pregnancy imposes a considerable economic burden on society. This phenomenon has been studied fairly extensively in the United States, but little is known about its costs within the European Union. This paper aims to evaluate the neonatal costs of a mother who smokes during pregnancy compared to the alternative of her not smoking . Geometric multidimensional approach that is used for analysis involves the use of conditional multiple correspondence analysis as a tool for investigati ng the dependence relationship between covariates and the assignment-to-treatment indicator variable within a strategy whose final aim is to find balanced groups | treatment effect, cluster analysis, multiple correspondence analysis, sample, proc corresp, binary variable, macro definition | source | 11 |
| 327 | Smarter Grid Operations with SAS/OR | Arnie de Castro, Greg Link |
Between the time electricity leaves the utility generators and reaches your home or business, 7% of the energy has been dissipated as heat. For the average utility, this represents a total loss of more than $75 million each year. Some of the electric current producing these losses do not result in the actual production of power, and can be minimized with the proper switching of devices that are located at strategic points in the distribution system. These devices can also conserve energy by reducing voltages in the distribution system and still provide a continuous supply of electricity to the customer. This paper discusses the use of SAS/OR to control device switching to optimize the operations of the electrical distribution system |
odds ratio, proc step, response variable | source | 12 |
| 328 | So Many Films, So Little Time | Lisa Eckler | The Toronto International Film Festival ("TIFF") is an annual event, screening a huge variety of new films for the international film industry as well as the general public. The number of choices means selecting which films to order tickets for can be overwhelming. I suffer the occupational hazard of considering every logic problem in terms of SAS code. Here we explore how to use some very simpl e code to explore scheduling options, which will support decision -making with the goal of seeing the most films from a priority list in the most enjoyable way. While many of us use SAS for efficiency in our work, this is a small example of how it can also be beneficial for personal time | excel, proc freq, proc sql | source |
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| 329 | So You're Still Not Using PROC REPORT. Why Not? | Daphne E. Ewing, Ray Pass |
Everyone who can spell SAS knows how to use PROC PRINT. Its primary use may be as a development tool to help in debugging a long multi-step program, or as a simple report generator when all that is really needed is a quick look at the data, or even a basic low-level finished report. However, if a report generation/information delivery tool with powerful formatting, summarizing and analysis features is called for, then PROC REPORT is the solution. PROC REPORT can provide the standard PROC PRINT functionality, but in addition, can easily perform many of the tasks that you would otherwise have to use the SORT, MEANS, FREQ and TABULATE procedures to accomplish. PROC REPORT is part of the Base SAS product, can run in both an interactive screen-painting mode or a batch mode, and should be the basic tool of choice when there is a need to produce powerful and productive reports from SAS data sets |
proc report, proc step, class variable, character variable, proc print, proc means, proc tabulate | source | 14 |
| 330 | Sobeys and SAS - How Do You Talk to Your Customer? | Ashok Setty, Wanda Shive |
For years, retailers have struggled to measure the effectiveness of their promotional advertising efforts. Harnessing the big data within their customer and transaction files continues to be a major challenge. Approaches for gleaning true customer insights from that data are becoming more common. Measuring total shopping behavior in conjunction with specific promotions provides a better understanding of the overall impact on profitability. This paper describes how retailers are utilizing customer analytics to measure the effect that mass promotions have on the total basket spend of customers and to identify the most relevant offers for each individual customer | data validation rules, subscriber analytics | source | 5 |
| 331 | Some Techniques for Integrating SAS Output with Microsoft Excel Using Base SAS | Vincent DelGobbo | This paper explains some techniques to integrate your SAS output with Microsoft EXCEL. The techniques that are presented in this paper require Base SAS9.1.3 SP4 and above, and can be used regardless of the platform on which SAS is installed. You can even use them on a mainframe! Creating and delivering your workbooks on- demand and in real time using SAS server technology is discussed. Although the title is similar to previous papers by this author, this paper contains new and revised material not previously presented |
excel, xls, sas base, proc export, sas output, proc print, title statement, proc step, dataset, sas log, sample, ods html, sas 9, format statement, output delivery system, by group processing | source | 19 |
| 332 | Some Useful Utilities on UNIX Platform | Kevin Chung | While using SAS on Unix platform, one might want to quickly brow se data; obtain contents; or perform a frequency on various data fields within a SAS data set. You can alwa ys write a specific one use SAS program and submit the program to get the results you need. However we are able to obtain this information in more efficient and effective manner by using the Unix shell script along with SAS code s. This paper demonstrates some useful utilities in Unix. Because of the flexibility of this Unix shelling structure, a user will both save time and increase productivity | dataset, variable list, proc freq, excel | source | 12 |
| 333 | Something for Nothing? Adding Group Descriptive Statistics Using PROC SQL Subqueries | Sunil Kumar Gupta | Can you actually get something for nothing? With PROC SQL's subquery and remerging features, yes, you can. When working with categorical variables, o ften there is a need to add group descriptive statistics such as group counts, minimum and maximum values for further by -group processing. Instead of first creating the group count, minimum or maximum values and then merging the summarized dataset to the original dataset, why not take advantage of PROC SQL to complete two steps in one? With PROC SQL's subque ry and summary functions by the group variable, you can easily remerge the new group descriptive statistics back to the original dataset |
proc sql, proc means, proc step, group by, class variable, create table, by group processing, numeric variable | source | 8 |
| 334 | Speed it Up: Using SAS to Automate Initial Discovery Practices | Christine John, Mariya Karimova |
Healthcare investigations frequently begin with a tip containing very little provider information. This presentation attempts to use SAS to automate the initial discovery process, turning a name into a full overview of the provider. Multiple data sources are combined, which oftentimes require fuzzy matching to resolve conflicting identifiers. The program utilizes INFILE URL and SAS text functions to obtain meaningful information from various websites. It further utilizes SAS ODS and SAS/GRAPH to create a single standard PDF report; which provides a visualization of provider billing patterns, summarizes their affiliations, and embeds hyperlinks to original web-based resources. Additional topics that are discussed include: creating a script for multiple users, paramaterization, utilizing system variables, and SAS 9.2 to SAS 9.3 conversion |
proc step, proc report, macro variable, ods pdf, sample, excel | source |
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| 335 | Statistical Analyses Using SAS Enterprise Guide | Scott P. Leslie | Conducting statistical analyses involves choosing proper methods, understanding model assumptions and displaying clear results. The latest releases of SAS Enterprise Guide offer conveniences, such as point -and-click wizards and integrated syntax help, to ease the burden on users. This tutorial demonstrates how to perform statistics quickly and easily using some handy features of SAS Enterprise Guide. Examples of multiple linear regression, logistic regression and survival analysis are covered as well as some hints on how to navigate Enterprise Guide menus. This tutorial is intended for SAS users with beginning to intermediate experience with the above mentioned statistics and basic experience with SAS Enterprise Guide | sas enterprise guide, proc reg, regression analysis, survival analysis, logistic regression, sashelp, logistic distribution, data science, proc step, predictor variable | source | 11 |
| 336 | Statistical Graphics for Clinical Research Using ODS Graphics Designer | Wei Cheng | Statistical graphics play an important role across various stages in clinical research. They help investigators to explore and understand raw data in the early stage of statistical analysis, as well as present final analysis result in the formal publications. The graphs need to be specifically designed and carefully drawn to best represent data and analysis. While this can be done by SAS programming using traditional SAS DATA steps and SAS/GRAPH procedures, the process is time consuming and time is spent to find the right options or annotation syntax. Since SAS 9.2M3, ODS Graphics Designer (Designer) becomes production software. This is an interactive "point and click" application that can be used to design and create custom graphs. This new application greatly enhances the ability to effectively generate statistical graphs for clinical research |
bar chart, vertical bar chart, proc freq, clinical research, treatment period, treatment arm, clinical trial, scatter plot, sas syntax | source | 12 |
| 337 | Stealing the Admin's Thunder: SAS Macros to Interact with the UNIX OS from within SAS Enterprise Guide | Thomas Kunselman | For SAS users who are unfamiliar with the UNIX environment, simple tasks like copying, renaming, or changing the permission settings on a file can be very non-intuitive. Many of these tasks are not even possible through the SAS Enterprise Guide Server List Window. This paper will present several SAS macros that can be used to: view and kill UNIX host processes; display, compare, and manage folders and files, including copying subfolders and changing permissions and owners; display and set default file system permissions for new objects. Please note that for these macros to work, the X command must be allowed on the SAS server | sas enterprise guide, elapsed time, macro definition | source | 12 |
| 338 | Stop your 'Wine?-ing: Use a Stored Process! | Angela M. Hall, Patricia S. Aanderud |
One of the major benefits of using SAS Stored Processes is extendibility. SAS stored processes are one of the most customizable products; there are several advantages, such as the ability to set up reports that can run in various locations, enhance out-of-the box functionality with custom widgets, and all the while leveraging server options and power. In this discussion, you will learn tips and tricks for using stored processes within SAS BI clients | sas enterprise guide, dataset, proc step, proc sql, where statement, macro variable, sample | source | 10 |
| 339 | Submitting SAS Code On The Side | Richard D. Langston | This paper explains the new DOSUBL function and how it can submit SAS code to run "on the side" while your DATA STEP is still running. It also explains how this function differs from invoking CALL EXECUTE or invoking the RUN_COMPILE function of FCMP. Several examples are shown that introduce new ways of writing SAS code |
macro variable, proc step, proc sql, call execute, dosubl, data step, macro definition | source | 6 |
| 340 | Take Home the ODS Crown Jewels: Master the New Production Features of ODS LAYOUT and Report Writing Interface Techniques | Daniel O'Connor | The ODS "crown jewels" are the most sought-after features that allow the customer to create sophisticated documents that can readily be published via print, Web, or mobile devices. Let's journey with SAS 9.4 as we explore the new enhancements in this production release of ODS Layout and Report Writing Interface. This example-driven content is intended to empower and captivate the novice ODS customer while challenging even the most advanced |
proc step, output delivery system, file statement, footnote statement, reporting, class variable, by group processing, options statement | source | 20 |
| 341 | Taking Full Advantage of sasCommunity.org: Your SAS Site | Arthur L. Carpenter, Donald J. Henderson |
sasCommunity.org is becoming a hub or clearinghouse for technical information related to the use of SAS software. The site is managed and run by SAS users for SAS users. It is free and open to all SAS users to browse. Any SAS user can contribute to the site - just create an ID in order to contribute new content or to expand upon existing content. sasCommunity.org is built on top of the same software as Wikipedia. So if you know how to use Wikipedia, you have a head start on using sasCommunity.org. This workshop will enable attendees to: Learn how to make use of this site Learn how to navigate the information contained on the site. Learn how to discover the wealth of its hidden treasures. Find out that there are a number of ways you can contribute, and discover how you too can quickly make a difference in this world-wide community of SAS users. Learn how even small contributions can enhance the site for everyone | macro function, macro facility | source | 10 |
| 342 | Targeting Public Value in New Zealand | Michael O'Neil | In support of Government initiatives to deliver better social, economic and fiscal outcomes for all New Zealanders, The Ministry of Social Development is implementing what has been called the "Investment-based Approach" that aims to improve social sector performance through better targeting. Social and fiscal outcomes can be better achieved through smart targeting using client centred evidence to inform strategic and case level targeted decisions. This paper presents some of the drivers and rational for implementing an approach that uses analytics at the centre of welfare reform. A wide range of methods are now being used to identify cohorts of interest and propensity models to support targeted campaign based initiatives on the basis of a level of risk. Return on investment is estimated through use of control and comparison groups and the capture of feedback to support model improvement (targeting) and organisational learning and attribution of value | type ii error, type i error, roc curve | source | 4 |
| 343 | The Armchair Quarterback: Writing SAS Code for the Perfect Pivot (Table, That Is) | Peter W. Eberhardt | "Can I have that in EXCEL?" This is a request that makes many of us shudder. Now your boss has discovered EXCEL pivot tables. Unfortunately, he has not discovered how to make them. So you get to extract the data, massage the data, put the data into EXCEL, and then spend hours rebuilding pivot tables every time the corporate data are refreshed. In this workshop, you learn to be the armchair quarterback and build pivot tables without leaving the comfort of your SAS environment. In this workshop, you learn the basics of EXCEL pivot tables and, through a series of exercises, you learn how to augment basic pivot tables first in EXCEL, and then using SAS. No prior knowledge of EXCEL pivot tables is required |
excel, dataset, proc step, proc tabulate, output delivery system, proc means, xls, raw data | source | 36 |
| 344 | The Box-Jenkins Methodology for Time Series Models | Theresa H. Ngo | A time series is a set of values of a particular variable that occur over a period of time in a certain pattern. The most common patterns are increasing or decreasing trend, cycle, seasonality, and irregular fluctuations (Bowerman, O'Connell, and Koehler 2005). To model a time series event as a function of its past values, analysts identify the pattern with the assumption that the pattern will persist in the future. Applying the Box-Jenkins methodology, this paper emphasizes how to identify an appropriate time series model by matching behaviors of the sample autocorrelation function (ACF) and partial autocorrelation function (PACF) to the theoretical autocorrelation functions. In addition to model identification, the paper examines the significance of the parameter estimates, checks the diagnostics, and validates the forecasts | time series analysis, moving average, proc calis, null hypothesis, ma, estimate, seasonal difference, correlation, sample, first difference, partial autocorrelation, regression analysis | source | 11 |
| 345 | The Concepts and Practice of Analysis with SAS Enterprise Guide | Christopher W. Schacherer | SAS Enterprise Guide continues to gain acceptance among SAS programmers as an enterprise solution for a wide range of reporting and analytic applications. However, in order for organizations to realize maximum benefit from their investment in Enterprise Guide, subject-matter experts and a new generation of analysts who have not been trained as SAS programmers need to be trained in the use of this tool. The current work provides a framework for this training by explaining the relationship between Enterprise Guide and traditional SAS programming, introducing the basic Enterprise Guide concepts necessary to function in this environment, and presenting examples of common tasks that will help these users become immediately productive | sas enterprise guide, proc step, out=, sas programmer, sas programming, dataset, raw data, sas library, excel, libname statement, insurance claims, proc freq, sql server, proc sql, relational database, work library, bar chart, sas administrator, proc sort, format statement, substr function, sas output, user-defined format, numeric variable, proc means | source | 48 |
| 346 | The Dynamic Cube Viewer - OLAP Made Easy | Raymond Ebben | The Dynamic Cube Viewer is a bespoke browser-based application that offers an intuitive interface for business users to query OLAP cubes, without the need to have an understanding of OLAP cubes. It has originally been developed as a benchmarking tool for the Association of Dutch Insurers and has been further developed by OCS Consulting to make it more generic. The application only reads OLAP cube metadata and uses this to build the user interface | dataset, excel | source | 7 |
| 347 | The Forest and the Trees: See It All with SAS Visual Analytics Explorer | Nascif Abousalh-Neto | It's a jungle out there! A data jungle, that is. With so much data to process, it's too easy to become lost. What's a data explorer to do? This paper explains how data exploration journeys usually follow a generic workflow composed of nine well-defined tasks that are easy to perform using SAS Visual Analytics Explorer. Armed with this knowledge, you will be able to see both the forest and the trees, and never worry again about losing your way | exploratory data analysis, correlation, data science, proc hpsplit, box plot, data mining, bar chart, heatmap, scatter plot, outlier | source | 14 |
| 348 | The Hash-of-Hashes as a "Russian Doll" Structure: An Example with XML Creation | Joseph W. Hinson | SAS9 hash objects have inspired novel programming techniques. The recent discovery that hash tables can contain even other hash objects: "hash of hashes" opens the door to their application to hierarchical data processing. Because hierarchies, like Russian dolls, can be considered "containers within containers." Thus, nested hash objects could model XML, a hierarchical data structure increasingly finding its way into clinical trial data. Clinical programmers now have to deal with hierarchical as well as tabular and relational data sets. SAS now provides tools like the XML LIBNAME engine and XML mapper. This paper aims to show, using a simplified CDISC LAB model, that the hash object could well be another tool for creating XML |
hash table, clinical trial, data wrangling, clinical data, hash iterator, laboratory data, cdisc, hash of hashes | source | 21 |
| 349 | The Hospital Game: Optimizing Surgery Schedules to Save Resources, and to Save Lives | Andrew S. Pease, Ayesgul Peker |
Surgeons are required to perform vital operations on a daily basis, but often their planning is not optimized for the "downstream" care. This leads to under- or overutilization of postoperative nursing wards, which can either compromise a hospital's ability to provide the best possible care and ensure the best possible patient outcome, or result in precious hospital funding going toward underutilized resources. This paper briefly reviews some of the academic research that is available for a data-driven, operations research approach to solving this challenge, which is dubbed the "Hospital Game" in some of this literature. The paper then proposes an optimization-based approach that uses the OPTMODEL procedure to derive the best surgery schedule for a major European hospital |
odds ratio, sample, proc step, standard deviation, integer programming, linear programming | source | 14 |
| 350 | The Ins and Outs of Web-Based Data with SAS | Bill McNeill | Do you have data on the web that you want to integrate with SAS? This paper explains how you can obtain web data, process it, and export it back to the web. Examples use existing features, such as the SOAP and XSL procedures, the SAS XML Mapper application, and the XMLV2 LIBNAME engine, along with two new features: the XMLV2 LIBNAME engine AUTOMAP= option and the JSON procedure. The AUTOMAP= option allows for creation of default XMLMap files within SAS. The JSON procedure exports SAS data sets in JSON format to an external file. And if you need to write free-form JSON output, forget the SAS PUT statements, the JSON procedure supports free-form JSON output as well |
web service, proc sql, dataset, libname engine, sas base, proc json, libname statement, ods json, where statement, ods html | source zip |
25 |
| 351 | The Magnificent DO | Paul M. Dorfman | Any high-level computer program can be written using just three fundamental constructs: Sequence, Selection, and Repetition. The latter forms the foundation of program automation, making it possible to execute a group of instructions repeatedly, modifying them from iteration to iteration. In SAS language, explicit repetition is implemented as a stand-alone structural unit via the DO loop - a powerful construct laden with a myriad of features. Many of them still remain overshadowed by the tendency to structure code around the implied loop - even when it makes the program more complex or error-prone. We will endeavor to both straighten out some such incongruities and give the sense of depth and breadth of the magnificent SAS construct known as the DO loop. In the SAS Language, explicit repetition is implemented via different forms of the construct known as the DO-loop | missing value, leave, continue, dataset, sas programming, do loop, conditional do loop, hash table, output, credit card | source | 15 |
| 352 | The SAS Output Delivery System: Boldly Take Your Web Pages Where They Have Never Gone Before! | Chevell T. Parker | HTML output is one of the most effective and portable methods of disseminating information within an organization. Using a variety of techniques in the SAS Output Delivery System (ODS), you can create HTML output that increases the visibility and functionality of your web pages. This paper discusses how to use these ODS techniques to deliver web content specifically for mobile devices, web content for both mobile and desktop devices, and web content specifically for desktop devices. In the first two categories, the paper discusses the challenges and solutions for both types of web content. For desktop devices, the paper discusses how to extract data from web pages and place it into pivot tables for data-visualization purposes in EXCEL |
sample, ods html, output delivery system, excel | source | 21 |
| 353 | The SAS Programmer's Guide to XML and Web Services | Christopher W. Schacherer | Because of XML's growing role in data interchange, it is increasingly important for SAS programmers to become familiar with SAS technologies and techniques for creating XML output, importing data from XML files, and interacting with web services-which commonly use XML file structures for transmission of data requests and responses. The current work provides detailed examples of techniques you can use to integrate these data into your SAS solutions using SAS XML Mapper, the XML LIBNAME engine, the Output Delivery System, the FILENAME statement, and new SOAP functions available beginning in SAS 9.3 |
web service, libname engine, dataset, proc step, macro variable, proc print, insurance claims, proc template, sas library, output delivery system, filename statement, libname statement, ods html, sas programmer, missing value, proc sql, excel, macro quoting function | source | 32 |
| 354 | The SAS Versus R Debate in Industry and Academia | Chelsea Lofland, Rebecca Ottesen |
Despite industry being heavily dominated by SAS, R is used widely in academia due to being free and open source software that is structured around users being able to write and share their own functions. However, this disconnect leaves many students who are pursuing analytical degrees struggling to get a job with less SAS experience than desired by companies. Alternatively they could face the struggle of transitioning everything they learned in university from R to SAS. Ideally one would know every possible programming language and use the one that best suits the situation. This is rather unrealistic. Our goal is to show the benefits of these two very different software packages and how to leverage both of their strengths together. We also address many of the misconceptions we often see with R users who have left the SAS programming language years ago and have not kept up with current SAS trends and advances | proc step, data governance, macro definition, excel | source | 6 |
| 355 | The SQL Tuning Checklist: Making Slow Database Queries a Thing of the Past | Jeffrey Bailey, Tatyana Petrova |
The SQL query had been running for 36 hours before it was killed. The project team is panicking. The customer is upset. What can you, a SAS programmer, do to get out of this dilemma? Something has to be done, but what? Chances are you have been there. You have felt the pain. You have lived through the helplessness and fear of imminent failure. Fortunately, it is possible to get out of this mess. This paper will show you the steps (and the mindset) required to solve this all-too-common problem. This paper includes examples from DB2, Greenplum, Oracle, and Teradata | proc sql, proc step, datepart, sas log, where statement | source | 13 |
| 356 | The Successful SAS Shop: 10 Ideas, Suggestions, and Radical Notions | Lisa Ann Horwitz | A SAS shop might have as few as one or two SAS programmers, or as many as several thousand programmers, analysts, statisticians, data stewards, administrators, and help desk monitors. A SAS shop might be new to an organization, or it might have grown and evolved over a period of many years. Regardless of its size, age, or overall mission, some basic factors allow these groups of people to find satisfaction and reward in what they do. Of course, more pay, longer vacations, and better benefits might be high on the list, but these aren't usually factors that employees have direct control over implementing. On the other hand, numerous low-cost or organizational suggestions can be implemented with relative ease to increase satisfaction and productivity. This paper details 10 ideas, suggestions, and radical notions to ensure happy and productive SAS programmers | sas administrator, sas programmer, user-defined format, sas enterprise guide | source | 7 |
| 357 | The Surprisingly "Sym"ple Alternative to Hardcoding | Rachel Carlson, Ruchi Sharma |
macro variable, call symputx, nominal variable, call symput, treatment arm, proc step | source | 6 | |
| 358 | The Top Four User-Requested Grid Features Delivered with SAS Grid Manager 9.4 | Doug Haigh | As more and more SAS customers choose to deploy their platform for SAS Business Analytics with SAS Grid Manager, we continue to expand the capabilities of a shared, managed, and highly available environment. Several features have been added in SAS 9.4, providing the following: easier administration of the different types of SAS users in the grid increased management of more SAS components running in the grid integration of the grid with IT standards, such as an existing enterprise scheduler increased debugging capabilities for quick problem resolution This paper details how to accomplish all of the above through new grid options sets, workspace servers spawned on the grid, new options in SAS Grid Manager Client Utility, and enhanced logging | sas grid, sas data integration studio, sas enterprise guide, sas log, sysrc, data mining | source | 7 |
| 359 | The Value of Neighborhood Information in Prospect Selection Models: Investigating the Optimal Level of Granularity | Dirk Van den Poel, Philippe Baecke |
Within analytical customer relationship management (CRM), customer acquisition models suffer the most from a lack of data quality because the information of potentia l customers is mostly limited to socio-demographic and lifestyle variables obtained from external data vendors. Part icularly in this situation, taking advantage of the spatial correlation between customers can improve the predictive perfor mance of these models. This study compares the pred ictive performance of an autoregressive and hierarchical t echnique in an application that identifies potentia l new customers for 25 products and brands. In addition, this study shows that the predictive improvement can vary sig nificantly depending on the granularity level on which the nei ghborhoods are composed. Therefore, a model is intr oduced that simultaneously incorporates multiple levels of granularity resulting in even more accurate predictions | logistic distribution, logistic regression, proc mixed, regression analysis, sample, response variable, correlation, predictor variable, data cleaning, estimate, missing value, significance level, proc step, likelihood function, proc glimmix, proc logistic, parametric test | source | 15 |
| 360 | This is the Modern World: Simple, Overlooked SAS Enhancements | Bruce F. Gilsen | proc step, date variable, sysdate9, character variable, libname statement, proc copy, proc cport, dataset, conditional do loop, relational database, proc sql, index | source | 12 | |
| 361 | Three Easy Ways to Create Customized SAS Graphs | Qinghua (Kathy) Chen | We often hear people saying that "a picture is worth a thousand words". With that in mind, it basically tells us how powerful graphics can be when used properly. Ways to make graphs with great visual impact has drawn a great deal of attention from people in many fields and impactful graphics help reviewers interpret the data. SAS has made significant improvement in graphs software over the past few years. With new features rolled out such as Output Delivery System (ODS) graphics, Graphic Template Language (GTL) and annotated data sets, creating customized graphics is as easy as creating a simple plot. This paper will describe three easy ways to create customized graphs in SAS |
proc template, scatter plot, output delivery system, proc step, series plot, graph template language, horizontal bar chart, bar chart, proc sgplot | source | 13 |
| 362 | Time Is Precious, So Are Your Models: SAS Provides Solutions to Streamline Deployment | Jonathan Wexler, Wayne Thompson |
Organizations spend a significant amount of time, often too much, operationalizing models. The more time you can spend on analytics, and the less time on deployment headaches, the better chance you have to address core business challenges. This paper shows you how SAS has accelerated data mining model deployment throughout the analytical life cycle, by providing key integration points across SAS solutions. Whether you built your models using SAS High-Performance Analytics Server, SAS Enterprise Miner, or SAS/STAT, this paper shows you how to automate the management, publishing, and scoring of models by using SAS Data Integration Studio, SAS Model Manager, and SAS Scoring Accelerator. Immediate benefits include reduced data movement, increased productivity of analytic and IT teams, and faster time to results |
sas model manager, data mining, score code, data cleaning, model deployment, data validation rules, challenger model, big data, champion model, data science, proc step, spk file | source | 17 |
| 363 | Time Series Data: Anatomy of an ETL Project | Leonard Polak | It's one thing to study SAS tools and another to apply them to actual situations. In this paper, we follow along as web data is copied and transformed-and ultimately made available to users | raw data, numeric variable, proc import, proc step, date variable, dictionary tables, macro variable, macro loop, proc sql, %if-%then-%else, macro definition | source | 7 |
| 364 | Tips and Techniques for Deploying SAS in an Application Virtualization Environment | Chuck Hunley, Michael King, Max Blake, Rob Hamm |
Application virtualization is being increasingly used by many organizations to more easily deploy, maintain, and manage their desktop applications. There are many vendors and products on the market to choose from, including VMware, Citrix, and Microsoft. Each vendor's technology comes with its unique set of features and nuances. What do you need to know to get SAS up and running? This paper explores some best practices, gotchas, and guidelines that will help you succeed when deploying and using SAS in an application virtualization environment | sas enterprise guide, sas management console, note | source | 9 |
| 365 | Tips and Techniques for Moving SAS Data to JMP Graph Builder for iPad | Michael Hecht | You have an iPad. You have the JMP Graph Builder app1 for it. But how do you get your SAS data set to your iPad, so you can use all the cool features that Graph Builder has to offer? This paper describes how to use the new JMP Engine in SAS to create a workflow that makes it easy to move your data sets to the iPad | proc export, dataset, proc step | 7 | |
| 366 | Tips for Generating Percentages Using the SAS TABULATE Procedure | Kathryn McLawhorn | PROC TABULATE is one of the few Base SAS procedures that calculate percentages. The procedure is unique in that it has many default statistics for generating percentages and also provides the ability to customize denominator definitions. Determining the right denominator definition is an important but often challenging aspect of calculating percentages. Written for intermediate users, this paper discusses techniques for enhancing PROC TABULATE output with percentage statistics. Using examples, the paper illustrates how to add standard percentages to the PROC TABULATE output, and it shares tips for calculating percentages that you might have thought not possible. The paper also illustrates how to avoid common pitfalls that are related to structuring denominator definitions and how to format table output |
class variable, proc tabulate, proc step, sample, output delivery system, missing value, proc freq | source | 13 |
| 367 | Top 10 Most Powerful Functions for PROC SQL | Chao Huang, Yu Fu |
PROC SQL is not only one of the many SAS procedures and also a distinctive subsystem with all common features from SQL (Structured Query Language ). Equipped with PROC SQL, SAS upgrades to a full-fledging relational database management system. PROC SQL provides alternative ways to manage data other than the traditional DATA STEP and SAS procedures. In addition, SAS's built-in functions are the add-on tools to increase the power of PROC SQL . In this paper, we illustrate ten popular SAS functions, which facilitate the capacity of PROC SQL in data management and descriptive statistics |
proc sql, proc step | source | 5 |
| 368 | Turn Your Plain Report into a Painted Report Using ODS Styles | Allison Booth, Cynthia L. Zender |
In order to use STYLE= statement level overrides, you need to understand which pieces or areas of PRINT, REPORT, and TABULATE output you can change. And then you need to understand how and where in your procedure syntax to use the STYLE= override syntax. Last, but not least, you need to know the names of the style attributes that you want to change. This presentation illustrates with concrete examples how to use STYLE= overrides with PRINT, REPORT, and TABULATE. As the examples move from the simple to the complex, you will learn how to change fonts, add text decoration, alter the interior table lines, perform trafficlighting, and insert images into your ODS output files using some ODS magic to improve your reports |
proc step, proc print, proc report, proc tabulate, user-defined format, sas enterprise guide, output delivery system, proc format, class variable, ods html | source | 22 |
| 369 | Uncovering Patterns in Textual Data with SAS Visual Analytics and SAS Text Analytics | Daniel Zaratsian, Justin Plumley, Mary Osborne |
SAS Visual Analytics is a powerful tool for exploring data to uncover patterns and hidden opportunities. The challenge with the growing amount of data is that the majority is unstructured, often in the form of customer feedback, survey responses, social media conversation, blogs, news articles, and other unstructured text. By integrating SAS Visual Analytics with SAS Text Analytics, you can uncover patterns in both structured and unstructured data, while enriching and visualizing your data with customer sentiment and categorical flags, and uncovering root causes that primarily exist within unstructured data. This paper highlights a case study that provides greater insight into unstructured data and demonstrates advanced visualization, while enhancing time to value by leveraging SAS Visual Analytics high-performance, in-memory technology and SAS advanced text analytics capabilities | unstructured data, root cause analysis | source | 11 |
| 370 | Unleashing the Power of Unified Text Analytics to Categorize Call Center Data | Arila Barnes, Jared Peterson, Saratendu Sethi |
Business analysts often want to take advantage of text analytics to analyze unstructured data. With that in mind, SAS is delivering a new web-based application that is designed to put the power of SAS Text Analytics into the hands of the analyst. This application combines the power of SAS Text Miner and SAS Content Categorization in a single user interface that enables users to automatically create statistical and rule-based models from their domain knowledge. This paper demonstrates how a business analyst in a call center environment can identify emerging topics, generate automatic rules for those topics, edit and refine those rules to improve results, derive insights through visualization, and deploy the resulting model to score new data | unstructured data, proc step | source | 12 |
| 371 | Update, Insert, and Carry-Forward Operations in Database Tables Using SAS Enterprise Guide | Sreenivas Mullagiri, Thomas E. Billings |
You want to use SAS Enterprise Guide to simulate database logic that includes any of: update, insert, carry- forward operations on old, changed, or new rows between two data sets, to create a new master data set. However, the Query Builder Task GUI does not have an Update/Insert option. Methods for simple types of update, insert, and/or carry-forward operations are described and illustrated using small data sets. First, we review Base SAS methods, including DATA STEP and PROC SQL code. Then, two GUI-only/Task-based methods are described: one based on the Sort Data Task GUI; the other on the Query Builder Task GUI. The issue of whether integrity constraints are preserved is also discussed |
sas enterprise guide, constraint, work library, proc step, proc sql, sas base, sas programming | source | 11 |
| 372 | Using CALL SYMPUT to Generate Dynamic Columns in Reports | Bob Lan, Regan Li, Sai Ma, Suwen Li |
When creating reports, we often want to make the report respond dynamically to data. If the headers and the number of columns in the report are unknown, it is helpful when they change dynamically depending on the data. As a powerful SAS procedure, PROC TABULATE can produce dynamic results in most cases. This paper describes how to use the CALL SYMPUT routine and PROC REPORT to generate dynamic columns in reports in cases where PROC TABULATE does not yield the desired results |
macro variable, proc step, call symput, proc report, proc tabulate, demographics table, macro loop | source |
6 |
| 373 | Using Classification and Regression Trees (CART) in SAS Enterprise Miner? for Applications in Public Health | Leonard Gordon | Classification and regression trees (CART) - a non-parametric methodology- were first introduced by Breiman and colleagues in 1984. In this paper they are employed using SAS Enterprise Miner and several examples are given to demonstrate their use. CARTs are underused (especially in public health) and they have the ability to divide populations into meaningful subgroups which will allow the identification of groups of interest and enhance the provision of products and services accordingly. They provide a simple yet powerful analysis. It is hoped that their value is demonstrated and this will enhance their increased use in data analysis. Keywords: classification and regression trees, CART | regression analysis, data mining, proc hpsplit, sample, data cleaning | source | 8 |
| 374 | Using Data Mining in Forecasting Problems | Chip Wells, Justin Kauhl, Timothy Rey |
In today's ever-changing economic environment, there is ample opportunity to leverage the numerous sources of time series data now readily available to the savvy business decision maker. This time series data can be used for business gain if the data is converted to information and then into knowledge. Data mining processes, methods and technology oriented to transactional-type data (data not having a time series framework) have grown immensely in the last quarter century. There is significant value in the interdisciplinary notion of data mining for forecasting when used to solve time series problems. The intention of this talk is to describe how to get the most value out of the myriad of available time series data by utilizing data mining techniques specifically oriented to data collected over time; methodologies and examples will be presented | time series analysis, data mining, time series data, feature selection, proc varreduce, correlation, response variable, big data, proc step, regression analysis, predictor variable, autocorrelation, sample, sas enterprise guide | source | 17 |
| 375 | Using Design Principles to Make ODS Template Decisions | Helen Smith, Susan Myers |
With the Output Delivery System (ODS), SAS continues to provide programmers with many style templates for developing reports. These default templates and style definitions present the data in a clear and attractive manner often with no further thought needed. The website "SAS ODS Style Examples" provides a link to screen shots of SAS ODS style templates (Swoope, 2011). However, when producing complicated reports with multiple requirements, using basic design principles to determine which template or which custom style definition to use can make for a more readable and comprehensive final report. This paper presents the code and the design considerations for two ODS reports; one, a redesign of a 10-plus year old SAS program originally designed with PUT statements, and two, a highly customized SAS program for delivering output in EXCEL. The reports are generated using PROC REPORT and developed with version 9.2 and 9.3 of SAS executing on a Windows XP platform |
sas management console, put statement, sample, excel | source | 12 |
| 376 | Using Mail Functionality in SAS | Erik W. Tilanus | The DATA STEP can write information to virtually any destination. You are probably familiar with writing to SAS data sets and external files. But also email can function as a destination. The paper will discuss how to configure the email features in the system options and share practical hints how to use them. Then we will proceed with sending a simple email from the DATA STEP, with or without attachments. After that we will use extensions to the standard PUT statement to support the email facility to send personalized mass-mailings. Finally, we will show how to send procedure output that has been created using ODS |
put statement, file statement, options statement, filename statement, dataset, sas log, cpu time, ods rtf, txt, macro function | source | 14 |
| 377 | Using PROC FCMP in SAS System Development: Real Examples | Xiyun Wang, Yves DeGuire |
At Statistics Canada, many complex applications have been built over the years using SAS. These applications typically follow a modular design and leverage the SAS Macros facility. In some cases, the SAS/TOOLKIT product has been used along with the C programming language to create user-written functions and procedures. With SAS9.2 and beyond, the FCMP procedure gives the SAS programmer the ability to create user-written functions and CALL routines using DATA STEP syntax. Many projects within Statistics Canada have started adopting the FCMP procedure to encapsulate business logic and ease the system development process. In this paper, examples of user- written functions and CALL routines are presented to illustrate key concepts as well as several use cases |
proc step, proc fcmp, odds ratio, sample, macro variable, where statement, return, sas programmer, dataset | source | 10 |
| 378 | Using PROC FCMP to the Fullest: Getting Started and Doing More | Arthur L. Carpenter | The FCMP procedure is used to create user defined functions. Many users have yet to tackle this fairly new procedure, while others have only attempted to use only its simplest options. Like many tools within SAS, the true value of this procedure is only appreciated after the user has started to learn and use it. The basics can quickly be mastered and this allows the user to move forward to explore some of the more interesting and powerful aspects of the FCMP procedure. Starting with the basics of the FCMP procedure, this paper also discusses how to store, retrieve, and use user defined compiled functions. Included is the use of these functions with the macro language as well as with user defined formats. The use of PROC FCMP should not be limited to the advanced SAS user; even those fairly new to SAS should be able to appreciate the value of user defined functions |
macro facility, proc step, proc fcmp, macro function | source | 10 |
| 379 | Using PROC FORMAT and Other Little Tweaks to Enable PROC TABULATE?s Hidden Capabilities of Optimizing Statistical Reporting View Paper View Poster | Heli Ghandehari, Victor Lopez |
PROC TABULATE is arguably the most efficient approach for calculating statistics and generating output, all within one procedure. However, developers must often stray from TABULATE when display specifications require values to be reported as concatenated pairs. For example, a common reporting requirement is for a mean and standard deviation to be grouped within a single cell, with the latter enveloped by brackets. Similarly, a range could be requested with the minimum and maximum delimited by a dash, or perhaps a confidence interval nestled within parentheses. The combinations are endless, but the underlying solution is simple and universal. This paper demonstrates the utility of PROC FORMAT's PICTURE statement when applied in combination with TABULATE's computational and reporting capabilities to create customized statistical tables |
proc step, proc tabulate, proc format, standard deviation | source |
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| 380 | Using PROC GENMOD to Investigate Drug Interactions: Beta Blockers and Beta Agonists and Their Effect on Hospital Admissions | Hui Fen Tan, Raymond Gregory, Ronald B. Low, Shunsuke Ito, Vann Dunn |
Every year, more than half a million adverse reactions to drugs are reported to the FDA. This paper is a real -world, large-scale review of beta blockers and beta agonist usage. We use New York Cit y public hospitals' records to investigate whether interactions of beta blockers and beta agonists are associated with increased hospital inpatient admissions, a common indicator of health care quality. PROC GENMOD in SAS provides a variety of count data models, including Poisson regression an d Negative B inomial regression. During clinical trials, many patients are excluded by strict selection criteria. This includes young patients, older patients, and patients with histor y of multiple illnesses. These patients tend to have higher hospital inpatient admission rates. These patients may be prescribed FDA-approved drugs in combinations they could not have received as part of the clinical trials |
regression analysis, clinical trial, negative binomial regression, poisson regression, binomial distribution, poisson distribution, data step, macro definition | source | 10 |
| 381 | Using SAS BI for Integrated Bank Reporting | James L. Beaver | This paper shows how base SAS,SAS Enterprise Guide, ETS and SAS Enterprise BI are used to provide a comprehensive view of bank performance. Data is extracted from the G/L, loan, deposit and application systems, real time data is accessed to provide up-to the minute results on loan activity and system reports are read in to provide additional information. PROC COMPUTAB is used to create financial statements, OLAP cubes are used to provide reports on bank balance sheet components and budget comparisons on non-interest income and expense items by department, and dashboards are used to provide real time reports on loan originations. The reports are presented using SAS BI through the SAS data portal to provide real time, trend and historical reports on the bank's performance |
sample, proc step, ledger, batch processing, sas base, extract, dataset, sas enterprise guide, excel | source | 10 |
| 382 | Using SAS Enterprise Guide: A System Administrator's Perspective | Haibo Jiang, Joseph Buonomo, Sarah Shiue, Thomas Barbour, Rickie Stevens |
With the implementation of SAS Intelligence Platform, and the promotion of SAS Enterprise Guide as the main Statistical Programming and Data Analysis tool for Allergan's clinical studies, we are challenged every day with the support of this new environment used by SAS Programmers and Statisticians in our organization. From a technical support point of view, understanding how end users interact with various components of SAS Enterprise Guide, and how such activities are processed and managed behind the scene by SAS Enterprise Guide and SAS Intelligence Platform Servers, is critical to supporting users. One of the key factors in diagnosing problems and providing resolutions quickly is being able to reproduce incidents and trace them down through system processes. This requires excellent knowledge of the underlying architecture of SAS Intelligence Platform: its various components and how they communicate and work with each other | sas enterprise guide, sas management console, clinical programming, temporary dataset | source | 22 |
| 383 | Using SAS to Assess Individuals? Best Performance in Multiple Dimensions | Aude Pujula, David Maradiaga |
There are many cases where we need to look at the best performance of an individual in several disciplines over multiple time events. For instance, we might want to know a triathlete's best position in the three disciplines over all the races of the season, or the highest test scores of a student in several sub-scores. Looking at the latter example, this paper compares four different methods implementable in Base SAS to create a data set that contains one record per student corresponding to the highest test scores. Of particular interest is the use of PROC SQL combined with the SELECT DISTINCT clause and the MAX function that allows the creation of the desired data set in one step |
proc step, out=, proc sql, select | source | 5 |
| 384 | Using SAS to Create Code for Current Triage Systems during Chemical Incidents | Abbas S. Tavakoli, Erik Svendsen, Jean Craig, Joan Culley |
Chemical incidents involve irritant chemicals such as chlorine poses a significant threat to life and require rapid assessment. This paper used the first outcomes-level study (NIH/NLM 1R21LM010833) involved an actual mass casualty chemical incident to create code for four triage systems (CBRN, SALT, START and ESI). Data used for this p aper from six datasets collected by the project team from a 60-ton railroad chlorine leak that occurred in Graniteville, South Carolina in 2005 include patient demographics, exposure estimates, symptoms, outcome categories, and physiological measurements. Data collected for approximately 900 victims of a chlorine leak were merged to generate a research dataset. SAS 9.2 used to create code from logic to mimic the triage decision tree yielding classifications for each system. Keywords: SAS, Chemical, Triage, University of South Carolina, College of Nursing | frequency analysis, proc freq | source |
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| 385 | Using SAS to Dynamically Generate SAS Code in Order to Display Both Variable Label and Name as Column Header in PROC REPORT and PROC PRINT | Heli Ghandehari, Victor Lopez |
With implementation of data standards such as CDISC SDTM, datasets contain sufficiently meaningful variable names and labels which allow direct reporting from dataset to output (PDF, RTF, and many more). This eliminates the necessity to program lengthy DEFINE statements in PROC REPORT or having to manually assign custom labels in PROC PRINT. This paper illustrates an innovative approach in using SAS to dynamically generate SAS code which will enable us to solve a seemingly easy problem: displaying both the variable label and name as a column header in PROC REPORT or PROC PRINT |
proc step, proc print, proc report, label statement, adverse events, data standard, cdisc | source | 5 |
| 386 | Using SAS to Expand the Application of Standard Measures and Guide Statistical Explorations: Creating Healthy Eating Index Scores Using Nutrition Data System for Research Output | David Landy, David A. Ludwig, Joy Kurtz, Tracie Miller |
New measures, summarizing several variables related to a common health construct are frequently developed in the healthcare industry to provide more valid or complete descriptions of quality and health concepts. We created a SAS program to calculate a measure of diet quality, the Healthy Eating Index (HEI, http://www.cnpp.usda.gov/HealthyEatingIndex.htm) using output from a widely applied dietary software package, Nutrition Data System for Research (NDSR, http://www.ncc.umn.edu/products/ndsr.html). Currently, application of the HEI in research and clinical assessment is limited by the challenges posed in calculating the HEI using the highly complex and detailed NDSR output. The SAS program extracts the required NDSR output files and then calculates the combination of algebraic manipulations and logical statements to obtain HEI scores | macro variable, dataset, txt | source |
12 |
| 387 | Using SAS to Measure Airport Connectivity: An Analysis of Airport Centrality in the US Network with SAS/IML Studio | Augusto Voltes-Dorta, Hector Rodriguez-Deniz, Pere Suau-Sanchez |
The U.S. Federal Aviation Administration (FAA) estimates that $52.2 billion will be available over the years 2011- 2015 to fund airport infrastructure developments. Because one of the main objectives is to reduce congestion and delays, there is a need to acknowledge the importance of connectivity (measured with a centrality indicator) when establishing funding priorities. Currently, the FAA does not do this. In this paper, we exploit the capabilities of SAS/IML Studio to implement a range of centrality measures, construct a graphical representation of the U.S. air transport network from airline ticketing data, test the algorithms to identify hub airports, and study the evolution of these indicators during the last decades in order to analyze the impact of airline decisions on airport connectivity |
sample, capacity planning, line chart, scatter plot, proc step, proc iml, proc freq | source | 14 |
| 388 | Using the ADaM ADAE Structure for Non-AE Data | Mario Widel, Sandra Minjoe |
The final and official ADaM ADAE structure titled "Analysis Data Model (ADaM) Data Structure for Adverse Event Analysis" was developed as an appendix to the ADaM v2.1 to allow simple production of standard Adverse Event tables. An ADaM sub-team is expanding this structure to cover other data analyzed in a similar fashion, such as Concomitant Medications. The basic premise is that data with the same analysis needs as the standard adverse events tables can and should use this structure. This presentation, by members of that ADaM sub-team, describes the AE analysis need and shows how to apply it for other data, such as Concomitant Medications, Medical History, and even Laboratory Events. Examples of ADaM SAS data sets and useful SAS program code are included | adam dataset, adverse events, concomitant medications, medical history, raw data, lowest level term, system organ class, laboratory data, ctcae grade, in=, sdtm domain, cdisc | source | 13 |
| 389 | Using the Boosting Technique to Improve the Predictive Power of a Credit Risk Model | Alejandro Correa Bahnsen, Andres F. Gonzalez, Darwin A. Amezquita |
In developing a predictive model, the complexity of the population used to build the model can lead to very weak scorecards when a traditional technique such as logistic regression or an MLP neural network is used. For these cases some nontraditional methodologies like boosting could help improve the predictive power of any learning algorithm. The idea behind this technique is to combine several weak classifiers to produce a much more powerful model. In this paper, boosting methodology is used to enhance the development of a credit risk scorecard in combination with several different techniques, such as logistic regression, MLP neural networks, a nd others, in order to compare the results of all methodologies and determine in which cases the boosting algorithm increases model performance | neural network, logistic regression, regression analysis, credit risk, logistic distribution, proc hpsplit, data mining, credit scoring, network optimization, response variable | source | 7 |
| 390 | Using the Power of SAS to Analyze and Solve Quality Problems at Shanghai General Motors | Yu Zhang, Ying Wang, Shaozong Jiang, Yahua Li, Jiawen Zhang, Nanxiang Gao, Jian Li, Minghua Pan |
Data to assist in solving quality problems is of enormous value to quality departments in the automotive industry, including that of Shanghai General Motors (SGM). However, millions of claims records, tens of thousands of solving reports, dozens of language descriptions, and heterogeneous regional code present great difficulty for dataflow and knowledge management. This paper explores SGM's information system, known as Problem Solving Analysis (PSA), which uses several foundation tools of SAS, such as Base SAS, SAS/CONNECT, and SAS/ACCESS, to solve business problems faster, and which has used advanced SAS Enterprise Content Categorization to establish 26,000 text rules for word recognition and accurate classification. PSA incorporates effective data infrastructure building, report linking, fast information searches, and diagnosis and forecasting of enterprise problems |
problem solving, advanced sas | source | 6 |
| 391 | Using the QUANTLIFE Procedure for Survival Analysis | Bob Rodriguez, Guixian Lin |
The QUANTLIFE procedure implements quantile regression, which provides a direct and flexible approach to modeling survival times without the proportionality hazard constraint of the Cox model. In clinical studies, quantile regression is helpful for identifying and distinguishing important prognostic factors for patient subpopulations that are characterized by short or long survival times. This paper compares the quantile regression model with the Cox and accelerated failure time models, which are commonly used in survival analysis. An understanding of the differences between these approaches is essential for deciding which model to use in practice. An example illustrates how to estimate regression parameters and survival functions |
survival function, regression analysis, proc quantreg, hazard function, quantile, proc quantlife, quantile functions, survival analysis, proc step, proc phreg, cumulative frequency, proc lifereg | source | 13 |
| 392 | Using the SAS Data Step to Generate HTML or Text-Based Mark-Up | Matthew T. Karafa | source | 7 | ||
| 393 | Using VBA to Debug and Run SAS Programs Interactively, Run Batch Jobs, Automate Output, and Build Applications | Feng Liu, Ruiwen Zhang |
SAS Enterprise Guide provides an API that allows users to automate almost every aspect of running Enterprise Guide projects or simply SAS programs. Visual Basic for Applications (VBA) under Microsoft EXCEL provides a rich environment for debugging and running VBA applications. This paper shows how you can use VBA to access the automation API of Enterprise Guide to do sophisticated tasks or build your own applications that run SAS. Through VBA, you can create SAS programs on the fly, debug and run programs, analyze SAS listing output, save SAS log to files, and examine SAS ODS. Y ou can accomplish many tasks like running PROC EXPORT automatically, which is not feasible through Enterprise Guide's main interface. Advanced SAS users can run batch jobs, schedule jobs in parallel, or use SAS output as input to other applications |
excel, proc export, sas log, sas enterprise guide, proc step, sas output | source | 9 |
| 394 | V is for Venn Diagrams | Kriss Harris | Would you like to produce Venn diagrams easily? This paper shows how you can produce stunning two-, three-, and four-way Venn diagrams by using the SAS Graph Template Language, in particular the DRAWOVAL and DRAWTEXT statements. From my experience, Venn diagrams have typically been created in the pharmaceutical industry by using Microsoft EXCEL and PowerPoint. EXCEL is used to first count the numbers in each group, and PowerPoint is used to generate the two- or three-way Venn diagrams. The four-way Venn diagram is largely unheard of. When someone is brave enough to tackle it manually, then working out the numbers that should go in each of the 16 groups and inputting the right number into the right group is usually done nervously! |
graph template language, macro variable, macro call, %if-%then-%else, excel | source |
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| 395 | Variable Reduction in SAS by Using Weight of Evidence and Information Value | Alec Zhixiao Lin | Variable reduction is a crucial step for accelerating model building without losing potential predictive power of the data. This paper provides a SAS macro that uses Weight of Evidence and Information Value to screen continuous, ordinal and categorical variables based on their predictive power. The results can lend useful insights to variable reduction. The reduced list of variables enables statisticians to quickly identify the most informative predictors for building logistic regression models | proc varreduce, regression analysis, proc step, logistic regression, proc sql, macro definition, logistic distribution, proc princomp | source | 12 |
| 396 | Variance Heterogeneity and Non-Normality: How the SAS TTEST Procedure Can Keep Us Honest | Aarti P. Bellara, Anh P. Kellermann, Diep T. Nguyen, Eun Sook Kim, Jeffrey D. Kromrey, Patricia Rodríguez de Gil, Yi-Hsin Chen |
The independent samples t-test is one of the most used tests for detecting true mean differences . The SAS system provides the PROC TTEST procedure which is an easy way to conduct a test for the differe nce between two population means by assuming homogeneity of variance or avoiding it. However, the t-test and its alternatives (the Satterthwaite's approximate test and Conditional t-test) assume population normality. Although past resear ch has provided evidence of the t-test's robustness t o departures of normality, questions abo ut the performance of conditional testing when the assumption of normality is not met remain . This paper describes pre vious research on preliminary tests under the normality assumption , extends this research to the e valuation of conditional testing to departures of normality, and provides guidance to researchers on the proper use of this test with non -normal, heteroscedastic population distributions |
type i error, sample size, sample, significance level, type i error control, proc ttest, proc step, power analysis | source |
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| 397 | Variance Partition: My Mission and Ambition Come to Fruition | Brenda Beaty, L. Miriam Dickinson |
In medical research, we are often interested in understanding the complex interplay of variables with one or more clinical outcomes. Because our bodies are always in motion, simply viewing a snapshot of data in time is sub-optimal. Longitudinal data gives us the advantage of modeling 'real-life' time-dependent variables and outcomes. This paper is an exploration of one such project. In this paper, we will first familiarize ourselves with a study of the relationship of diabetic nephropathy and blood pressure measured longitudinally. We will then explore a number of ways to model the data with the final goal of using time-varying covariates to model the trajectory of "the average patient", using complete partitioning of the variance | predictor variable, response variable, time-dependent covariate | source | 8 |
| 398 | Vehicle Retail Forecasting Demand and Inventory Management Case Study at Shanghai General Motors | Christina Zhong | Just like any investment in business, Vehicle forecast and inventory management needs to serve the purpose of maximizing profit. For manufacturers, it is crucial to manage Supply Chain Operations as efficiently and profitably as possible. As we all know, Demand-Driven forecasting allows companies to sense demand signals through the synchronization of internal/external data. As the result, the accurate demand forecast will help the motorcar manufacture to support the inventory plan. However, in many cases inventory has turned into a major cash flow constraint thus making it necessary to optimize inventory using analytical and statistical methods in an integrated approach | demand forecasting, inventory optimization | source | 7 |
| 399 | Versatile Global Prompting for SAS Web Report Studio | Hong Jiang | Prompts built into the information map are convenient tools for developers using SAS Web Report Studio (WRS). However, the parameter values set through these prompts are not able to populate to other web report sections or objects, limiting their usefulness. This paper describes a solution for creating versatile global prompts that support one-time user response for multiple objects or sections in a SAS web report. Both single-value and multiple-value selection features can be implemented by following the directions described in this paper | macro variable, sas enterprise guide | source | 7 |
| 400 | Virginia's Best: How to Annotate County Names and Values on a State Map | Anastasiya M. Osborne | This paper describes a work project to annotate a Virginia state map with long county names and National Agricultural Statistics Service (NASS) data, using enhanced color and techniques to minimize map crowding. Displaying text and numeric data by county on a state map is different from displaying state-level data on a U.S. map. Long county names rather than two- letter state abbreviations require additional effort by a programmer to create a readable map. The SAS code with %ANNOMAC, %CENTROID, PROC GPROJECT, PROC GMAP, and a 20-pattern color scheme were developed to create requested maps that showcased in color Virginia's top agricultural counties. This paper is for intermediate level programmers |
excel, proc step | source | 20 |
| 401 | Virtualized Environment for SAS High-Performance Products | Daniel Zuniga, Rich Pletcher, Thomas N. Keefer Jr. |
Companies are adopting virtualization technologies to support entire IT infrastructures. There is an increase in requests for virtualization deployment reference architectures and best practices. A SAS staff built reference architecture was tested in a virtualized environment for SAS 9.3, SAS Grid Manager, SAS High-Performance Analytics, and SAS Visual Analytics. This paper outlines performance testing results and best practices for planning, managing and deploying a successful enterprise-class, virtualized environment | sas grid, high-performance sas | source | 7 |
| 402 | Visual Techniques for Problem Solving and Debugging | Andrew B. Ratcliffe | No matter how well we plan, issues and bugs inevitably occur. Some are easily solved, but others are far more difficult and complex. This paper presents a range of largely visual techniques for understanding, investigating, solving, and monitoring your most difficult problems. Whether you have an intractable SAS coding bug or a repeatedly failing SAS server, this paper offers practical advice and concrete steps to get you to the bottom of the problem. Tools and techniques discussed in this paper include Ishikawa (fishbone) diagrams, sequence diagrams, tabular matrices, and mind maps | problem solving, proc step | source | 10 |
| 403 | Visualize Your OLAP Cubes on a Map through a Stored Process | Frank Poppe, Sjoerd Boogaard |
What if you have an OLAP cube with a geo-dimension and you want a map from that, but you don't have ArcGIS? Enter this general stored process. It can read measures and dimensions from the cube, and it uses SAS/GRAPH software to combine that with boundary data, creating a color-coded map. The map is clickable to navigate between the geographical levels. A selection pane offers measures, and non-geographical dimensions surface as (hierarchical) filters. Measures and dimensions are read from the metadata; values for the filters are read from the cube, using MDX. Boundaries are clipped to the right zoom level, and the picture gets a background with roads from a web service. HTML, CSS, and JS is generated to glue everything together and to deliver it to the portal |
where statement, proc step, macro variable, proc sql, web service, proc gmap | source | 10 |
| 404 | We All Have Bad Dates Once in a While... | Patrick Kelly, Randall Deaton |
Dates in a corporate data arena can be a dangerous liaison. The strain of translating corporate date types to SAS date types can be tricky to navigate, let alone bringing a third-party date type into the mix. Adding third wheel to your SAS dates can create a comedy of errors. An experienced SAS programmer with knowledge of the SAS macros and a few clever programming tricks can more easily resolve your SAS Dates from a sticky situation to an orderly affair | sysdate9, macro variable, put statement | source | 4 |
| 405 | Weathering the Storm: Using Predictive Analytics to Minimize Utility Outages | Kathy Ball, Mark Konya |
In order to meet ever-increasing customer service expectations, electric utilities must continuously improve the reliability of their electric distribution systems. Over the last decade, utilities have invested in digital technologies that give them near-real time readings on the health of their electric grid. This data is incredibly useful during major storm outages, but the flood of data pouring in from transformers and meters can quickly overwhelm even the most seasoned Distribution Engineer. Without analytical technologies, engineers cannot respond quickly enough to prevent additional outages and improve restoration times. Distribution Optimization for storm management equips utility engineers and dispatchers to predict which assets will be affected by storms while optimizing the placement of crews, thus decreasing outage restoration times | correlation, sas visual analytics, predictive analytics, survival analysis, data mining, linear programming, regression analysis | source |
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| 406 | Weighted Sequential Hot Deck Imputation: SAS Macro vs. the SUDAAN PROC HOTDECK | David Izrael, Michael P. Battaglia |
Item non-response is a challenge faced by virtually all surveys. Item non-response occurs when a respondent skips over a question, refuses to answer a question, or indicates that they do not know the answer to a question. Hot deck imputation is one of the primary item non-response imputation tools used by survey statisticians. Recently, new competitor in the field of Weighted Sequential Hotdeck Imputation has arrived: PROC HOTDECK of SUDAAN, version 10. We compared the results of imputation using the new procedure with the results of the Hotdeck SAS Macro with respect to: a) how close the post-imputation weighted distributions and standard errors of the estimates are to those of the item respondent data; b) whether there is a difference in the number of times donors contribute to the imputation |
imputation, proc step, proc calis, macro definition, missing value, multiple imputation | source |
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| 407 | What Do Your Consumer Habits Say About Your Health? Using Third-Party Data to Predict Individual Health Risk and Costs | Albert Hopping, Rick Monaco, Sarah Rittman, Satish Garla |
The Affordable Care Act is bringing dramatic changes to the health care industry. Previously uninsured individuals are buying health insurance and consuming health care services differently. These changes are forcing insurers to reevaluate marketing, engagement, and product design strategies. The first step in addressing these challenges is understanding the financial risk of new entrants into the marketplace. How do you predict the risk of a person without any historical cost information? What if all you know is the name and address? Historically, the finance industry has long used third-party consumer data to predict future finance habits and credit risk. This paper takes a look at applying advanced analytics from SAS to third-party data in order to predict health care utilization risk and costs | health insurance, response variable, insurance claims, data mining, missing value, cluster analysis, nominal variable, data cleaning, imputation, validation set, neural network, correlation, extract, training set, proc varclus, regression analysis, histogram, proc princomp | source | 15 |
| 408 | What SAS Administrators Should Know About Security and SAS Enterprise Guide | Casey Smith | SAS Enterprise Guide is a flexible and powerful tool in the hands of your users. However, as every superhero knows, with power comes responsibility. As an administrator, you want to ensure various groups of users have access to the specific resources they need to be as productive as possible, while at the same time protecting company assets and minimizing risk. This paper explores various security considerations from the SAS Enterprise Guide perspective, such as authentication, authorization, user administration, access management, encryption, and role-based availability of application features | sas enterprise guide, sas management console, sas enterprise guide project, sas log, libname statement, access control, dataset | source | 20 |
| 409 | What Score Should Johnny Get? Missing_Items SAS Macro for Analyzing Missing Item Responses on Summative Scales | Jeffrey D. Kromrey, Patricia Rodríguez de Gil |
Missing data are usually not the focus of any given study but researchers frequently encounter missing data when conducting empirical research. Missing data for Likert -type response scales, whose items are often combined to make summative scales, are particularly problematic because o f the nature of the constructs typically measured, such as attitudes and opinions. This paper provides a SAS macro, written in SAS/IML and SAS/STAT, for imputation of missing item responses that allows estimation of person -level means or sums across items in the scale. Imputations are obtained using multiple imputation (MI), single regression substitution (SRS), relative mean substitution (RMS), and person mean substitution (PMS). In addition, the results of a simulation study comparing the accuracy and precision of the imputation methods are summarized. Keywords: Missing data, missing items, summative scales |
missing value, mean imputation, imputation, sample, regression analysis, multiple imputation, missing at random, dataset, rmse, macro call, mse, variable, correlation | source |
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| 410 | What's in a SAS Variable? Get Answers with a V! | William C. Murphy | If you need information on variable attributes, PROC CONTENTS will provide all of the specifics. You could also access the SQL DICTIONARY which contains tables filled with details on the variables in the active data sets. If you need just one piece of information on a single variable both of these methods could prove to be cumbersome. However, SAS has a whole series of functions that can produce the information on one attribute of one variable at one time. These functions are named with a V prefix followed by descriptive term for the attribute. They include VNAME, VLABEL, VTYPE, VFORMAT and several others. We will demonstrate how these V functions are useful not only in reporting but in standardizing the structure of a database |
proc step, vvalue, data dictionary, proc contents, proc sql | source | 5 |
| 411 | What's New in SAS Data Management | Malcolm Alexander, Nancy A. Rausch |
The latest releases of SAS Data Integration Studio and DataFlux Data Management Studio provide an integrated environment for managing and transforming your data to meet new and increasingly complex data management challenges. The enhancements help develop efficient processes that can cleanse, standardize, transform, master, and manage your data. Latest features include capabilities for building complex job processes; new web-based development and job monitoring environments; enhanced Extract/Load/Transform (ELT) transformation capabilities; big data transformation capabilities for Hadoop; integration with the SAS LASR platform; enhanced features for lineage tracing and impact analysis; and new features for master data and metadata management. This paper provides an overview of the latest features of the products and includes use cases and examples of the product capabilities | data dictionary, data validation rules, web service, big data, data integration, data governance, proc step, data cleaning | source | 17 |
| 412 | What?s New in SAS Enterprise Business Intelligence for SAS 9.3 | Rick Styll | SAS Enterprise BI Server provides a comprehensive suite of BI tools that enables a broad set of business and IT users to produce and consume consistent, fact-based information. The latest revision contains enhancements to both SAS Web Report Studio and SAS BI Dashboard. Key capabilities are discussed and demonstrated by members of the product team. Designing reports and dashboards is now more flexible, and downstream consumers benefit from better performance, improved navigation and interactions, and better integration with EXCEL and your e-mail client. Plans for future releases are previewed, such as mobile delivery of SAS Web Report Studio reports, and how SAS Enterprise BI Server fits within the overall BI portfolio |
line chart, excel, business intelligence, sas enterprise guide, txt, data mining | source | 24 |
| 413 | Where Should I Dig? What to Do Before Mining Your Data | Stephanie R. Thompson | Data mining involves large amounts of data from many sources. In order to successfully extract knowledge from data, you need to do a bit of work before running models. This paper covers selecting your target and data preparation. You want to make sure you find golden nuggets and not pyrite. The work done up front will make sure your panning yields results and is not just a trip down an empty shaft | data mining, nominal variable | source | 3 |
| 414 | Whirlwind Tour Around SAS Visual Analytics | Anand V. Chitale, Christopher Redpath |
SAS brings a revolutionary approach for analytical based data visualization, exploration and reporting. Come and join us for a whirlwind tour through SAS Visual Analytics 6.1 inspired by customer experiences and use cases. We start with an overview of the technology concept behind SAS VA and enter into the Hub, leading to various destinations during the tour. From Hub we will do multiple trips out to applications starting with the Data Builder followed by the Explorer, Designer and end the journey with Mobile integration. There will be a sneak peek into world of Microsoft Office and don't worry, SAS coders are not left behind. Final stop will be Enterprise Guide showing you how you can leverage the technology behind SAS Visual Analytics through SAS code | excel, business intelligence, analytics platform, sas enterprise guide, sas visual analytics | source | 11 |
| 415 | Why the Bell Tolls 108 times? Stepping Through Time with SAS | Peter W. Eberhardt | For many SAS programmers, new or even advanced, the use of SAS date and DATETIME variables is often very confusing. This paper addresses the problems that the most of programmers have. It starts by looking at the basic underlying difference between the data representation and the visual representation of date, DATETIME and time variables. From there it discusses how to change data representations into visual representations through the use of SAS formats. Since date manipulation is core to many business process, paper also discusses date arithmetic first by demonstrating the use of simple arithmetic to increment dates; then by moving on to SAS functions which create, extract and manipulate SAS date and DATETIME variables. Finally, paper demonstrates the use of the %sysfunc macro function and the %LET statement to present date, DATETIME and time variables. This paper is introductory and focuses on new SAS programmers, however, some advanced topics also covered |
sysdate9, date variable, time variable, datetime variable, macro variable, datetime, sas programmer, sas function, time, proc sql, character variable, relational database, format statement, macro function | source |
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| 416 | Wide-to-Tall: A Macro to Automatically Transpose Wide SAS Data into Tall SAS Data | James R Brown | If your SAS world involves forecasting or other date-specific data, you have probably seen column names such as forecast_19224, sales_19230, or inventory_19250. If several of these prefixes exist in a single file, the underlying SAS data file could have thousands of columns. Analyzing this data is an exercise in scrolling, note-taking, copying and pasting. PROC TRANSPOSE is not sophisticated enough take on this challenge. This paper presents a macro which will transform your data by automatically creating a CSV file with distinct columns for the date, each prefix variable, and any non-date-suffixed columns in your input. The non-wizardry behind this makes use of the dictionary tables, SAS name lists (forecast_18950-forecast_19049), and colon notation (forecast_:) to eliminate the task of enumerating long lists of variable names |
dataset, dictionary tables | source | 10 |
| 417 | With a Trace: Making Procedural Output and ODS Output Objects Work For You | Louise S. Hadden | The Output Delivery System (ODS) delivers what used to be printed output in many convenient forms. Wh at most of us don't realize is that "printed output" from procedures (whether the destination is PDF, RTF, or HTML) is the result of SAS packaging a collection of items that come out of a procedure that most people want to see in a predefined order (aka t emplate.) With tools such as ODS TRACE, PROC CONTENTS and PROC PRINT, this paper explores the many buried treasures of procedural output and ODS output objects and demonstrates how to use these objects to get exactly the information that is needed, in exactly the format wanted |
proc step, proc contents, estimate, proc means, sample, data step, output delivery system | source |
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| 418 | Working with a Large Pharmacy Database: Hash and Conquer | David Izrael | Working with a large pharmacy database means having to process - merge, sort, and summarize - hundreds of millions of observations. By themselves, traditional methods of processing can lead to prohibitive data processing times that endanger deadlines. The hash object is the fastest and most versatile method in the SAS system of substantially accelerating the processing. In our paper, we apply hash methods to a routine lookup function where one needs to merge the kernel pharmacy database with its satellites. We also present comparatively new non- traditional features of the hash object, such as handling duplicate keys and finding frequency counters. At the same time, we underscore the necessity of traditional sort-and-merge methods, but suggest that they be used carefully | hash table, cpu time | source |
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| 419 | Writing a Useful Groovy Program When All You Know about Groovy Is How to Spell It | Jack Hamilton | SAS is a powerful programming system, but it can't do everything. Sometimes you have to go beyond what SAS provides. There are several built-in mechanisms for doing this, and one of the newest is PROC GROOVY. It sounds like a product of San Francisco's Haight-Ashbury, but it's actually a programming language based on another product with San Francisco Bay area roots, Java. You can think of it as a simplified, easier to use version of Java -- simplified enough that you can put together a useful PROC GROOVY program from Internet examples without knowing anything about the language. This presentation focuses on handling directories and ZIP files, but many other things are possible |
proc groovy, proc step, macro variable, cpu time | source | 11 |
| 420 | Writing Macro Do Loops with Dates from Then to Now | Ronald J. Fehd | macro variable, do loop, macro loop, macro definition, proc print, proc step, %if-%then-%else | source | 13 | |
| 421 | You?re Invited! Learn How SAS Uses SAS Software to Invite You to SAS Global Forum | Lori Jordan, Shawn Skillman |
Discover how you are chosen to receive an invitation to SAS Global Forum. This paper explores the use of SAS software, including SAS Marketing Automation, SAS Enterprise Guide, SAS Enterprise BI (Business Intelligence) and SAS DataFlux Data Management in the selection process. See how SAS Marketing uses strategic segmentation practices and advanced analytics to target e-mail communications and improve list performance for SAS Global Forum | data mart, sas enterprise guide | source | 12 |
| 422 | You?ve Got SASMAIL! A Simple SAS Macro for Sending e-Mails | Rajbir Chadha | As SAS software programs become complex, processing times increase. Sitting in front of the computer, waiting for the program to finish isn't something that everyone enjoys. This paper talks about a way to have SAS send out automatic e-mails once the process finishes and include the log, output or reports as an attachment. This function gives users the freedom to work on other pressing tasks rather than constantly checking the SAS process log for completion. The SASMAIL custom macro function combines the functionality of FILENAME EMAIL, SAS Macros and the SQL Procedure to deliver the intended results. It sends out an e-mail with a copy of the log (when available) as an attachment providing users with the flexibility to access required information even when mobile. SASMAIL detects the SAS session you are running, whether DMS or BATCH, and selects the attachment options accordingly |
stored macro, macro facility, autocall macro, excel, macro function, options statement, proc step, ods html, sas log | source | 11 |
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