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BUGSSESUG 2017 Papers - Index
149 papers. Source: SESUG (SouthEast SAS Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | A Data-Driven Approach for Generating Define.xml v2.0 using ADaM Specifications and ADaM Datasets | Jaskaran Sing Gill, Hima Bhatia, ICON Plc |
Case Report Tabulation Data Definition Specifications (CRT-DDS) commonly known as Define.xml is an integral part of FDA's electronic submissions. Define.xml facilitates the review process by providing information about the structure and content of standard clinical data (SDTM and ADaM) in a machine readable format. There are two versions of define.xml v1.0 and v2.0 available. In March 2016, FDA announced to end support for Version 1.0 of define.xml and mandate the use of version 2.0 for all the electronic submissions. This paper presents a data-driven approach for generating define.xml v2.0 using ADaM ADS specifications and ADaM datasets. A Define ready ADaM ADS specification plus ADaM datasets are passed into a macro that automatically generates define.xml v2.0. This macro also provides an error report for any inconsistencies identified between ADaM datasets and ADaM specifications | adam dataset, proc contents, length statement, cdisc | source | 8 |
| 2 | A Hands-on Introduction to SAS DATA Step Hash Programming Techniques | Kirk Paul Lafler | SAS users are always interested in learning techniques that will help them improve the performance of table lookup, search, and sort operations. SAS software supports a DATA STEP programming technique known as a hash object to associate a key with one or more values. This presentation introduces what a hash object is, how it works, the syntax required, and simple applications of it use. Essential programming techniques will be illustrated to sort data and search memory-resident data using a simple key to find a single value |
hash table, proc transpose, lookup, proc step, sas log, composite key, dataset, proc sort, sas base | source | 12 |
| 3 | A Macro for Generating the Adverse Events Summary for ClinicalTrials.gov | Andrew Moseby, Maya Barton |
In the clinical trials industry, the website ClinicalTrials.gov serves as a publicly accessible outlet of information on trial outcomes. Once a federally sponsored trial concludes, the study team is responsible for publishing adverse events data to ClinicalTrials.gov. These data must be deli vered in a very particular structure, uniform across all studies. This uniformity allows for generalization via a SAS macro. The purpose of this paper is to introduce such a macro. Macro variables offer flexibility to account for study - specific characteristics such as number of treatment groups and variable naming conventions. Input datasets may be modified in the work directory in cases where the macro variables do not accommodate a particularly non-standard input dataset | system organ class, randomization, adverse events, adverse event summary, in=, macro variable | source | 7 |
| 4 | A macro of building predictive model in PROC LOGISTIC with AIC-optimal variable selection embedded in cross-validation | Hongmei Yang | Logistic regression leveraging stepwise selection has been widely utilized for variable selection in health care predictive modeling. However, due to the drawbacks of stepwise selection, new ideas of variable selection are emerging, including Akaike Information Criterion (AIC)-optimal stepwise selection which utilizes AIC as the criterion for variable importance and builds a model based on a combination of stepwise logistic regression and information criteria. As predictive factors selected over a single sample may over fit the sample and have poor prediction capability on independent test data, embedding variable selection in resampling techniques, such as cross-validation, is recommended to appropriately estimate expected prediction error, especially with a limited sample size. When processing the AIC-optimal selection through cross-validation, different lists of influential variables may be selected over the iterations. Simply averaging the coefficients would yield a final model with many more predictors than necessary, and therefore reduced predictive accuracy. This paper proposes additional steps to address this issue. Variables selected in the AIC-optimal stepwise process are ranked by their frequency appearing in the AIC-optimal lists obtained from cross-validation iterations. A final model is obtained by sequentially adding the variables with the same frequency until an optimal averaged area under the Receiver Operating Characteristic curve (AUC) is achieved. We present the algorithm and the macro used to achieve the selection in the context of cross-validation. Intended audience: SAS users of all levels who work with SAS/STAT and PROC LOGISTIC in particular. |
data cleaning, feature selection, logistic regression, logistic distribution, stepwise selection, regression analysis, proc freq, sample, macro variable, macro definition | source | 7 |
| 5 | A Practical Application of VTABLE | Kannan Deivasigamani, Sumathi Subramaniam |
You may have read about "Dictionary.Tables", which is one of the available sources of information available for developers to take advantage of Meta data based on different requirements but this paper is talks about a simple but powerful application with an example. Several articles have been published in the past many years but this paper provides a practical example of how it is used in some financial transaction processing in some leading banks. We have some processes built on a requirement that looks for transactions recorded in the past 30 days or past 10 days depending on the business requirement. They can be based on files that may have been on an irregular schedule. This calls for a way to look up the creation date of the file within SAS. Fortunately, SAS provides session tracking variables that are available through Vtable which is available through SASHELP library. This not only stores information about SAS files that are part of the standard SAS libraries, but also stores information from a users current SAS session and libraries defined within the program and file names defined within the program. The columns those are available for reading provides useful information that allows a user to operate with much flexibility. In this paper, I show example code of how the files are created and the corresponding Meta data is stored in the SASHELP.Vtable Meta data dataset and how it can be retrieved to dynamically build code that can further be used in subsequent processing. This is just a tip of the iceberg but it is up to the user to take advantage of the available information to the best use as deemed appropriate. | dictionary tables, dataset | source | 6 |
| 6 | A SAS macro replacement for Dynamic Data Exchange (DDE) for use with SAS grid computing | Saki Kinney, David Wilson, Benjamin Carper |
The ability to write to specific cells of EXCEL workbooks is an important feature of DDE that users lose when migrating to grid computing. SAS Add-In for Microsoft Office requires programming Visual Basic and complicates quality control. To replace DDE functionality we have developed a set of macros which use PROC IML to integrate functionality of R packages XLConnect and openxlsx for working with EXCEL workbooks. This paper describes the macros, and their performance and implementation with SAS grid computing |
excel, xls, sas grid, grid computing, proc iml, macro quoting function, macro definition, proc step, macro variable, macro loop | source | 9 |
| 7 | A Second Look at the ODS Destination for PowerPoint | Tim Hunter | This paper demonstrates how to use the ODS destination for PowerPoint to create attractive presentations from your SAS output. Packed with examples, this paper gives you a behind-the-scenes tour of how ODS creates Microsoft PowerPoint presentations. You get an in-depth look at how to customize the ODS PowerPoint style templates that control the appearance of your presentation. With this information you can quickly turn your SAS output into an engaging and informative presentation. This paper is a follow-on to the SAS Global Forum 2013 paper "A First Look at the ODS Destination for PowerPoint." |
sas output, proc step, proc template, output delivery system, footnote statement, options statement | source | 28 |
| 8 | A Software Toolkit for Data Management | Kim Chantala, Jean Robinson, Helen Smith |
RTI has developed a suite of data tools to help keep data preparation on schedule and within budget. This toolkit provides a remarkably easy, low-cost way to create codebooks, master lists of SAS data sets for a project, reports of variables needing special investigation, and data crosswalks showing the relationship of variables across datasets. Traditionally, these documents are produced at the end of a project with a great deal of programming or are manually produced, but our tools allow the programmer to seamlessly create these documents at any time during the data preparation task. We have found that producing these documents early in data collection improves data quality and communication between the data collection team and client. This toolkit provides a comprehensive way to document and review not only datasets you create, but also datasets that you receive, especially if they do not have good documentation | dataset, numeric variable, proc format, missing value, character variable, proc step, user-defined format, proc freq, label statement, sysdate9, time., format statement | source | 19 |
| 9 | A Tutorial on the SAS Macro Language | John J. Cohen | The SAS Macro language is another language that rests on top of regular SAS code. If used properly, it can make programming easier and more fun. However, not every program is improved by using macros. Further-more, it is another language syntax to learn, and can create problems in debugging programs that are even more entertaining than those offered by regular SAS. We will discuss using macros as code generators, saving repetitive and tedious effort, for passing parameters through a program to avoid hard coding values, and to pass code fragments, thereby making certain tasks easi-er than using regular SAS alone. Macros facilitate conditional execution and can be used to create program modules that can be standardized and re-used throughout your organization. Finally, macros can help us create interactive systems in the absence of SAS/AF. When we are done, you will know the difference between a macro, a macro variable, a macro statement, and a macro function. We will introduce interaction between macros and regular SAS language, offer tips on debugging macros, and discuss SAS macro options. |
macro variable, macro definition, macro facility, macro loop, macro call, do loop, macro function, title statement, macro put, %if-%then-%else | source | 10 |
| 10 | Advanced Programming Concepts: History of the List Processing and Cardinality Ratio Memes | Cardinality Ratio Memes | A statement in a natural language contains three parts: subject, verb and object. A statement in a computer language contains only two parts: verb and an object; the subject, or actor, is the computer's operating system. The predecessor of the meme list processing is the computer language LISP, in which every statement is a function call and the object is a list. The list processing paradigm of programming contains these steps: 1. identify an object, an item 2. write a function, process, or procedure for an item 3. prepare a list, a list of items 4. use a loop on the list, to process each item. This paper reviews the author's development of the concept of list processing and its implementation in SAS software. The purpose of this exposition is to highlight the author's papers published on these topics and to provide a critique of earlier ideas. This is accomplished by a review of the development of processes for calculating the cardinality ratios of the variables in a data set. | proc step, macro variable, proc sql, create table, macro definition, proc contents, macro call, select, proc freq, call execute, macro loop, %if-%then-%else | source | 29 |
| 11 | Advanced Project Management beyond Microsoft Project, Using PROC CPM, PROC GANTT, and Advanced Graphics | Stephen B. Sloan | The efficient use of space can be very important when working with large SAS data sets, many of which have millions of observations and hundreds of variables. We are often constrained to fit the data sets into a fixed amount of available space. Many SAS data sets are created by importing EXCEL or Oracle data sets or delimited text files into SAS and the default length of the variables in the SAS data sets can be much larger than necessary. When the data sets don't fit into the available space, we sometimes need to make choices about which variables and observations to keep, which files to zip, and which data sets to delete and recreate later. There are things that we can do to make the SAS data sets more compact and thus use our space more efficiently. These things can be done in a way that allows us to keep all the desired data sets without sacrificing any variables or observations. SAS has compression algorithms that can be used to shrink the space of the entire data set. In addition, there are tests that we can run that allow us to shrink the length of different variables and evaluate whether they are more efficiently stored as numeric or as character variables. These techniques often save a significant amount of space; sometimes as much as 90% of the original space is recouped. We can use macros so that data sets with large numbers of variables can have their space reduced by applying the above tests to all the variables in an automated fashion. |
proc step, proc sql, excel | source | 9 |
| 12 | Advanced project management, beyond Microsoft Project, using PROC CPM and Gantt and Advanced Graphics | 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, proc sql, excel | source | 9 |
| 13 | An Introduction to Visit Window Challenges and Solutions | Mai Ngo | In clinical trial studies, statistical programmers often face the challenge of subjects' visits not occurring on the exact scheduled visit dates. As a result, visit windowing is often needed for analysis purposes. This paper aims to provide a general introduction to visit window programming. I present some different scenarios where visit windowing is needed and sample SAS codes for each of these cases. I also discuss strategies for applying the visit window rules to multiple programs with different visit frequencies and window lengths and cover validation strategies in these cases | study day, clinical trial, raw data, visit, clinical data, data cleaning, data science, proc sql, adam dataset, cdisc | source | 6 |
| 14 | An Obvious Yet Helpful Guide to Developing Recurring Reports in SAS | Rachel Straney | Analysts, in particular SAS programmers, are often tasked with compiling data and generating reports at a moment's notice. It is paramount to make report generation efficient for ease of replication. The method of preparing and compiling recurring reports can be made easier when the process of reporting is well - defined. The objectives for this paper are to outline a checklist that can guide the creation of a reporting process for your data. SAS capabilities like labels and formats as well as the SAS Macro F acility play an important role in automating a reporting process. Identifying SAS procedures ahead of time, like PROC PRINT or PROC TABULATE, and developing a program that is flexible and adaptable can save time. General steps to successfully develop a reporting process will be described and detailed examples to show how to achieve this will be provided |
macro definition, macro variable, sas enterprise guide project, proc step, macro function, macro facility, proc tabulate, ods pdf, sas output, excel | source | 11 |
| 15 | Analysis of Tweets on Demonetization in India using SAS Enterprise Miner | Jital Patel, Narmada Deve Panneerselvam |
The citizens of a country often face the benefits or the brunt of various policies adopted by the government. Social platforms then become sounding boards for them to express their displeasures or concerns for the matter at hand. One such policy that sent Twitter into a frenzy was the demonetization order that was announced rather abruptly by the Indian Prime Minister on November 8th 2016 without any form of prior intimation to public. In this paper, we have analyzed the tweets that helped us recognize whether demonetization was perceived positively or negatively by the citizens. About 15 days after the demonetization decision was announced, a dataset of 8,000 tweets spanning two days, was collected from a publicly available data source. Using the commonly used terms in the tweets and studying the strength of their relations using concept links, in general, show a positive feedback to the demonetization implementation policy. Using text clustering and text topic to group people with similar thoughts based on the tweets, reveal that demonetization was positively supported by a large number of people. Thus, our overall analysis showed that a vast majority of Indians accepted the demonetization policy positively while some of them expressed their displeasure over it. | parsing, proc freq, excel, data mining | source | 19 |
| 16 | Applying IFN and IFC Functions | Jinson J. Erinjeri | The SAS IFC and IFN functions are helpful in compact as well as in elegant coding. Both IFC and IFN functions can be employed in Base SAS and PROC SQL as an alternative to the traditional IF THEN ELSE, SELECT END and SELECT CASE statements. The paper presents the applications of IFN and IFC functions in various scenarios along with a peek at efficiency comparison s of some of the traditional approaches. Also, presented are pitfalls using these functions and the ways around the pit for a successful run |
cpu time, ifc, ifn, missing value, numeric constant, proc sql, proc step, character constant, if-then-else | source | 7 |
| 17 | Applying JMP's Imaging Analytic Tools to Target Prostate Cancer Tumors that Guide External-Beam Radiation Therapy | Melvin T. Alexander, Social Security |
The motivation of this paper came from questions I received on my SESUG 2016 presentation. There, I showed how John Ponte's JMP Image Column Analyzer add-in could select frames of MRI prostate cancer images (saved in an Expressions column). The selected frames showed special markers near the prostate. These markers identified the location where to aim radiation beams that will kill the cancer cells without harming healthy cells. Selected frames with the markers were output to JMP data tables representing the image data. I appended the marker-frame data tables into a larger JMP data table. In this presentation, I compute kernel density estimates (non-parametric measures) of the marker's intensity values from the Bivariate Analysis >Nonpar Density platform. Contour lines with the largest density of marker's intensity values provide useful metrics for further data analysis and exploration | dataset, bivariate analysis | source | 12 |
| 18 | Applying Logistics Regression to Forecast Annual Organizational Retirements | Alan Dunham | This paper briefly discusses the labor economics research about employee decisions to retire from corporations and government organizations, and then shows how that reasoning can be used for practical forecasting by applying logistics regression. A test dataset is generated and stochastically perturbed for the forecasting exercise. The SAS PROC LOGISTICS procedure is used to forecast the probabilities of annual individual retirements. Given the binomial nature of the retirement decision, the probabilities can be summed to obtain annual total retirement forecasts. Theoretical underpinnings, test data, code, and |
logistic regression, logistic distribution, regression analysis, predictor variable, proc logistic, proc step, binomial distribution, maximum likelihood estimation, confidence interval, proc reg, likelihood function | source | 12 |
| 19 | Automate Secure Transfers with SAS and PSFTP | Kyle Thompson | PSFTP, or PuTTY SFTP offers a way to securely send files between computers. SAS can be used to automate this secure transfer of files with the versatile and encrypted SSH-2 scheme via the free, open-source PuTTY application. After reviewing the use of the command line interface and associated code in SAS, we describe PuTTY commands that can be used for single-line execution or coupled together into batch submission within a SAS program running in Windows. From there, we offer examples of SAS code which can be used in whole or in part to integrate and automate PuTTY's use within a SAS program. | sysrc, txt | source | 8 |
| 20 | Automated Validation of Complex Clinical Trials Made Easy | Richann J. Watson | Validation of analysis datasets and statistical outputs (tables, listings, and figures) for clinical trials is frequently performed by double programming. Part of the validation process involves comparing the results of the two programming efforts. COMPARE procedure output must be carefully reviewed for various problems, some of which can be fairly subtle. In addition, the program logs must be scanned for various errors, warnings, notes, and other information that might render the results suspect. All of this must be performed repeatedly each time the data is refreshed or a specification is changed. I n this paper, we describe a complete, end-to-end, automated approach to the entire process that can improve both efficiency and effectiveness |
proc compare, proc step, dataset, macro variable, data cleaning, sas output, sample, where statement, independent qc, proc report, macro parameter, macro call, macro definition | source | 35 |
| 21 | Automatic Verification of Combined Datasets | Joe Matise | One common task for a SAS programmer is to combine datasets from different sources, perhaps from multiple rounds of a study or from different panels in a survey. When combining these datasets, it is important to ensure that the final dataset(s) have the correct combination of rows, both that they have all of the expected rows from each source dataset and that the rows from the source datasets are not in conflict. We present a solution for automatically verifying a combined dataset. We verify that every row in the source datasets has a matching row in the combined dataset, that every row in the combined dataset comes from a unique source, that the ID variable in the final dataset is unique, and provide several examples of possible logic checks that can be added to the application depending on needs, such as verifying that the study round matches the dataset source. The intended audience for this presentation is an intermediate programmer. We use hash tables in this application, but present them in sufficient detail for a programmer with no background in hash tables to understand their purpose and use. | hash table, data merging | source | 9 |
| 22 | Backward Variable Selection for Logistic Regression Based on Percentage Change in Odds Ratio | Evan Kwiatkowski | Variable selection is a fundamental component of statistical modeling. A common variable selection method used in health sciences is backward variable selection, which iteratively removes variables based on their relevance to the model. Often, automated backward variable selection procedures determine variable relevance based on overall statistical significance. However, many epidemiologists, including formative thinkers Greenland and Robins, favor a "change-in-estimate" approach to variable selection rather than an overall significance approach. We developed a SAS software macro to implement a backward variable selection procedure for logistic regression using the "change-in-estimate" method. Our macro implements backwards variable selection in the logistic regression model in the situation where there is a single independent variable (IV) and single dependent variable (DV) of interest, with additional covariates that are eligible for removal based on their relevance to the model | feature selection, odds ratio, logistic regression, regression analysis, logistic distribution, response variable, predictor variable | source | 6 |
| 23 | Beginner Beware: Hidden Hazards in SAS Coding | Alissa Wise | New SAS programmers rely on errors, warnings, and notes to discover coding issues. However, it is important to note that some coding issues may be hiding in plain sight. Herein are a few examples of these issues including incomplete comparisons and inadvertently truncating variables with the IMPORT procedure. The explanations provided are meant to assist new SAS programmers navigate these hazards so that results are clean and programs run more efficiently |
proc step, proc compare, by group processing, proc sort, merge statement, proc import | source | 7 |
| 24 | Behind the Scenes: from Data to Customized Swimmer Plots Using SAS Graphical Template Language (GTL) | Rita Tsang | A Swimmer plot is a useful visual tool to assess treatment efficacy for individual subjects in oncology clinical trials. These efficacy endpoints may include tumor response evaluation over time as measured by Response Evaluation Criteria in Solid Tumors (RECIST), or percent change from baseline in target lesions for measurable disease as defined by the presence of at least one measurable lesion. These endpoints are always juxtaposed with other valuable information, such as treatment doses, duration of treatment, primary tumor type, and treatment status to create a compelling visual presentation. There are many industry publications describing how to create swimmer plots in SAS. They provide a useful framework for using either SGPLOT or GTL to generate swimmer plots. This paper will build on the flexibility of SAS GTL to go further behind the scenes to illustrate how to prepare the data structure and variables for a customized swimmer plot. Focus will be placed on using dynamic programming for addressing missing data. The SGRENDER procedure will be used to create the graph based on the prepared data set and the graph template created in PROC TEMPLATE. |
swimmer plot, proc template, proc step | source | 16 |
| 25 | Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS Code | Joshua M. Horstman | Nearly every SAS program includes logic that causes certain code to be executed only when specific conditions are met. This is commonly done using the IF…THEN…ELSE syntax. In this paper, we will explore various ways to construct conditional SAS logic, including some that may provide advantages over the IF statement. Topics will include the SELECT statement, the IFC and IFN functions, the CHOOSE and WHICH families of functions, as well as some more esoteric methods. We'll also make sure we understand the difference between a regular IF and the %IF macro statement |
if-then-else, subsetting if, where statement, ifn, select, ifc, macro definition, sas programmer, %if-%then-%else, missing value, proc format, whichn, execution phase, macro parameter, when, proc step, macro facility | source | 10 |
| 26 | Beyond the Standard Map Alternate Methods for Visualizing Location Data | Seth W. Hoffman | The bulk of a data scientist's job is data preparation and cleaning. However, presenting the findings in a compelling manner is what generates speaking engagements. "A Map!" is the obvious answer for visualizing location data. Though the definition is quite broad, most people picture a general reference map, or maybe a heat map if it is the middle of summer. This paper uses PROC GMAP as the drawing tool to explore some less traditional metaphors and ideas for displaying analysis results which contain a spatial component |
inner join, create table, proc gmap, sample, proc step | source | 10 |
| 27 | Binning of Predictors for the Cumulative Logit Model | Bruce S. Lund | Binning of a predictor is widely used when preparing a predictor for use in binary logistic regression. In this paper the practice of binning is extended to the cumulative logit model. Here the target has more than 2 levels. A binning algorithm and SAS code is presented. The user may choose to bin a predictor X so that the ordering of X is maintained. In this case, for each k for 2 ≤ k ≤ L (where L= number of levels of X), the algorithm can find the k-bin solution which is optimal with respect to log-likelihood or to a generalized information value (IV). The optimal solution is found by complete enumeration of all solutions. Additionally, the algorithm can find if there is an optimal k-bin solution which is monotonic versus the empirical cumulative logits of the target. Alternatively, the user may choose to bin X without regard to the ordering of X. In this case the algorithm maximizes log-likelihood or generalized IV at each step in the binning process but an optimal k-bin solution is not guaranteed. A generalization of weight of evidence (WOE) is defined and SAS code for WOE coding is generated. Examples are presented. | logistic regression, logistic distribution, proc logistic, likelihood function, proc step, regression analysis, sample, binary variable, sample size, response variable, class variable, macro call, correlation, macro variable | source | 20 |
| 28 | Building an Out-of-Pocket Cost (OOPC) Calculator for Medical Expenses using the Medical Expenditure Panel Survey (MEPS) | Barry Merriman | Building an Out-of-Pocket Cost (OOPC) Calculator for Medical Expenses using the Medical Expenditure Panel Survey (MEPS) Background: It can be difficult for consumers buying health insurance on the health insurance marketplace to compare plans. Aside from different premiums plans can have different cost-sharing characteristics: copayment, coinsurance, deductible, or maximum out-of-pocket payment. In addition, copayments and coinsurance can vary across different types of services and even drugs. Having a way to estimate out-of-pocket costs (OOPC) would help consumers compare plans and make it easier to choose a suitable plan. There is an OOPC calculator on healthcare.gov, the federally-facilitated marketplace. However, it is based on data from a commercial claims database and is expensive. I sought to find a suitable data source that is publicly available and is free. Any resulting calculator could be used in any marketplace including state-based. Methods: In order to estimate OOP costs you first need to estimate utilization and associated costs. Then you apply the plan cost-sharing characteristics to each of the healthcare events to estimate patient responsibility or the OOPC. I used MEPS utilization data from 2013 for the under 65 population to create healthcare domains, for example, inpatient facility charges, primary care visits, or outpatient surgery. Patients were grouped by age and sex and further divided into low, medium, and high groups according to the cost of their healthcare. Plan cost-sharing characteristics are applied to the costs associated with typical utilization within each domain for each age/sex/low/medium/high group to give an OOPC estimate for each group. Results: If the tool is used it will be in the background of a web interface where customers shop for health insurance. Also in the background will be a dataset with plan cost-sharing characteristics. The customer will input age/sex for all family members to be insured. The estimated OOPC is calculated for any plan the customer selects. The customer can combine the OOPC with plan premiums to compare total costs across plans. Conclusion: The OOPC calculator based on MEPS data yields comparable results to a calculator based on commercial claims and is therefore a viable alternative that is free. | health insurance, sample | source | 5 |
| 29 | Building Heat Maps for Data Cleaning and Beyond | Kristen Much, Nicole Bader |
A heat map is a graphical representation of data that translates data values into colors within a matrix. This type of data visualization summarizes a vast amount of data within a single snapshot which helps to quickly communicate relationships between data values. Using SAS, a heat map can be generated easily with the HEATMAPPARM statement in either the SGPLOT/SGPANEL procedures or Graph Template Language (GTL). The focus of this paper is to explore the syntax of the HEATMAPPARM statement within both SGPLOT/SGPANEL and GTL as well as to walk through a direct application of a heat map to be used in preparation for database lock. The example demonstrates how to develop a heat map that indicates the status of all case report forms (CRFs) within a database across all visits for all subjects. When used effectively, this tool can greatly enhance understanding within a clinical team regarding the status of requirements for a successful database lock |
heatmap, database lock, graph template language, clinical trial | source | 12 |
| 30 | Building Sequential Programs for a Routine Task with Five SAS Techniques | Gongmei Yu, Paul A. LaBrec |
When a task needs to be implemented on a regular basis and has a tight timeline, the SAS programs should be designed with minimum required updates to the code and maximum automation in each processing step. This paper intends to illustrate the process of developing such programs with five SAS techniques: (1) Macro variable; (2) Macro program; (3) Conditional and iterative statement; (4) %INCLUDE statement; (5) DDE (Dynamic Data Exchange) outputting. This paper assumes basic knowledge of SAS procedures, macro language and the use of program logic. The emphases of the paper is to illustrate how the five techniques can be applied to the specific task of calculating healthcare payment weights. The code may include other techniques not addressed in this paper |
macro variable, excel, do loop, macro loop, macro definition, %if-%then-%else | source | 11 |
| 31 | Can A SAS Programmer Stay Cool | Jiangtang Hu | The traffic of SAS mailing list is keeping decreasing. There are less active SAS bloggers, including myself. It seems everyone is talking about the coolness of R, Python, machine learning or deep learning. The other side is, SAS as a software and a programming language is getting better. As a long time and continuous SAS heavy user, I'm asking: can a SAS programmer stay cool? Furthermore, can a SAS programmer like me cool? It's not a silly question. Lots of SAS programmers feel the crunch. In this paper, I will talk about the challenges a SAS programmer can face. The challenges can come from external competitors like R and Python, technology trend like machine learning and deep learning. I will talk about in which SAS is good at, while in which not. In the end, I will explore some how-to for a SAS programmer to stay alert, marketable, and even more, cool. I will also encourage SAS programmers to keep update with SAS software; Lua, Python, machine learning and other cool stuff are already imbedded in SAS system! | sas programmer, proc step, machine learning, sas programming, sas viya, proc sql, deep learning, proc groovy, data science | source | 6 |
| 32 | Challenges and Solutions for Handling Re - screened Subjects in SDTM | Charity Quick, Paul Nguyen |
A common issue with SDTM is tabulating data for subjects who enroll multiple times in a single trial. Currently, the FDA advises that as long as there is one USUBJID for each specific subject, a different subject identifier (SUBJID) value can be used for each screening attempt. However, it can be challenging to handle this data while conforming to both the SDTM Implementation Guide and FDA-published validation rules. This paper will detail how to deal with subjects who have multiple screening visits while conforming to SDTM and FDA rules. The intention is to produce high quality data while maintaining traceability with the multiple screening visits. We will look at how to structure the Demographic Domain (DM) and the accompanying supplemental qualifiers data set accordingly. Furthermore, we explore how to identify additional domains that could be affected by re-screening | medical history, raw data, sdtm domain, cdisc | source | 6 |
| 33 | Churn the Data Around - A Machine Learning Approach to Understanding Why Customers Leave | Sean Ankenbruck | Customer attrition is a common problem that affects companies across a variety of industries. Companies value profitable customers that are consistent and engage in long-term interactions that contribute to a high customer lifetime value. Identifying and understanding the factors that influence customer churn allows companies to gain a competitive advantage. This paper discusses a data-driven approach to identify important factors that influence churn and classify individuals based on their predicted likelihood to churn. It also provides a visual analysis of churn rates across various customer groups. Depending on whether the goal is purely accuracy, or a correct classification, there are several methods that can be used to identify customer churn. The entire process is accomplished using SAS tools such as SAS Visual Analytics with SAS Visual Data Mining and Machine Learning (VDMML) | churn, proc hpsplit, feature importance, neural network, logistic regression, logistic distribution, regression analysis, proc freq, attrition, raw data, response variable, sample | source | 9 |
| 34 | Clinical Data Visualization using TIBCO Spotfire and SAS | Ajay Gupta | In Pharmaceuticals/CRO industries, you may receive requests from stakeholders for real-time access to clinical data to explore the data interactively and to gain a deeper understanding. TIBCO Spotfire 7.6 is an analytics and business intelligence platform, which enables data visualization in an interactive mode. Users can further integrate TIBCO Spotfire with SAS (used for data programming) and create visualizations with powerful functionality e.g. data filters, data flags. These visualizations can help the user to self-review the data in multiple ways and will save a significant amount of time. This paper will demonstrate some basic visualizations created using TIBCO Spotfire and SAS using raw and SDTM datasets. This paper will also discuss the possibility of creating quick visualizations to review third party vendor (TPV) data in formats like EXCEL and Comma Separated File (CSV) |
bar chart, horizontal bar chart, box plot, adverse events, order by, vital signs, dataset, data visualization, laboratory data, excel, cdisc | source | 12 |
| 35 | Creating a DOS Batch File to Run SAS Programs | David W. Franklin | We often have many SAS programs to run in a directory. While it is possible to run each individually, it is better if a DOS Batch file be created with the list of programs being run and the order in which they are run. This paper looks at a SAS macro that will take the list of SAS programs in a directory, as specified by the user, and create a DOS Batch file to which can then be run to run all the SAS programs. Also presented will we a small SAS program that you can run at the end of the program to send you an email saying when the programs had finished, and whether there are any issues in the SAS LOG to review! | sas log, macro definition | source | 4 |
| 36 | Data Analysis and Storytelling - Communicating Analytical Results with Clarity, Precision and Efficiency | Kirk Paul Lafler | The data analysis process involves the gathering and collection, cleansing, transforming, and modeling of data from various sources. The purpose is to discover, evaluate, understand and derive useful information from the data to support decision-making. Unfortunately, and all too often, data analysts omit a very crucial step - the development of a narrative, or story, of the data analysis process and outcome. This omission not only fails to bring context, insight and interpretation of the data analysis results to stakeholders, it neglects to bring meaning, relevance and interest to the "key" points of the data analysis results. This presentation describes the importance, considerations and steps needed in developing a compelling narrative, along with the necessary visual analytics, to communicate a convincing point-of-view to help persuade others to understand the complexities associated with the data analysis results | proc step, data science, data visualization, proc sgplot, vertical bar chart, bar chart, histogram, proc freq, proc sort, sas base, proc means | source | 10 |
| 37 | Data movement issues: Explicit SQL Pass-Through can do the trick | Kiran Venna | Data movement between Teradata and SAS will have huge impact on run time of SAS ETL Job. Usage of explicit SQL Pass-Through will reduce data movement and will also improve the query performance. Proper ETL planning by using explicit SQL Pass-Through whenever it is possible will be very helpful in attaining required results by using optimal resources. explicit SQL Pass-Through can also enhance execution of SAS macros, when a Teradata Table is used to create a SAS data set. This paper presents few case studies related to design of explicit SQL Pass-Through in ETL and SAS Macros. Issues with explicit SQL Pass-Through with SAS macro variables and also with Remerging summary statistics with data are presented along with solutions | sql pass-through, proc sql, dataset, macro variable, implicit sql pass-through, macro definition, sas programmer, libname statement, proc means, create table, data cleaning, proc step | source | 8 |
| 38 | Data Quality Control: Using High Performance Binning to Prevent Information Loss | Deanna Naomi Schreiber-Gregory, Henry M. Jackson |
It is a well-known fact that the structure of real-world data is rarely complete and straight-forward. Keeping this in mind, we must also note that the quality, assumptions, and base state of the data we are working with has a very strong influence on the selection and structure of the statistical model chosen for analysis and/or data maintenance. If the structure and assumptions of the raw data are altered too much, then the integrity of the results as a whole are grossly compromised. The purpose of this paper is to provide programmers with a simple technique which will allow the aggregation of data without losing information. This technique will also check for the quality of binned categories in order to improve the performance of statistical modeling techniques. The SAS high performance analytics procedure, HPBIN, gives us a basic idea of syntax as well as various methods, tips, and details on how to bin variables into comprehensible categories | outlier, proc step, interval variable, response variable | source | 9 |
| 39 | DE-Identifying Data when Sharing Clinical Data | Pushpa Saranadasa | In any particular clinical study a great deal of information is collected that may be of use in other research endeavors. If analysts other than the originating clinical team review the same data from different perspectives then it is more likely that additional assessments and applications will emerge: New investigational approaches to a compound or treatment, perhaps from safety testing to efficacy testing, may come upon new applications accruing to the benefit of the medical community. The caveat to data sharing is that the data transparency necessary within the originating research team may limit the information to be provided to other public and/or private partners interested in using the data in their research. The sharing of knowledge may involve the patient-level detailed data used in clinical studies. For many reasons, not the least of which is patient privacy, any shared data must first be de-identified | adam dataset, macro definition, cdisc | source | 5 |
| 40 | Decomposing the R-squared of a Regression Using the Shapley Value in SAS | Charles D. Coleman | After creating a linear regression model, quantifying the importance of each variable is often desired, either for academic purposes or to design the most effective interventions. This normally involves decomposing the variance, or, equivalently, . Using the Shapley Value, known in this literature as the LMV, it is possible to do such a decomposition. This decomposition can be applied to models with interactions and is robust against multicollinearity. It has the interpretation that it is the marginal contribution of each variable to . This is implemented in macro %DECOMPOSE_R2 using PROCs GLM, IML, macro string functions and DATA STEP-like macro processing |
predictor variable, regression analysis | source | 11 |
| 41 | Detecting Outlying Data in Proficiency Studies with SAS | Stacey Slone | Regardless of the industry, one of the first steps in analyzing data from a collaborative or a proficiency study is to detect any outliers in the data. Regulations published by the International Organization for Standardization, ISO, enumerate several tests to consider (ISO 5725-2, 1994; ISO/FDIS 13528, 2015). To identify outlying means in the laboratories participating in proficiency study, the ISO regulations suggest the single Grubb's test and the Mandel's h test. Similarly, the ISO regulations propose using the Cochran's test and the Mandel's k test to identify outlying standard deviations among participating laboratories. ISO 5725-2 provides a partial table of significant values for these four outlier tests (ISO 5725-2, 1994). However, since these collaborative studies typically analyze multiple parameters, a programmatic solution to finding not only the scores, but the critical values, leads to savings in time and effort. In his 2013 paper, Wilrich (Wilrich, 2013) derived the formulas for the critical values for each of the fore mentioned tests. The included SAS macro can perform any of these tests on outliers, compare the result with the correct critical values and print a summary or graph as appropriate based on the tests chosen. Options for printing include significant results for the Grubbs or Cochran test, graphs for the Mandel's h and k, with reference lines at the 95% and 99% critical values, and a summary table of results. | outlier, macro call, standard deviation, sample, raw data, degrees of freedom, confidence interval, proc step, proc sql | source | 11 |
| 42 | Discovering CMS Data for Healthcare Research | Laxminarayana Ganapathi, Matthew Urato, Jean Hastie, Merry G. Rabb |
Center for Medicare and Medicaid (CMS) hosts a plethora of health care data and at the outset aims to disseminate the benefits of this vast information to the public, without compromising the individual's privacy. It is estimated that over 98% of adults age 65 and over are enrolled in Medicare and ov er 99% of the deaths in this group are accounted for in the Medicare program, thus making it a vital repository of data for healthcare research about the senior population of about 50 million. While the size of the data could be intimidating, researchers h ave taken up the challenge of working with this ocean of data to evaluate, model, hypothesize and help CMS in improving its services. It is welcome news for SAS users that most of the data is available as SAS data sets. This work aims to introduce the newc omers to the structure of the CMS databases and point to the need and advantage of a deeper understanding of the data for the success of a study | insurance claims, propensity score matching | source | 9 |
| 43 | Document and Enhance Your SAS Code, Data Sets, and Catalogs with SAS Functions, Macros, and SAS Metadata | Roberta J. Glass | Discover how to document your SAS programs, data sets, and catalogs with a few lines of code that include SAS functions, macro code, and SAS metadata. Do you start every project with the best of intentions to document all of your work, and then fall short of that aspiration when deadlines loom? Learn how SAS system macro variables can provide valuable information embedded in your programs, logs, lists, catalogs, data sets and ODS output; how your programs can automatically update a processing log; and how to generate two different types of codebooks |
proc step, macro variable | source | 12 |
| 44 | Dynamically Assigning Length to Transposed Variables | Ethan Ritchie | When using PROC TRANSPOSE to transform "narrow" data (a single subject variable stored in many rows) into "wide" data (one row with subject values stored as distinct variables), the length of the original subject variable is used as the length for each new variable. As a result, variable lengths may be much larger than necessary. This paper will demonstrate a method to automatically assign the smallest length necessary to each variable once the "narrow" data is transposed into "wide" data. The method is currently used on large sample survey data that is updated daily and accounts for the possibility that the optimal length of a variable may change from day to day |
macro variable, proc contents, proc step, character variable, proc print, raw data | source | 5 |
| 45 | Enrollment Simulation in Clinical Trials | Venita DePuy | Enrollment predictions for clinical trials are often as simple as estimating A subjects per site per month x B sites x C months = D target enrollment. This prediction does not model the uncertainty of the estimated enrollment duration. The commonly used method provides an exact number, but in reality, while we may average a certain number of subjects per month, we are likely to see some variability in enrollment. In addition, this method does not incorporate possible enrollment delays, such as delaying enrollment between each subject due to dose toxicity concerns. We propose a simulation method to allow for the creation of estimated enrollment durations with associated confidence intervals, while also allowing for optional delays in recruitment. This method simulates exponentially distributed times to subject enrollment. In addition, this approach also allows us to take into consideration variables such as start-up times or enrollment holds due to data monitoring committee meetings | confidence interval, clinical trial | source | 5 |
| 46 | ETL Load performance benchmarking using different load transformations in SAS Data Integration Studio | Sai S Potluri | This paper is primarily intended to provide some helpful insights to the developers in loading large volumes of data into an Oracle Database by carrying out a performance benchmarking between different load transformations available in SAS Data Integration Studio. The performance benchmarking for bulk loads has been performed on the following Load Transformations using different load styles available with them. Oracle Bulk loader Table loader The performance comparison for update operation has been performed between the following transformations Merge Table Loader | benchmarking, cpu time, proc append, memory usage, update statement, proc step, proc sql, sas data integration studio | source | 8 |
| 47 | Everyone can use a little Currency - when dependent data set updates silently make your analysis data set out of date | Scott Worrell | Nearly every analysis data set has dependencies on multiple other data sets. The dependent data sets may be raw data sets or other analysis data sets. At times a dependent data set is updated, but the person in charge of updating the analysis data set is not notified of the modification to the dependent data set. This makes the analysis data set with the data dependency invalid. In turn, this condition produces data integrity issues in every Table, Listing or Figure (TLFs) that uses the analysis data set. Before running TLFs, it is a good idea to verify that all dependent data sets are older or the same age as the analysis data sets. The Data Currency Utility detects data set currency issues and reports them so that they can be corrected through an update run of the analysis data set creation program. This presentation is appropriate for persons with all levels of programming experience, but assumes basic understanding of SQL Dictionary Tables | macro variable, macro call, adam dataset, proc sql, data cleaning | source | 8 |
| 48 | Exploring JMP Modeling Functionality Using Consumer Expenditure Data | Joshua Klick | The Consumer Expenditure (CE) Surveys collect information on demographic, housing, income, and expenditure characteristics of households in the US. The purpose of this presentation is to explore JMP modeling techniques that explain consumer unit (CU) expenditures of the CE Public Use Micro Data from the CE Interview survey. An initial linear model is constructed based on an exploratory analysis of the data. The second and third models utilize stepwise regression based on the Bayesian Information Criterion and the Max K-Fold RSquare. The fourth model implements Bayesian model averaging via an interface to the R Bayesian Adaptive Sampling (BAS) package. Last, a fifth model partitions data based on the relationship between predictors and response variables. This paper concludes with a summary of model performance | response variable, predictor variable, confidence interval, confidence level, data cleaning, forward selection, validation set, regression analysis | source | 12 |
| 49 | Extracting Clinical Research Data from OnCore and Preparing it for Statistical Analysis in SAS | Chang Xu, Dominic T. Moore, Allison M. Deal |
This paper outlines a simple and straightforward method to extract data from the OnCore data base system and to pr epare it for analysis using SAS . With this method, the user only needs to have ba sic online permission in OnCore along with basic knowledge of SAS and EXCEL. Example code and instructions are provided |
clinical research, excel, proc step | source | 13 |
| 50 | Figure 1. Visual Advantages of the Nested Range Bar Chart | Fan Pan, Wenjia Wang, Jin Liu |
A bar chart is a graph with rectangular bars with lengths that are proportional to the values that they represent, which is probably one of the most useful of all graphs. A bar chart is an alternative to line plots to display the mean or midpoint response when the responses are categorized by discrete values. Bar charts are usually used to compare a response value among specific categories, and widely employed in many fields, including clinical research, agriculture, and education. There are many situations where a simple bar chart does not tell the whole story, or can even be visually misleading. In this way, stacked or subgroup bar charts were showing the distribution of different subgroups among the cluster groups is an excellent visualization tool. This paper demonstrates how to create different kinds of stacked bar charts including: vertically and horizontally stacked bar charts, 100% Stacked Bar Chart and 3D stacked bar charts, and how to control their appearance and layout. Stacked bar chart can be created in GCHART using the VBAR, SUBGROUP, and GROUP options. SGPLOT also quickly produce the bar charts with one or two classification groups, whereas three levels of categorization can be done easily using the SGPANEL procedure. Besides making sure that the graph is not misleading, the paper also demonstrates ways to improve the good looking and layout of these stacked bar charts. For example, how to specify properties of graphs, such as format, color, name, height, axis and width, how to send bar graphs to different ODS destinations and how to apply ODS styles to graphs. Other features such as midpoints and statistic information, text label, inside and outside test annotations, and legend will also be combined to generate an advanced stacked bar chart. Based on the information, a researcher who is not quite familiar with SAS programming could be still able to create stacked bar charts with the appropriate layout. |
bar chart, proc template, proc step | source | 4 |
| 51 | Finding the Gold in Your Data: An Overview of Data Mining | David A. Dickey | The term "data mining" has appeared often recently in analytic literature and even in popular literature, so what exactly is data mining and what does SAS* provide in terms of data mining capabilities? The answer is that data mining is a collection of tools designed to discover useful structure in large data sets. With an emphasis on examples, this talk gives an overview of methods available in SAS Enterprise Miner and should be accessible to a general audience. Topics include predictive modeling, decision trees, association analysis, incorporation of profits and neural networks. We'll see that some of the basic ideas underlying these techniques are closely related to standard statistical techniques that have been around for some time but now have new more appealing names than their statistical ancestors and have been automated to become more user friendly. | logistic distribution, regression analysis, logistic regression, neural network, data mining, roc curve, proc hpsplit, validation set, training set, likelihood function, anova, data cleaning, odds ratio, response variable, binomial distribution | source | 25 |
| 52 | Format-o-matic: Using Formats To Merge Data From Multiple Sources | Marcus Maher, Ipsos Public |
User-defined formats are often the best way to merge a single value onto a larger dataset, but they can seem overly complicated for the novice programmer to use, particularly when remembering all of the specific variable names and details like the 'OTHER' row. We first explain the use case for user-defined formats and some of the considerations to keep in mind when using them. Then, we present a single macro that creates a user-defined format from an already existing dataset, with parameters for all of the commonly used options and some of the less common ones, written for maximum flexibility. The macro is intended to be able to be used by novice programmers without complete knowledge of the workings of the process, but the advanced options make it appropriate for any level of programmer. This presentation does not require any understanding of user-defined formats, or even SAS formats at all. The intended audience is novice and intermediate level programmers, as well as anyone interested in an off-the-shelf user-defined format macro. | proc step, format statement, user-defined format, proc format, proc freq, sample, proc means | source | 10 |
| 53 | From FREQing Slow to FREQing Fast: Facilitating a Four-Times-Faster FREQ with Divide-and-Conquer Parallel Processing | Troy Martin Hughes | With great fanfare, the release of SAS 9 delivered multithreaded processing to a single-threaded SAS world. Procedures such as SORT, SQL, and MEANS could now run faster by taking advantage more fully of system resources through parallel processing paradigms. Multithreading commonly implements divide-and-conquer methodologies in which data sets or data streams are decomposed into subsets and processed in parallel rather than in series. Multithreaded solutions are faster (but typically not more efficient) than their single-threaded counterparts because execution time (but not system resource utilization) is decreased. As the costs of memory and processing power have continued to decrease, however, there remains no excuse for not implementing multithreaded processing wherever possible. To this end, and because SAS unfortunately abandoned some hapless procedures in single-threaded Sheol, this text aims to reunite the single-threaded FREQ procedure with its multithreaded bedfellows. The FREQFAST macro is introduced and espouses divide-and-conquer parallel processing that performs a frequency analysis more than four times faster than the out-of-the-box FREQ procedure. Non-environmental factors affecting FREQ performance (e.g., number of observations, number of unique observations, file size) are elucidated and modeled to demonstrate and predict performance improvement delivered through FREQFAST. |
proc freq, parallel processing, sample, macro variable, character variable, proc step, frequency analysis, sas base, repeated measures, macro definition, stress testing, correlation, numeric variable, macro loop, key performance indicator, sysrc, stepwise selection, regression analysis, proc sql, %if-%then-%else | source | 22 |
| 54 | From Words to Actions: Using Text Analytics to Drive Business Decisions | Reid Baughman | Companies from a variety of industries collect free-form text data that can be used to identify new patterns and relationships. Because unstructured text data does not fit the standard row and column format, it can be more difficult to analyze and utilize. This growing array of data has the pot ential of yielding new insights for companies seeking to better understand customers and gain an edge relative to competitors. By transforming free-form text data into a structure that can be analyzed and visualized, analysts can use supervised and unsupervised data mining techniques to shed new light on old business problems or develop fresh insights. Call center transcripts, medical records, practitioner's notes, survey responses, or any free-form response fields can all hold valuable insights for any organization This paper will outline an example of how to manage and transform free-form text data, apply advanced analytical methods to extract useful patterns, and develop actionable insights | data mining, proc freq | source | 6 |
| 55 | Fuzzy Matching Programming Techniques Using SAS Software | Stephen B. Sloan, Kirk Paul Lafler |
Data comes in all forms, shapes, sizes and complexities. Stored in files and data sets, SAS users across industries know all too well that data can be, and often is, problematic and plagued with a variety of issues. When unique and reliable identifiers are available, users routinely are able to match records from two or more data sets using merge, join, and/or hash programming techniques without problem. But, what happens when a unique identifier, referred to as the key, is not reliable or does not exist. These types of problems are common and are found in files containing a subscriber name, mailing address, and/or misspelled email address, where one or more characters are transposed, or are partially and/or incorrectly recorded? This presentation introduces what fuzzy matching is, a sampling of data issues users have to deal with, popular data cleaning and user-defined validation techniques, the application of the CAT functions, the SOUNDEX (for phonetic matching) algorithm, the SPEDIS, COMPGED, and COMPLEV functions, and an assortment of programming techniques to resolve key identifier issues and to successfully merge, join and match less than perfect or messy data. | proc step, proc sql, proc sort, character variable, proc freq, proc contents, order by, sas function, missing value, data merging, numeric variable, proc transpose, nominal variable, create table, data transformation, where statement, data cleaning, proc format, character functions | source | 25 |
| 56 | Generating Reliable Population Rates Using SAS Software | Jack N. Shoemaker | The business of health insurance has always been to manage medical costs so that they don't exceed premium revenue. Monitoring and knowing about these patient populations will mean the difference between success and financial ruin. At the core of this monitoring are population rates like per member per month costs and utilization per thousand. This paper describes techniques using SAS softw are that will generate these population rates for an arbitrary set of population dimensions. Keeping the denominators in sync with the numerators is key for implementing trustworthy drill - down applications involving population rates | proc step, macro variable | source | 6 |
| 57 | Getting on SAS Grid, Our Journey | David Wilson, Annette M. Green, Karen Terminiello |
RTI International made the move from PC and server-based SAS to SAS Grid this past year. The decision to move to the Grid was based on cost over time, scalability - allowing room for growth, centralization of SAS administration, improved performance, and a highly available environment. This paper will focus on how the overall transition occurred from the decision to move to SAS Grid to getting 400+ SAS users successfully transitioned within a 12-month period on both the Federal Information Processing Standard (FIPS) Low (limited adverse effect) and FIPS Moderate (serious adverse effect) environments. The process included getting sponsorship from the business units and IT, identifying an implementation partner, forming stake-holder teams, gathering requirements and designing the system, establish a plan and timeline for communication, implementation, training, and roll-out. Along the way, we addressed challenges in our environment: which platform to use, moving from Windows to Linux, FIPS mod requirements that impacted the architecture of the system, user response to the transition, regional users and data housed at regional offices, as well as legacy code that required modification due to the move to SAS Grid. Some unexpected positive items to come out of the transition included more collaboration between SAS programmers across the business units which continue after the transition was completed and overall knowledge of what SAS programmers throughout the company utilize in their project specific code. As we transition from implementation to operational mode as a company that utilizes SAS Grid, our previous project technical advisory team has become a Grid advisory committee, a cross-functional team that continues to emphasize the business needs of the analytical community as well as improve our relationship with SAS and leverage the latest features SAS offers. We hope sharing our experience and lesson-learned will be valuable to others considering the transition to SAS Grid. | sas grid, excel, sas studio, sas base, sas programmer, sas enterprise guide | source | 12 |
| 58 | Getting Started with SAS Prompts | Brian K. Varney, Experis Business |
Allowing SAS users to leverage SAS prompts when running programs is a very powerful tool. Using SAS prompts makes it easier for SAS users to submit parameter driven programs and for developers to create robust, data driven programs. This presentation will demonstrate how to create SAS prompts from SAS Enterprise Guide and roll them out to users so they can take advantage of them from SAS programs and SAS Stored Processes | macro variable, sas enterprise guide, macro definition | source | 13 |
| 59 | Guide to ETL Best Practices in SAS Data Integration Studio | Sai S Potluri | This Paper converses about the ETL best practices, tips and techniques that can be followed by developers when using SAS Data Integration Studio for building and maintaining data warehouses/data marts. This Paper discusses the following topics. Best ETL Design Practices. Helpful coding insights in SAS DI studio. * Techniques and implementation using the Key transformations in SAS DI studio | surrogate key, sas data integration studio, proc sql, scd type 1, dataset, proc step, exception handling, batch processing, sas base | source | 12 |
| 60 | Hands-On with an Excel-Based Code Playground for Creating and Sharing SAS ODS Graphics | Ted Conway | You've heard that SAS ODS Graphics provide a powerful and detailed syntax for creating custom graphs, but for whatever reason still haven't added it to your bag of SAS tricks. Let's change that! Workshop participants will quickly gain experience creating a variety of charts by using an EXCEL -based code "playground" to submit SAS code examples and view the results directly from EXCEL. More experienced users will also find the code playground useful for compiling SAS ODS Graphics code snippets for themselves and to share with colleagues, as well as for creating EXCEL-hosted dashboards containing precisely sized and placed SAS graphics |
excel, bar chart, proc step | source | 5 |
| 61 | HASH Beyond Lookups - Your Code Will Never Be the Same! | Elizabeth Axelrod | If you've ever used the hash object for table lookups, you're probably already a fan. Now it's time to branch out and see what else hash can do for you. This paper shows how to use hash to build tables and aggregate data. Why would you ever want to do this? How about achieving a complex process that would have taken multiple sorts and many passes through your data - all in a single DATA STEP that flows intuitively and isn't hard to write! I learned this technique from a presentation by hash masters Paul Dorfman and Don Henderson, and now my code - and how I think about solving problems - will never be the same |
hash table, hash iterator, lookup | source | 8 |
| 62 | Hello World! Getting Started with the SAS DS2 Language | Patricia S. Aanderud, Jonathan Boase |
DS2 is an object-oriented programming language that is used for advanced data manipulation and data modeling applications. DS2 features extend the DATA STEP capabilities by adding variable scoping, user- defined methods, ANSI SQL data types and user-defined packages. In this workshop, you'll write some DS2 programs to familiarize yourself with the language. You'll have some ideas of ways to upgrade or extend your existing code when you return to the office |
proc step, set statement, proc sql | source | 7 |
| 63 | How High Will My Magikarp Jump? | Marcus Maher, Ipsos Public |
The Pokémon Company's new mobile game, Magikarp Jump, has simple game play where the goal is to train your Magikarp to jump as high as possible. The distance jumped (in meters) is solely determined by the jump points possessed by the Magikarp, which can be earned by eating berries and by training your Magikarp. Measurements for jump points are imprecise and only recorded with three significant digits, and the relationship between the two is not defined by the game. What is the relationship between height jumped and jump points, and how does one deal with potential bias from imprecise measurement? Using data obtained by the presenters and their children through several dozen playthroughs, this presentation explores techniques used in estimation and t he importance of statistical graphs for exploring the data and assessing quality of fit | proc step, proc reg, scatter plot | source | 5 |
| 64 | How to use UNIX commands in SAS code to read SAS logs | James Willis | Reading multiple logs at the end of a processing stream is tedious when the process runs on a UNIX platform. In UNIX, the logs must be individually viewed using a UNIX command like "CAT" or "MORE" or an application like ULTRA EDIT or the UNIX VI edit process. The UNIX options are tedious and time consuming for even skilled UNIX users. The risk of missing an important log message is significant. SAS batch programs running in UNIX can be chained together using include statements. When a long chain of programs is executed, and each program in the chain has its log printed separately, reviewing all of the logs from the process can be difficult. The process is definitely time consuming. Reading the logs using UNIX commands and SAS logic simplifies and speeds up the log verification process. "ERROR:", "WARNING:", "CONVERSION", "MISSING", "NOT FOUND" and all other messages can be located, ranked, written to a spreadsheet, and then the spreadsheet can be emailed to you | macro variable, input statement | source | 7 |
| 65 | I Spy PII: Detect, Protect, and Monitor Personal Data with SAS Data Management | Cecily Hoffritz | The clock is ticking! Is your company ready for 25 May 2018 when the General Data Protection Regulation that has to do with data privacy laws across Europe comes into force? Failing to comply, companies incur very large fines and may lose customer trust if sensitive information is compromised. With data streaming in from multiple channels in different formats, sizes and wavering quality, it is increasingly difficult to keep track of personal data so that you can protect it. SAS Data Management helps companies on their journey towards governance and compliance involving tasks such as detection, quality assurance and protection of personal data. This paper focuses on SAS Federation Server and SAS Data Management Studio from the SAS Data Management suite to surface and manage that hard to find personal data. SAS Federation Server provides you with a universal way to access Hadoop, Teradata, SQL Server, Oracle, SAP HANA and other data without data movement during processing. SAS Federation Server's advanced data masking and encryption capabilities can be utilized when virtualizing data for users. Purpose-built data quality functions are used to perform identification analysis, parsing, matching and extraction of personal data in real time. We will also provide insight on how SAS Data Management Studio's exploratory data analysis enables you to scan through your investigation hub to identify and categorize personal data. | proc sql, sas enterprise guide, dataset, sample, where statement, sas base, data science, data cleaning, select, sql server, libname statement | source | 33 |
| 66 | Identifying and Removing Subjects with Duplicates in Long Data Format: A Simple Approach Using PROC SORT and PROC SQL | Szu-Fu Chao | This paper will illustrate an efficient two-step method to identify and remove all examinees with duplicate observations in long format testing data, rather than simply the redundant records. This approach applies in the former case for instances where each row in the dataset contains only one item record per subject, and is generally implemented only when the sample size is sufficiently large and the number of participants who have duplicate records only account for a small proportion (e.g., < 3%) of the overall data set. The issue with duplicates is resolved by implementing the combination of the NODUPKEY and DUPOUT options in PROC SORT and one of two queries in PROC SQL. Although the option NODUPKEY from PROC SORT can indeed provide a good way to handle duplicates (i.e., removing redundancies and only keeping intended observations with appropriate key variables specified), there seems to be no explicit function in SAS to remove both the primary and duplicate records for these subjects. However, the goal can be achieved easily by applying one additional step in PROC SQL with the information extracted from the data generated by the option DUPOUT in PROC SORT. Variations can be implemented to either create a new data set or overwrite the original data directly. This paper provides a quick and efficient way of producing duplicate-free long format data with problematic subjects excluded. A complete example of the method will be demonstrated using data from a computer-based assessment field test with 32 items and 198 examinees that initially producing 5960 rows in total. People from different professional backgrounds who may be dealing with similar issues are likely benefit from the techniques demonstrated in this paper with relatively limited effort. |
proc step, proc sort, proc sql | source | 5 |
| 67 | Identifying Duplicate Variables in a SAS Data Set | Bruce F. Gilsen | In the big data era, removing duplicate data from a data set can reduce disk storage use and improve processing time. Many papers have discussed removal of duplicate observations, but it is also useful to identify duplicate variables for possible removal. One way to identify duplicate variables is with PROC COMPARE, which is commonly used to compare two data sets, but can also compare variables in the same data set. It can accept a list of variable pairs to compare and determine which variable pairs are identical. This paper shows how to obtain a summary report of identical variables for all numeric or character variables in a data set, using the following steps: 1. Dynamically build a list of all possible numeric or character variable pairs for PROC COMPARE to analyze. 2. Convert PROC COMPARE pairwise results (e.g., N1 is identical to N3, N3 is identical to N5, N1 is identical to N5, etc.) into a summary report that groups all identical variables (e.g., N1, N3, and N5 are identical). For very large data sets, the paper shows how to substantially improve performance by first executing PROC COMPARE one or more times on a small number of observations to reduce the number of extraneous comparisons. |
proc compare, proc step, macro variable, conditional do loop, numeric variable, proc sql, character variable, if-then-else | source | 15 |
| 68 | Identifying Semantically Equivalent Questions Using Singular Value Decomposition | Varsha Reddy Akkaloori | Quora is a Q&A social network centered on the idea of promoting ideas and knowledge. With over 100 million monthly visitors, it's not surprising that many people ask similarly worded questions causing site visitors to spend more time discovering the best response to their question. This also frustrates authors because they feel they need to answer multiple versions of the same question. This paper aims at solving a challenge released by Quora to improve the experience of its authors and site visitors by grouping queries with similar intent using SAS. To ensure that different words are processed equivalently as the same representative parent term, Pydictionary module in Python, based on thesaurus.com, was used for extracting synonyms for the most frequently occurring terms in the term-by-document matrix. With the help of SAS Enterprise Miner, singular value decomposition (SVD) was implemented to reduce the dimensions of the term-by-document frequency matrix. Euclidean distance was used to determine distance between sentences that have been projected into the SVD space. In addition, Inverse Document Frequency weight was chosen so that frequently occurring terms will have been down-weighted and the rarer but concentrated terms have the greatest influence on similarity. The accuracy of the classification of question pairs was 62.4%. Further research would be continued to make a utility which would predict if a question is duplicate based on the prior knowledge imbibed into it thereby acting as a recommender system for Quora. | proc freq, data mining, proc distance, euclidean distance, data dictionary, frequency analysis | source | 7 |
| 69 | Improving a Graph Using PROC GPLOT and the GOPTIONS Statement | Wendi L. Wright | Starting with a SAS PLOT program, we will transfer this plot into PROC GPLOT and review the many ways you can improve the look of the plot using SAS GRAPH statements. We will make the plot really shine by customizing titles, footnotes, symbols, legends, axes, and even the reference line. At each step, a hands-on example will be presented where the user will choose their own features such as symbol colors and placement of the legend. In the end, you will have built your own personalized graph using the Title, Footnote, Symbol, Legend, and Axis statements |
proc step, proc gplot | source | 20 |
| 70 | Intracompany social media support for a SAS Grid migration | Harper Gordek | In 2016 RTI International migrated over 400 SAS users from SAS on PCs and several stand -alone Linux servers to a SAS Linux Grid. Despite best efforts, pre-migration company-wide training could not cover every potential topic. In addition, post-migration support from IT was necessarily focused on issues of base functionality. For specific programming issues to be addressed with expediency it became necessary then for new methods of communication and collaboration to arise. But how to best do this when these 400 users spanned several departments with limited historical communication between them? In response, staff began to use to social networking applications like Slack and Yammer and created an internal migration support community. In this presentation, we'll cover how the limitations of company-wide approaches led to innovative solutions that gathered together a diverse internal community to better problem solve and share knowledge | sas grid, time to response | source | 8 |
| 71 | Introduction to ODS Graphics | Chuck Kincaid, Experis Business |
This presentation teaches the audience how to use ODS Graphics . Now part of Base SAS, ODS Graphics are a great way to easily create clear graphics that enable any user to tell their story well. SGPLOT and SGPANEL are two of the procedures that can be used to produce powerful graphics that used to require a lot of work. The core of the procedures is explained, as well as some of the many opti ons available. Furthermore, we explore the ways to combine the individual statements to make more complex graphics that tell the story better. Any user of Base SAS on any platform will find great value in the SAS ODS Graphics procedures |
proc step, series plot, sas base, numeric variable, proc sgplot, proc sgpanel | source | 18 |
| 72 | Job Upward Mobility: Getting Better When You're Already Good | Molly Meade Hall | You're a SAS Programmer, or a Statistical Analyst, or a Data Scientist, or a Manager of SAS Programmers. You make a healthy six figures, or close to it. Perhaps you drive a Lexus, or other luxury car. You're reasonably happy in your career. Still, sometimes you can't help feeling a little stuck. You're curious about exploring opportunities, but you want to do it inconspicuously, and you don't want to jeopardize your current position. We will talk about the SAS job market. We will explore networking channels that work. Can using Social Media boost your career? Is it appropriate? Let's discuss. Some people say that resumes are dead - is this true? What about the cover letter - is it necessary? We will also examine interview tips and techniques. Even if you are happy at your current company, or in your current job, this presentation is for everyone, and it is appropriate for people at all levels. It's time to stop procrastinating and, perhaps, level up | data science, predictive analytics | source | 11 |
| 73 | Joining Data: Data Step Merge or SQL? | Harry Droogendyk | This paper explores the joining of datasets / tables using both the DATA STEP MERGE and PROC SQL. Similarities between the two methods are identified and occasions when one method might be preferred over the other are discussed. Specific issues relating to INNER, OUTER and FULL joins are covered, as are the vagaries of the SQL ON vs. WHERE clauses. An array of examples will illustrate exactly how joins are accomplished in both DATA STEP and SQL environments. Included in the presentation is a method of displaying SQL's inner workings providing hints for query optimization |
proc sql, proc step, data step, inner join, where statement, normalization, full join, create table, index, transaction data, data merging, macro variable, right join | source | 19 |
| 74 | Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP, 2nd ed. | Mark Keintz | From stock price histories to hospital stay records, analysis of time series data often requires use of lagged (and occasionally lead) values of one or more analysis variable. For the SAS user, the central operational task is typically getting lagged (lead) values for each time point in the data set. While SAS has long provided a LAG function, it has no analogous "lead" function - an especially significant problem in the case of large data series. This paper will (1) review the lag function, in particular the powerful, but non-intuitive implications of its queue-oriented basis, (2) demonstrate efficient ways to generate leads with the same flexibility as the lag function, but without the common and expensive recourse to data re-sorting, and (3) show how to dynamically generate leads and lags through use of the hash object | lag function, hash table, merge statement, time series analysis, set statement, by group processing, missing value, hash iterator | source | 15 |
| 75 | Let SAS Do Your DIRty Work | Richann J. Watson | Making sure that you have saved all the necessary information to replicate a deliverable can be a cumbersome task. You want to make sure that all the raw data sets and all the derived data sets, whether they are Study Data Tabulation Model (SDTM) data sets or Analysis Data Model (ADaM) data sets, are saved. You prefer that the date/time stamps are preserved. Not only do you need the data sets, you also need to keep a copy of all programs that were used to produce the deliverable, as well as the corresponding logs from when the programs were executed. Any other information that was needed to produce the necessary outputs also needs to be saved. You must do all of this for each deliverable, and it can be easy to overlook a step or some key information. Most people do this process manually. It can be a time-consuming process, so why not let SAS do the work for you? | macro variable, dataset, cdisc | source | 8 |
| 76 | Macros for creating a custom report of figures | Laura Williams | Often, with clinical studies, a report that includes both tables and figures can be preferred. PROC REPORT can be used to place multiple images in a document, such as an RTF file, in combination with summary tables or other text. First, a set of macros have been developed to control the appearance of figures within our organization, using a single template with GTL (Graph Template Language). Plus, the macros also control the file type, size, and resolution of the ODS output. Second, another set of macros have been developed to join these figures into an RTF file and populate the appropriate titles and footnotes. This second set of macros is a joint set of macros that can also produce tables. Therefore, we are able to create reports in a certain style that have a consistent appearance regardless of who programs the analysis output |
proc step, proc template, proc report, output delivery system, ods rtf, macro definition | source | 10 |
| 77 | Mapping Participants to the Closest Medical Center | David W. Franklin | "How far are patients from Clinics?" That was the question which was asked on a wintery afternoon. Recorded in the database was the ZIP codes of each participant, but how could we use that data to find the closest distance from the nearest clinic? "And would it not be nice if we could map it?" This paper looks at calculating the distance from a participant ZIP code to a Medical Center using the GEODIST function, finding the smallest distance for each participant to attend, and finally color-coding the plots depending on the distance calculated using the GMAP procedure. Also helpful is producing reports, one of which will be the those participants who are farthest away and the distance |
proc gmap, sashelp | source | 8 |
| 78 | Mapping Roanoke Island Revisited: An OpenStreetMap (OSM) Solution | Barbara B. Okerson | In a previous presentation, SAS was used to illustrate the difficulty and solutions for mapping small pieces of coastal land that are often removed from map boundary files to smooth boundaries. Roanoke Island one of the first areas of the current United States to be mapped (1585), was used as an example since it is smoothed out of many current maps. While these examples isolated Roanoke Island, they didn't provide detail beyond city names on the map. Originally limited to SAS Visual Analytics, SAS now makes available background maps with street and other detail information for SAS/GRAPH using open source map data from OpenStreetMap (OSM). This paper will review the previous solutions and then look at how to map Roanoke Island using SAS/GRAPH and OSM |
macro variable, proc gmap, proc step | source | 12 |
| 79 | Merge with Caution: How to Avoid Common Problems when Combining SAS Datasets | Joshua M. Horstman | Although merging is one of the most frequently performed operations when manipulating SAS datasets, there are many problems which can occur, some of which can be rather subtle. This paper examines several common issues, provides examples to illustrate what can go wrong an d why, and discusses best practices to avoid unintended consequences when merging | merge statement, by group processing, out=, in=, pdv, length statement, dataset, sas log, proc sql | source | 14 |
| 80 | Meta-Analysis and Matrix Multiplication: Adapting an IML-Based Macro for DerSimonian and Laird's Error Calculation to use Base SAS | Venita DePuy | PROC IML is a wonderful tool; but not all companies opt to purchase the SAS license for that product. This paper summarizes my journey to adapt Hertzmark & Spieglman's %METAANAL macro, which produces DerSimonian-Laird estimators for meta-analysis, to use Base SAS. The macro itself is also provided |
proc mixed, fixed effects, treatment effect, proc calis, confidence interval, proc iml, sas base, odds ratio, normal distribution, clinical trial, proc step, sample, binomial distribution, relative risk | source | 16 |
| 81 | Methods for Creating Sparklines using SAS | Richard Andrews | A sparkline is a very small line chart, typically drawn without axes or coordinates, to represent the general shape of the variation (typically over time) of some measurement, such as temperature or stock market price, in a simple and highly condensed way (Spark, 2017). This paper will discuss how SAS can be used to mimic sparklines created in Microsoft EXCEL. A number of methods exist for generating sparklines in SAS, two of which will be discussed. The first is a series of images that can be added to the REPORT procedure employing an SGPLOT and the second utilizes a highly customized process using the annotate facility available with SAS |
proc report, excel, normalization, proc step | source | 10 |
| 82 | Migrating from PC SAS to SAS Grid on Linux | Susan Myers, Inga Allred, M. Rita Thissen |
SAS Grid is available for Windows or Unix, and it offers features and benefits that are not available under PC SAS. What challenges do programmers face when an organization makes the move from Windows - based PC SAS to Linux-based SAS Grid? To adapt to the new system, you must address differences between Windows and Linux as well as the differences between PC SAS and SAS Grid . To complete some batch operations, you also need to coordinate programs on both platforms as part of the same overall process. We present our experience and lessons learned regarding Windows versus Linux, the use of PuTTY versus MobaXterm terminal emulators, data management, program conversion, and the use of Windows command files that call Linux shell scripts. These combined operations may work well, but the learning curve can be steep | sas grid, sas enterprise guide, excel, sas studio, proc sql, batch job, dataset | source | 11 |
| 83 | Missing Data and Complex Sample Surveys Using SAS: The Impact of Listwise Deletion vs. Multiple Imputation Methods on Point and Interval Estimates when Data are MCAR, MAR, and MNAR | Listwise Deletion | Social scientists from many fields use secondary data analysis of complex sample surveys to answer research questions and test hypotheses. Despite great care taken to obtain the data needed, missing data are frequently found in such samples. Even though missing data is a ubiquitous problem, the methodological literature has provided little guidance to inform the appropriate treatment for such missingness. This Monte Carlo study used SAS to investigate the impact of missing data treatment (hot deck-based multiple imputations versus regression-based multiple imputation versus listwise deletion) when data are MCAR, MAR, and MNAR. By using 10% to 70% of missing data (along with complete sample conditions as a reference point for interpretation of results), the research focused on the parameter estimates in multiple regression analysis in complex sample data. Results are presented in terms of statistical bias in the parameter estimates and both confidence interval c overage and width | sample, missing value, imputation, multiple imputation, estimate, regression analysis, proc step, correlation, confidence interval, proc surveyreg, sample size, sample weight, confidence level, intraclass correlation, missing completely at random, missing not at random, monte carlo, missing at random, multiple regression, proc mi, proc mianalyze, data science, outlier | source | 26 |
| 84 | MOPP the SOP - Makeover of Programming Process Steps in the Standard Operating Procedure | Kristin Danna, Kathy Harkins, Lisa Spring |
Our goal was to redesign our standard operating procedures and the corresponding tools and templates for defining, developing and validating programs to support analysis and reporting (A&R) performed by Biostatistics and Research Decision Sciences (BARDS). Current processes were evaluated and comprehensive documentation, templates and training materials were developed for each of the system development lifecycle stages - Define, Develop, Validate and Operations. This paper will provide insights into the planning, implementation and execution of a major procedure rewrite in order to streamline processes and align with industry best practices | data cleaning, sample | source | 5 |
| 85 | Multicollinearity: What Is It, Why Should We Care, and How Can It Be Controlled? | Deanna Naomi Schreiber-Gregory, Henry M. Jackson |
Multicollinearity can be briefly described as the phenomenon in which two or more identified predictor variables in a multiple regression model are highly correlated. The presence of this phenomenon can have a negative impact on the analysis as a whole and can severely lim it the conclusions of the research study. This paper reviews and provides examples of the different ways in which multicollinearity can affect a research project, and tells how to detect multicollinearity and how to reduce it once it is found. In order to demonstrate the effects of multicollinearity and how to combat it, this paper explores the proposed techniques by using the Youth Risk Behavior Surveillance System data set. This paper is intended for any level of SAS user. This paper is also written to an audience with a background in behavioral science or statistics | regression analysis, predictor variable, correlation, ridge regression, estimate, response variable, condition index, sample, proc princomp, proc reg, multiple regression, proc corr | source | 12 |
| 86 | Mysteries of Posting Results on reddit | Adeline J. Wilcox | After reading a December 2016 USA TODAY story revealing data leaked from the U.S. Department of Veterans Affairs (VA), this retired VA employee doubted the quality of the leaked data. After I completed my analysis of the data published by USA TODAY , I wanted to share my results, but not by corresponding with USA TODAY . Naively, but successfully, I created a reddit account and posted my analysis on the subreddit named dataisdepressing. My post remained at the top of the front page of the dataisdepressing subreddit for nearly three months, despite receiving no comments and few upvotes. For analyses of general interest that produce cheerless results, the dataisdepressing subreddit can be a means for sharing results or visualizations that can be displayed on a single screen. Reddit users submitting content to the dataisdepressing subreddit should not be disappointed if their post receives no more than a few hundred views | dataset, master data management | source | 4 |
| 87 | ODS basics | Michael Jadoo | The Output Delivery System (ODS) is a method within SAS to control the output from PROC/DATA steps. This feature will allow one to create output from either a DATA STEP or PROC STEP in many different formats. This paper will go over a few statements that will create popular output that is used in many offices |
sas output, proc step | source | 4 |
| 88 | Old Age and Treachery vs. Youth and Skill: An Analysis of the Mean Age of World Series Teams | Joe DeMaio | Every October, baseball fans discuss and debate the ages of World Series opponents. Such a comparison limits itself to the two teams competing for the Commissioner's Trophy. Does the older of the two teams win more often than not? If so, how does the age of that team compare to the rest of the league? Perhaps the older of the two teams won the World Series but was still younger than the league average. Furthermore, there are two distinct types of players in baseball. Batters score runs. Pitchers prevent the other team from scoring runs. In 2016, the mean age of the World Series Champion Chicago Cubs' pitchers was 31.1 years and older than the mean age of the Cleveland Indians' pitchers at 25.3 years. The reverse was true for batters. The mean age of Cubs' batters was 27.2 years versus the Cleveland Indians' batters at 29 years | correlation, pearson correlation | source | 7 |
| 89 | One Project, Two Teams: The Unblind Leading the Blind | Kristen Reece Harrington | In the pharmaceutical world, there are instances where multiple independent programming teams exist to ensure blinded treatments are maintained by the appropriate parties. For the purposes of this discussion, blinded corresponds to fake data and unblinded corresponds to actual data. Within these projects, blinded programmers use temporary fake data to create programs which produce both blinded and unblinded results. To ensure the blind is maintained and ethics are upheld, only the blinded programming team produces and modifies the programs. While robust programming is key, another major contributing factor for success is communication. This discussion will explore the process of initializing a project supported by blinded and unblinded teams, successful communication techniques when real data comes into play, and ways to effectively troubleshoot validation issues without providing unblinding information | data cleaning, macro variable, sdtm domain, cdisc | source | 5 |
| 90 | One-to-One, One-to-Many, and Many-to-Many Joins Using PROC SQL | Kirk Paul Lafler | A powerful and essential PROC SQL programming technique that all SAS users should understand, and be comfortable performing, is the process of joining (or combining) two or more tables of data. This paper and presentation describes and illustrates the join process, including what a join is, exploration of one-to-one, one-to-many, and many-to-many data relationships, identifying a primary key (or unique identifier), special preparation requirements for each table being specified in a join, and popular join techniques available to SAS users. To demonstrate the power of the join process, examples of conventional (symmetrical matching) and unconventional (asymmetrical left, right and full matching) using PROC SQL join programming techniques are illustrated |
proc sql, data merging, where statement, inner join, proc step, full join, right join | source | 19 |
| 91 | Parallel Coordinates Plot Made Easy | Shane Rosanbalm | A parallel coordinates plot is useful for visualizing multivariate data. Unfortunately, there isn't a PARALLEL statement in SGPLOT. In this paper we present a macro called %parallelplot. Using a minimum of parameters (data=, var=, group=) the macro will produce a parallel coordinates plot via |
macro call, macro parameter | source | 5 |
| 92 | Parallel Processing in a SAS Grid | Merry G. Rabb, Keith J. Brown |
In 2016 RTI International migrated over 400 users from SAS software on PCs and several stand-alone Linux servers to a SAS Linux grid. In addition to other improvements the new grid provided an opportunity to improve the performance of some of our long-running jobs. SAS jobs often contain independent tasks that can be split up and distributed across multiple nodes of a SAS grid. Running independent multiple tasks in parallel will usually result in a reduction in total elapsed time for the job. This paper will discuss practical considerations for introducing parallel processing into a SAS grid job, including identifying types of concurrency and how best to divide the program into subtasks. We will cover the basics of how to add the required code, including how to pass information from one task to another | parallel processing, proc freq, elapsed time, macro variable, proc step, sas grid, libname statement, macro loop, sas enterprise guide, macro definition | source | 12 |
| 93 | Parallelizing Windows Operating System Services Job Flows | David Kratz | SAS Job flows created by Windows operating system services have a problem: At present they can only execute jobs in series (one at a time). This can slow job processing down, and limits the utility of these flows. This paper shows how one can alter Windows operating system services flows after they have been generated to enable jobs to run in parallel (side by side). A high level overview of a SAS PROC GROOVY script which automates these changes is provided, as well as a summary of the positives and negatives of running jobs in parallel |
proc step, proc groovy, parallel processing, sysrc | source | 7 |
| 94 | Pinching Off Your SAS Log: Adapting from Loquacious to Laconic Logs To Facilitate Near-Real Time Log Parsing, Performance Analysis, and Dynamic Data-Driven Design and Optimization | Troy Martin Hughes | Too often in SAS literature, the role of the program log is narrowly conceptualized as a static, post hoc quality control review that validates program success through the detection of program failure or the lack thereof. Especially when software development occurs outside of a formalized software development life cycle (SDLC), as is often the case with non-production software and within end-user development environments, SAS practitioners must painfully parse logs in search of notes, warnings, runtime errors, and other often elusive indications of functional failure. A substantial body of SAS literature advances antediluvian manual log review through the automation of log parsing and analysis and subsequent communication of program success (or failure) to stakeholders. To a lesser extent, SAS literature depicts how log parsing can be utilized to extract, analyze, and ultimately improve software performance metrics. After a cursory review of SAS automated log parsing literature, this text elucidates and expands this second objective of automated log parsing, demonstrating how performance metrics can be analyzed in near-real time to drive program flow dynamically. By pinching off shorter logs and saving these as temporary text files, SAS programs can analyze performance metrics for individual procedures or processes, enabling software to detect anomalous or undesirable CPU, input/output (I/O), or memory consumption and to respond dynamically to optimize execution. | key performance indicator, sas log, proc sort, cpu time, stress testing, macro variable, if-then-else, repeated measures, repeated measures anova, txt, macro definition, sample, proc sql, character variable, data cleaning, exception handling, sysrc, numeric variable, parallel processing, proc printto, quality control, options statement, proc step, macro loop | source | 25 |
| 95 | PMML & SAS: An Introduction to PMML and PROC PSCORE | Andrew Gannon | The Predictive Model Markup Language (PMML) is becoming the standard write-up language for modeling. Based in XML, it can easily be used by many different applications including R, SAS, and SPSS - among others. SAS can integrate PMML by invoking the PROC PSCORE process. This document serves to introduce the PMML language and its terminology - as well as an introduction to the PROC PSCORE method in SAS. This paper should serve only as an advertisement to PMML and an introduction to using PMML with SAS |
regression analysis, proc step, data dictionary, nominal variable | source | 9 |
| 96 | PROC IMPORT and more. Or: when PROC IMPORT just doesn't do the job | David B. Horvath | PROC IMPORT comes in handy when quickly trying to load a CSV or similar file. But it does have limitations. Unfortunately, I've run into those limitations and had to work around them. This session will discuss the original CSV specification (early 1980's), how Microsoft EXCEL violates that specification, how SAS PROC IMPORT does not follow that specification, and the issues that can result. Simple UNIX tools will be described that can be used to ensure that data hilarities do not occur due to CSV issues. Recommendations will be made to get around some of PROC IMPORT limitations (like field naming, data type determination, limitation in number of fields, separator in data). CSV, TAB, and DLM types will be discussed |
proc import, proc step, dataset, excel, macro variable | source | 8 |
| 97 | proc sql; select distinct memname, memlabel from dictionary.dictionaries; quit; | Kiran Venna | Dictionary.columns is a dictionary table, which gives metadata information of columns. Dictionary.columns is very helpful in understanding various data related issues. Data integrity issues, while appending data from external files to SAS Datasets can be understood with help of Dictionary.columns. Appropriate appending of external files to SAS Datasets can be done with help of SAS Macros. Data movement from Hive to Teradata by using SAS can cause an issue when variable name length is greater than 32. Longer variable name issues can be evaluated with the aid of Dictionary.columns. PROC FEDSQL can solve variable names length issue. While inserting data from SAS to RDBMS, difference in number of columns can cause an issue and this issue can be analyzed and fixed with help of dictionary.columns. PROC SQL; select distinct memname, memlabel from dictionary.dictionaries; quit; |
proc append, input statement | source | 8 |
| 98 | Propensity Score Methods for Causal Inference with the PSMATCH Procedure | Yang Yuan, Yiu-Fai Yung, Maura E. Stokes |
In a randomized study, subjects are randomly assigned to either a treated group or a control group. Random assignment ensures that the distribution of the covariates is the same in both groups and that the treatment effect can be estimated by directly comparing the outcomes for the subjects in the two groups. In contrast, subjects in an observational study are not randomly assigned. In order to establish causal interpretations of the treatment effects in observational studies, special statistical approaches that adjust for the covariate confounding are required to obtain unbiased estimation of causal treatment effects. One strategy for correctly estimating the treatment effect is based on the propensity score, which is the conditional probability of the treatment assignment given the observed covariates. Prior to the analysis, you use propensity scores to adjust the data by weighting observations, stratifying subjects that have similar propensity scores, or matching treated subjects to control subjects. This paper reviews propensity score methods for causal inference and introduces the PSMATCH procedure, which is new in SAS/STAT 14.2. The procedure provides methods of weighting, stratification, and matching. Matching methods include greedy matching, matching with replacement, and optimal matching. The procedure assesses covariate balance by comparing distributions between the adjusted treated and control groups. |
propensity score, propensity score matching, treatment effect, response variable | source | 17 |
| 99 | Purrfectly Fabulous Feline Functions | Louise S. Hadden | Explore the fabulous feline functions and calls available in SAS 9.1 and later. Using CAT functions and CAT CALLs gives you an easier way to streamline your SAS code and facilitate concatenation of character strings. So, leave verbose coding, myriad functions, and the vertical bar concatenation operators behind! SAS 9.2 (and beyond) enhancements will also be demonstrated | cpu time, proc step, character functions | source | 15 |
| 100 | Quality Control in SAS: Checking Input, Work, and Output | Aaron Brown | Part of being a responsible SAS programmer is checking that one's programs work as intended. Errors and bugs can come from several causes. This paper goes through some tools for checking input data, one's work as one works on the data, and output data. It is unlikely that all of these tools will be applicable for every program, but the author hopes that this paper will give tools that other programmers can use as quality control resources. The author also hopes this paper will emphasize the need for quality control and checking assumptions. These tools utilize the COMPARE, CONTENTS, FREQ, MEANS, PRINT, and SQL procedures | proc step, proc freq, proc sql, quality control, proc sort, dataset, proc compare, proc contents, character variable, proc print, proc import, sample | source | 11 |
| 101 | Removing the Mask of Average Treatment Effects in Chronic Lyme Disease Research Using Big Data and Sub-Group Analysis | Mira Shapiro, Lorraine Johnson |
There is considerable controversy in the medical community surrounding chronic Lyme disease (CLD). CLD sufferers have persistent Lyme symptoms resulting from being untreated, under-treated or, having a lack of response to their antibiotic treatment protocol. Some of the past Lyme disease studies that reported on average treatment effects were unable to identify treatment successes. Using patient-reported outcome data collected by LymeDisease.org via their online registry, MyLymeData, we will show that using sub-group analysis techniques can unmask valuable information about treatment effectiveness | treatment effect, sample, sample size | source | 9 |
| 102 | RETAINing Your Sanity: 5 Ideas to Manipulate Data Using the RETAIN Statement | Matthew Taylor | SAS PROCs are generally structured to work with long, vertically oriented datasets. The challenge with datasets that are created this way is that it is very easy to work with data across observations but it is cumbersome to try to work with data vertically. Thus enters the RETAIN statement, a way to hold values across observations and assist in those comparisons. This paper will review five ways to use the RETAIN statement to solve common dataset related problems | retain, proc step | source | 7 |
| 103 | Ron Fehd, SAS-L's Macro Maven, Answers Your Macro Questions | Ronald J. Fehd | SAS software consists of two languages, SAS and its macro language. The purpose of this talk is to provide overview and perspective of how SAS works and how the macro language can work both within and before SAS program statements and steps. | macro variable, macro facility | source | 6 |
| 104 | SAS and Hadoop: The 6th Annual State of the Union | Paul Kent | The fourth maintenance release for SAS 9.4 and the new SAS Viya platform bring even more progress with respect to the interoperability between SAS and Hadoop (the industry standard fro big data). This talk brings you up-to-date with where we are: more distributions, more data types, more options. And then there is the cloud. Come and learn about the exciting new developments for blending your SAS processing with your shared Hadoop cluster. | order by, fact table | source | 16 |
| 105 | SAS and the Voluntary Framework of Accountability: A Prime Example of the Use of SAS in Education | Kelly D. Smith, Bobbie E. Frye, Paul G. Earls |
Community colleges in the United States provide accessible learning opportunities for more than 10 million students each year (Community College FAQs, n.d.). The student population served by community colleges often differs from the student population served by four year colleges. Consequently, accountability metrics developed for four year colleges are not always appropriate for two year community colleges (American Association of Community Colleges, 2012). In 2012, metrics designed to better evaluate the effectiveness of community colleges were established as part of the Voluntary Framework of Accountability (VFA). The metrics used in the VFA were developed by the American Association of Community Colleges (AACC), the Association of Community College Trustees and the College Board Advocacy and Policy Center. VFA metrics utilize data that community colleges are already collecting for internal and external reporting (state and federal) | macro variable, dataset | source | 7 |
| 106 | SAS Doesn't Store Dates | Nat H. Wooding Jr. | Base SAS stores two types of variables: character strings and floating-point numbers. While a user can store a date value in a SAS data set using one of several methods, SAS does not recognize the value as being different from any other numeric or character variable. This paper explores the ways a date value can be stored and the ramifications of each storage method. In addition, it looks at some issues that are encountered when date values are read from data bases. This paper is intended for new SAS users and those who are not familiar with date handling in SAS | date variable, sysdate9, character variable, proc step | source | 9 |
| 107 | SAS In The Classroom: Exploratory Data Analysis with SAS Studio | Jonathan W. Duggins | SAS Studio, and by extension SAS University Edition, provide exciting opportunities for students early in their statistics education. By allowing students, and teachers, the ability to use a point -and-click, web- based interface students can calculate summary statistics, explore complex sampling distributions, and carry out inference procedures using an industry-standard analysis package but with minimal need for programming. We will discuss walking students through an activity intended to develop a graphics -based understanding of the sampling distribution for a mean. At the conclusion of the session participants will know: the difference between tasks and snippets; how to create graphical and numerical summaries of a data set, how to draw samples from a population; and how to use macro variables to customize the code generated by a process flow | sample, histogram, sas studio, sample size, proc means, macro variable, response variable, random sampling, proc step, macro facility | source | 20 |
| 108 | SAS Macros for Time - Dependent Effects and Risk Factors on Survival | Chao Zhang, Manali Rupji, Yuan Liu, Jeanne Kowalski |
In traditional Kaplan-Meier or Cox regression analysis, typically a risk factor measured at baseline is examined for it's association with survival thereafter. During follow-up however, things may change, such that either the effect of a fixed baseline risk factor may vary over time, resulting in a weakening or strengthening of associations over time, or the risk factor itself may vary over time. In the former case, such as effect is often seen in what appears to be significant differences in survival not necessarily overall, among all survival times, but early on or at later survival times. We address such time-dependent effects on survival by creating two SAS macros, one for landmark and another for quantile survival regression. As opposed to the currently available SAS procedure for quantile survival regression, our quantile survival analysis macro includes a test for significant differences among the levels of a categorical covariate within quantiles of survival time by constructing a plot of the estimated difference in survival time between levels of a categorical covariate that are shown along with 95% confidence intervals (CI). Using these plots, the significant quantiles are readily identified based on whether or not the 95% CI includes zero or not and the corresponding survival times are also displayed. For addressing a baseline risk factor known to vary at a single fixed a priori specified time (a.k.a., landmark time), we developed a SAS macro for landmark survival analysis that constructs Kaplan-Meier plots in two parts: pre- and post-landmark, along with log-rank p-values. | survival analysis, time-dependent covariate, regression analysis, proc phreg, proc quantreg | source | 6 |
| 109 | SAS ODS EXCEL Destination: Using the ID Option to Write Different Excel Workbooks at the same time | William E. Benjamin Jr. | One of the features of the new ODS EXCEL destination is the ID option. This feature allows you to define different output EXCEL Workbooks that use the same SAS Code but use different output options when writing an EXCEL Workbook. I will show you how to create three workbooks at once using this ODS option |
excel, xls, ods excel, ods pdf | source | 7 |
| 110 | SAS ODS EXCEL Destination: Using the START_AT sub-option to place your data where you want it on the Excel Worksheet | William E. Benjamin Jr. | The SAS ODS EXCEL destination statement option called "OPTIONS" has many sub-options. One of those sub- options is START_AT which allows you to place your output anywhere on the open output EXCEL worksheet. I will show you how the put your starting output data element into any row and column in the open worksheet |
excel, xls | source | 6 |
| 111 | SAS ODS EXCEL Destination: Using the STYLE Option to spruce up your Excel output workbook | William E. Benjamin Jr. | The SAS environment maintains many different output styles to use to enhance the visual display of your output data. The ODS EXCEL destination can take advantage of these SAS maintained styles to apply formatting and color schemes to your EXCEL output workbooks. I will show you how to use the ODS EXCEL destination STYLE option to enhance your output workbooks |
excel, xls, ods excel | source | 7 |
| 112 | SAS Spontaneous Combustion: Securing Software Portability through Self-Extracting Code | Troy Martin Hughes | Spontaneous combustion describes combustion that occurs without an external ignition source. With the right combination of fire tetrahedron components-including fuel, oxidizer, heat, and chemical reaction-it can be a deadly yet awe-inspiring phenomenon, and differs from traditional combustion that requires a fire source, such as a match, flint, or spark plugs (in the case of combustion engines). SAS code as well often requires a "spark" the first time it is run or run within a new environment. For example, SAS programs may operate correctly in an organization's original development environment, but may fail in its production environment until necessary folders are created, SAS libraries are assigned, control tables are constructed, or configuration files are built or modified. And, if software portability is problematic within a single organization, imagine the complexities that exist when SAS code is imported from a blog, white paper, textbook, or other external source into a new environment. The lack of software portability and the complexities of initializing new software often compel development teams to build code from scratch rather than attempting to reuse or rehabilitate existent code. One solution is to develop SAS code that flexibly builds and validates its environment and required components during execution. To that end, this text describes techniques that increase the portability, reusability, and maintainability of SAS code and demonstrates self-extracting, spontaneously combustible code that requires no spark. | sas library, macro variable | source | 12 |
| 113 | SAS Survey Report Macro for Creating User-Friendly Descriptive Summaries | Tammiee Dickenson | The use of online platforms such as Survey Monkey and Qualtrics allow for ease of survey administration. Survey items are entered into the system and the data are collected directly from respondents electronically. While these online platforms provide summaries of results, the reports are often not in a user-friendly format that is ready to share with stakeholders. The purpose of our survey report macro is to prepare descriptive summaries of item-level results from surveys collected from online systems in a user-friendly format. Features of the macro code include data steps using arrays and do loops to organize the data for analysis, PROC TABULATE to produce accessible tables of results, and use of the Output Delivery System (ODS) to create reports in Word or EXCEL format. The survey report macro produces survey results for Likert-type items from a single survey administration. In our work as program evaluators, our projects typically include multiple schools that are implementing a common program. We often wish to summarize survey results for all schools within the project to inform the program overall and for individual schools to share with school personnel for use in program planning at the school-level. The survey report macro allows flexibility to run analyses for different subsets of participants for whom the user wishes to report results. There are also user-specified choices for reporting options built into the macro code. |
proc step, proc tabulate, do loop, excel, output delivery system, proc freq, data step, proc means | source | 16 |
| 114 | SAS Visual Analytics Tricks We Learned from Reading Hundreds of SAS Community Posts | Reading Hundreds | After you know the basics of SAS Visual Analytics, you realize there are some situations that require unique strategies. Sometimes tables are not structured right or become too large for the environment. Maybe creating the right custom calculation for a dashboard can be confusing. Geospatial data is hard to work with if you haven't ever used it before. We looked through 100s of SAS Communities posts for the most common questions. These solutions (and a few extras) were extracted from the newly released Introduction to SAS Visual Analytics book. | scenario analysis, sas base, sample, dataset | source | 19 |
| 115 | SAS/GRAPH and GfK Maps: a Subject Matter Expert Winning Combination | Louise S. Hadden | SAS has an amazing arsenal of tools to use and display geographic information that is relatively unknown and underutilized. High quality GfK Geocoding maps have been provided by SAS since SAS 9.3 M2, as sources for inexpensive map data dried up. SAS has been including both GfK and "traditional" SAS map data sets with licenses for SAS/GRAPH for some time, recognizing there will need to be an extended transitional period. However, for those of us who have been putting off converting our SAS/GRAPH mapping programs to use the new GfK maps, the time has come, as the "traditional" SAS map data sets are no longer being updated. If you visit SAS MapsOnline, you will find only GfK maps in current maps. The GfK maps are updated once a year. This presentation will walk through the conversion of a long-standing SAS program to produce multiple US maps for a data compendium to take advantage of GfK maps. Products used are Base SAS and SAS/GRAPH. SAS programmers of any skill level will benefit from this presentation. |
proc step, create table | source | 21 |
| 116 | Sending SAS Data Sets and Output to Microsoft Excel | Imelda C. Go | For many of us, using SAS and Microsoft EXCEL together is inevitable. This paper is useful for a beginner who wants to know how the two can be used together and how to send SAS data and/or output to EXCEL. A number of SAS features facilitate the sharing of data between the two. There are tools in SAS Enterprise Guide (EG) that help send output data into EXCEL. With SAS/ACCESS, PROC EXPORT can convert a SAS data set into an EXCEL file. The Output Delivery System (ODS) offers a number of destinations, such as the EXCEL destination that facilitates the transfer of SAS output into EXCEL. Using Dynamic Data Exchange (DDE) is a way for a user to populate a preexisting or preformatted EXCEL file with SAS data. SAS traffic lighting (color coding of SAS output elements) in a report can also be applied to SAS output such that the traffic lighting will appear in EXCEL. Note: The following examples are based on using SAS EG in a Windows 7 environment with Microsoft Office 2010 |
excel, xls, macro call, sas output, dataset, sas enterprise guide, proc step, proc export, macro variable | source | 10 |
| 117 | Setting the Percentage in PROC TABULATE | David W. Franklin | PROC TABULATE is a very powerful procedure which can do statistics and frequency counts very efficiently, but it also it has the capability of calculating percentages on many levels for a category. This paper looks at the automatic percentage calculations that are provided, and then delves into how a user can specify the denominator for your custom percentage |
proc tabulate, proc step, proc freq | source | 7 |
| 118 | Statistical Analyses of Public Health Surveys Using SAS Survey Package | Trung Ha, Julia Soulakova |
We will discuss the key SAS survey package (SAS 9.4 or later) procedures that one can use to perform statistical analyses of complex survey data with replicate weights. We will address the topic of replicate weights and balanced repeated replications for variance estimation. The key methods for obtaining summary statistics and performing testing for quantitative and qualitative outcomes will be discussed. In addition, model building approaches for logistic regressions with fixed effects will be addressed. Examples will illustrate the concepts and procedures for smoking-related behaviors using the Tobacco Use Supplement (TUS) to the Current Population Survey (CPS) data set with 160 replicate weights. The survey is widely used to estimate the prevalence of smoking, as well as rates of smoking-related events and behaviors, e.g., quitting behaviors among smokers and attitudes towards smoking laws. Keywords: complex survey, balanced repeated replication, CPS, TUS | sample, logistic regression, logistic distribution, regression analysis, proc freq | source | 6 |
| 119 | Statistician's secret weapon: 20 ways of detecting raw data issues | Lixiang Larry Liu | Unclean clinical raw data is always statistician and statistical programmer's nightmare for all the downstream SDTM, ADaM and TFLs development work. Raw data issues could mess up the programming logic, create OpenCDISC reject, error, warning messages, and worst of all, if incorrect data is analyzed, study team could draw wrong or inaccurate conclusions regarding drug's safety and efficacy, which could put patients' safety in jeopardy and have significant impact on company's financial status. This paper will review 20 effective ways of detecting raw data issues. Since they are applied to the drug dispense, labs, and safety related CRF data (Including adverse event, medical history, concomitant therapy, drug exposure etc.), which are common for all clinical trials, these methods and their associated SAS programs could be easily used for clinical trial studies across different therapeutic areas | data governance, clinical trial, database lock, raw data, adverse events, exposure, visit, protocol deviations, create table, date variable, data validation rules, medical history, statistical programmer, randomization, proc step, concomitant medications, cdisc | source | 8 |
| 120 | Streamlining ADaM Dataset Specifications | Hunter Vega | In a large group of statistical programmers, you often find that people have a varied style for how they might create an analysis dataset specification. It could either be too detailed so that it compromises the independent programming concept or lacking in key information, e.g. how flags are to be derived. In an effort to consolidate some core practices for analysis dataset programming, we put together a training for our statistical programming and biostatistics teams to go over basic ADaM dataset concepts and the process to be followed at CROS NT. The purpose of the training was to create guidelines for how to create an analysis dataset specification, how much information one should be expected to include and when the biostatisticians should review. This collaboration of our biostatistics and statistical programming groups has improved the quality of our analysis dataset specifications as well as improved the efficiency of our programming of these datasets | adam dataset, statistical programmer, clinical programming, data science, adverse events, vital signs, data cleaning, concomitant medications, raw data, cdisc | source | 6 |
| 121 | Student Development and Enrollment Services Dashboard at UCF | Carlos Piemonti | At the University of Central Florida (UCF), Student Development and Enrollment Services (SDES) combined efforts with Institutional Knowledge Management (IKM), which is the official source of data at UCF, to venture in a partnership to bring to life an electronic version of the SDES Dashboard at UCF. Previously, SDES invested over two months in a manual process to create a booklet with graphs and data that was not vetted by IKM; upon review, IKM detected many data errors plus inconsistencies in the figures that had been manually collected by multiple staff members over the years. The objective was to redesign this booklet using SAS Web Report Studio. The result was a collection of five major reports. IKM reports use SAS Business Intelligence (BI) tools to surface the official UCF data, which is provided to the State of Florida. Now it just takes less than an hour to refresh these reports for the next academic year cycle | lookup, fact table | source | 8 |
| 122 | Super Happy Fun Times: Diagnosing and Resolving an Intermittent Failure with the SAS 9.4 Grid Workspace Server | Rebecca Hayes, Green Peach |
As any SAS user or Administrator knows - intermittent problems are often the most difficult to diagnose. This paper will explore a real world scenario in which the client experienced intermittent failures when attempting to assign a logical application server in Enterprise Guide or when attempting to validate the Workspace Server in SAS Management Console. These attempts would succeed approximately 1 out of 3 times. The issue stemmed from changes in how the Object Spawner functions in SAS 9.4 versus previous releases of SAS and how this can manifest itself in a SAS Grid environment. In this paper we will discuss and review those changes and the temporary and permanent solutions | sas grid, sas management console, sas enterprise guide | source | 6 |
| 123 | Table Lookups: Getting Started With Proc Format | John J. Cohen | Table lookups are among the coolest tricks you can add to your SAS toolkit. Unfortunately, these techniques can be intimidating both conc eptually and in terms of the programming. We will introduce one of the simplest of these techniques, employing PROC FORMAT and the CNTLIN option as part of our construct. With any luck, this will prove both easy-enough to program and more efficient to run |
proc format, proc step, lookup | source | 5 |
| 124 | Tales from the Help Desk: Solutions to Common DATA Step Tasks | Bruce F. Gilsen | In 32 years as a SAS consultant at the Federal Reserve Board, I have seen some questions about common SAS tasks surface again and again. This paper collects the most common questions related to basic DATA STEP processing from my previous "Tales from the Help Desk" papers, and provides code to explain and resolve them. The following tasks are reviewed: 1. Using the LAG function with conditional statements. 2. Avoiding character variable truncation. 3. Surrounding a macro variable with quotes in SAS code. 4. Handling missing values (arithmetic calculations versus functions). 5. Incrementing a SAS date value with the INTNX function. 6. Converting a variable from character to numeric or vice versa and keeping the same name. 7. Converting character or numeric values to SAS date values. 8. Using an array definition in multiple DATA steps. 9. Using values of a variable in a data set throughout a DATA STEP by copying the values into a temporary array. 10. Writing data to multiple external files in a DATA STEP, determining file names dynamically from data values. In the context of discussing these tasks, the paper provides details about SAS processing that can help users employ SAS more effectively. See the references for seven previous papers that contain additional common tasks. |
sysdate9, date variable, lag function, macro variable, temporary array, character variable, if-then-else, input statement, numeric variable, proc sql, proc step, put statement, file statement, missing value, numeric functions | source | 19 |
| 125 | The %FRED Macro-Finding Risk in Education Data | Fred Edora | There has been increasing attention given to the analysis of the risk of students who are disproportionally represented in special education settings, such as the overidentification of students in specific disabilities and/or racial/ethnic groups who receive special education services around the country. With recently passed regulations related to the Individuals with Disabilities in Education Act (IDEA), states and local school districts must determine if new standards in disproportionality and risk will affect how they provide services to students with disabilities. The often complicated risk ratio formulas require many repetitive calculations for multiple demographic categories. This makes the issue a perfect candidate for a macro! The %FRED macro ("Finding Risk in Education Data") uses the SAS macro facility along with array processing and the SAS Prompt Manager to help stakeholders quickly and more accurately determine if their state or local school district is at risk for disproportionality based on recommended formulas for states to use. This will allow analysts in SAS Enterprise Guide to run a robust program without having to worry about programming the calculations themselves. This paper will outline the steps from conception to completion. | relative risk, sas enterprise guide, macro variable, macro facility, excel, proc step, array | source | 6 |
| 126 | Timing Variables In Clinical Trials: Avoiding Common Mistakes And Dealing With Unforeseen Issues | Gregory Weller | Timing variables are an often overlooked part of clinical trials data collection and mapping, particularly in the SDTM and ADaM standards. However, failure to properly plan timing variable collection and mapping before study data is collected can have major consequences later on. This paper will review good practices for collecting and mapping timing information in clinical trials, from clinical database setup through SDTM and ADaM. Examples from real studies will be used to highlight the challenges that can happen when either: a) insufficient timing information is collected, or b) unforeseen issues arise in the collection of timing variables | clinical trial, adam dataset, sdtm domain, vital signs, randomization, clinical data, cdisc | source | 4 |
| 127 | Tips and Tricks for Raking Survey Data with Advanced Weight Trimming | Michael P. Battaglia | Raking to population control totals is often the final step in developing survey weights. Raking is an iterative procedure that brings the weighted sample into agreement on socio-demographic variables that are available for the sample and the population. It is primarily used to reduce unit nonresponse bias. Raking can lead to some observations ending up with extreme weights, in other words, weights that are very large or very small compared to the mean weight, resulting in inflated standard errors. In 2009, we developed a SAS raking macro that implements weight trimming during the raking iterations, ensuring that the weighted sample will agree with the population. We recently enhanced the macro to add several options related to weight trimming. There are four trimming values that can be specified: two "high end" trimming values - the individual high cap value and the global high cap value; and two "low end" trimming values -- the individual low cap value and the global low cap value. The macro also includes two trimming options. In the "OR" method an observation has its weight reduced if the value of the weight is greater than the individual high cap value or the global high cap value. An observation has its weight increased if the value of the weight is less than the individual low cap value or the global low cap value. In the "AND" method an observation has its weight reduced if the value of the weight is greater than the individual high cap value and the global high cap value. An observation has its weight increased if the value of the weight is less than the individual low cap value and the global low cap value. Using survey data, we illustrate how the new weight trimming features are used: whether to trim weight or not, by which method to trim, which end to trim, and how much to trim. We also demonstrate how to set different convergence criteria for a subset of the raking variables when raking convergence problems arise. These enhancements have proven to be of interest to the survey research community. | control total, sample, cv, sample size | source | 12 |
| 128 | Tips for Effective SAS Platform and User Administration | Michael G. Sadof | Effective administration and support of a SAS Enterprise BI environment is difficult due to the many and varied type of users and SAS products. Presented here will be some tips and techniques of supporting your environment and users. Some topics to be discussed are how to organize the folder struc ture on the platform, monitoring the environment, training users, and providing them with support, standards, and resources for self-help. To keep things running smoothly certainly involves tuning of the environment but it is essential to optimize user programs as well. Enclosed within are some ideas to gently turn your users into SAS rock stars. When the SAS environment is running efficiently and programs are delivering timely and accurate results to the users you don't have to worry and you can be happ y | sas programmer, libname statement | source | 7 |
| 129 | Tired of CALL EXECUTE? Try DOSUBL | Jueru Fan | DOSUBL was first introduced as a function in SAS V9.3. It enables the immediate execution of SAS code after a text string is passed. Macro variables that are created or updated during the execution of the submitted code are exported back to the calling environment. With this feature it can be treated as a powerful alternative to traditional CALL EXECUTE when creating macros. This paper demonstrates the principal difference between DOSUBL and CALL EXECUTE with a real-world application. With a thorough understanding of the discussion in this paper a reader should be able to apply the technique more generally | cpu time, macro variable, call execute, data step, macro call, proc contents, sas log, proc step, &variable, macro quoting function | source | 26 |
| 130 | Tornado Inflicted Damages Pattern | Vasudev Sharma | On average, about a thousand tornadoes hit the United States every year. Three out of every four tornadoes in the world occur in the United States. They damage life and property in their path and they often hit with very little, sometimes no, warning. Tornadoes cause approximately 70 fatalities and 1,500 injuries in US every year. Once a tornado destroyed an entire motel in Oklahoma and the motel's sign was later recovered in Arkansas. Tornadoes most frequently hit "Tornado Alley" which is mainly made up of Nebraska, South Dakota, Oklahoma, Texas, and Kansas. A tornado extends from a thunderstorm to the ground and appears as a funnel shaped cloud rotating with winds, which can reach 300 miles per hour and can exceed a one-mile radius. Tornadoes can travel very long distances making them very devastating. Since the ability to detect the intensity and direction of tornadoes prior to formation is limited, predicting the likelihood a tornado will form with accuracy can save many lives, as well as property. The purpose of the study is to find a pattern in the fatalities, injuries and property loss. The tools used are Base SAS, SAS Enterprise Miner, R, and Tableau. The results include statistical analysis, descriptive analysis, predictive analysis and visualizations from these tools. | anova, proc glm, sample, proc ttest, null hypothesis, post hoc test, proc genmod, proc step, heteroscedasticity, binary variable | source | 11 |
| 131 | Two Sample Parametric and Nonparametric Statistical Tests for a Large Number of Comparisons Made Easy | Dennis J. Beal, Oak Ridge |
Parametric and nonparametric two sample statistical tests have been in SAS for a long time. However, for environmental data there often are many constituents to compare their mean or median concentrations to see if they are statistically the same or not. Typically, hundreds of chemical or radionuclide constitue nts need to be statistically tested between two sampling locations. This paper provides SAS code that efficiently calculates the summary statistics and output from two sample statistical tests by constituent including p- values and a final determin ation whether they are significantly different at a given significance level. Parametric t-tests are performed when the data are normally or lognormally distributed in both samples . The nonparametric two sample Wilcoxon Rank Sum (WRS) test is performed whe n the data are neither normally nor lognormally distributed | dataset, sample, significance level, proc means, null hypothesis, proc step, homoscedasticity, proc ttest, excel, heteroscedasticity, standard deviation | source | 7 |
| 132 | UCF Visual Analytics - Dataset Development for the Undergraduate Attrition Study | Scott A. Milbuta, Michelle Parente |
The office of Institutional Knowledge Management (IKM) at the University of Central Florida (UCF) has been gradually developing many insightful and impactful applications in SAS Visual Analytics. This allowed existing information once held in discrete and disconnected set of reports to be combined and quickly seen in detail across several new dashboards. The Attrition Study Dashboard in particular required a different approach in its development as the questions being asked required more attention in order to surface properly in Visual Analytics. To do this, development of the datasets that drives this information in our Visual Analytics environment had to be redeveloped from our existing BI Portal delivered datasets. New challenges and insights were learned in executing this vision and process leading to solutions in order to build a master dataset for our Attrition Study Dashboard. This process is presented here and showcases the end result | sample, data warehouse | source | 8 |
| 133 | Understanding the influence of the day of the week in the reviews written using SAS Enterprise Miner TM and SAS Sentiment Analysis Studio | Sujal Reddy Alugubelli | In recent times, manufacturers give importance to reviews written by users on social networking and e-commerce websites. Understanding customers' needs is a critical aspect of every business which helps in satisfying their customers. With the huge number of reviews available it is an arduous job to narrow down and find significant comments or understand what external factors may influence those comments. One external factor that could be a potential influence on the customer reviews is the effect of day of the week on the way a user writes a review. This factor is analyzed and examined in this paper. According to published research, human beings react differently in different times. They tend to overreact when they are under pressures and are more accepting when they are relaxed. As most individuals are stressed on weekdays because of the workload, they are likely more stressed during weekdays and are calmer on weekends. To investigate this, in this paper analysis is done separately for weekday and weekend reviews. This may help a company to understand the attenuating effect of an external factor on the nature of the review. Reviews written on Amazon.com for two products from electronics product category are explored in this paper. Each product has more than 4000 reviews. SAS Enterprise Miner and SAS Sentiment Analysis Studio is used to analyze reviews written on weekdays and weekends. | data mining, parsing | source | 10 |
| 134 | Unleash the Power of PROC REPORT With the ODS EXCEL Destination | Devi Sekar | A new ODS destination for creating Microsoft EXCEL workbooks is available with SAS 9.4M3. This destination is an extremely easy and handy tool for producing ad-hoc as well as production EXCEL reports. The ODS EXCEL destination has several advantages over ODS ExcelXP tagset. With the ODS EXCEL destination, you can bring all those powerful features available with the REPORT procedure such as predefined styles, traffic-lighting, custom formatting, and compute block flexibility straight into your EXCEL reports. Once you start using the ODS EXCEL destination, you will quickly realize that the PRINT procedure is not sufficient to meet all the formatting demands for your EXCEL Reports. This paper covers various techniques that you can use with PROC REPORT and the ODS EXCEL destination, to make your EXCEL reports pretty and publication-ready! |
excel, proc report, proc step, ods excel, output delivery system, macro variable, options statement, xls, sas base | source | 19 |
| 135 | Using Categorical Variables in Regression Analysis | Jonas V. Bilenas | In this tutorial, we will review how to deal with categorical variables in regression models using SAS. Techniques will show how categorical variables can test for changes in intercept and slope effects in regression models. We will review how to use the CLASS statement in a number of regression procedures and we will also review how to select the reference level for categorical variables | regression analysis, nominal variable, binary variable, proc genmod, proc step, proc reg, goodness of fit, main effect, confidence interval | source | 15 |
| 136 | Using PROC REPORT and ODS STYLE Options to Make Really Great Tables | Wendi L. Wright | Questar Assessment, Inc. had a need to automate many tables in a large public report created every year. This paper describes the techniques used to automate the tables in SAS. The tables can now be created with little or no modification before being posted, making the creation of the report much more efficient. This intermediate-level paper shows examples of how to use the ODS RTF STYLE= option in PROC REPORT to customize the borders in table headers and table cells. It will also cover the many other ways the STYLE option can be used to set widths, heights, and justification and to change fonts and colors in the table's foreground and background. Also covered will be how to add subscript, superscript, and carriage returns to cell contents |
proc report, proc step, by group processing | source | 25 |
| 137 | Using SAS Enterprise Miner for Categorization of Customer Comments to Improve Services at the U.S. Postal Service | Olayemi Olatunji, United States Postal |
Delivering high-quality service and providing excellent customer experiences are performance outcome goals the U.S. Postal Service has established to measure corporate strategy success and continuous improvement efforts. Social media has opened the door for customer engagement and decision making. With the help of Twitter, Facebook, and Yelp, government agencies are more informed about how customers feel about their service and experience. Using Yelp data we will text mine comments about U.S. Postal Service customer service, retail service, mail delivery, and facility services using SAS Text Miner of SAS Enterprise Miner 7.1. The aim of this paper is to provide ways to categorize consumer comments regarding U.S. Postal Service services to improve the customer experiences at stations | parsing, data mining | source | 7 |
| 138 | Using SAS for Application Programming Interface Requests | Michael Jadoo | Application Programming Interface (API) is a method of requesting data and has been around for some time. Their primary uses are for front-end webs developers who wish to use these respective data sets for charts, tables, and maps. However, this method of data request can also be useful for data processing and analysis as fewer steps in the data products process can be achieved. In the paper we will discover how an API request can remove steps in the data production process and how to make a request for data from a major statistical accounts producer. Moreover, some useful tips will be revealed when using this method | dataset, proc http, proc step, api, macro variable, web service, data wrangling | source | 9 |
| 139 | Using SAS to Employ Propensity Score Matching in an Institutional Research Office to Create Matched Groups for Outcomes Analyses | Bobbie E. Frye, James E. Bartlett |
It is common to encounter student unit record data in the community college and to analyze the impact of educational interventions using two groups of students, those exposed to the intervention and those not exposed. Yet results are limited in that the students are not typically randomly selected into experimental and control groups. Non-random selection implies that the two groups of students may be very different on key factors that affect the results of analyses through self-selection bias and other differences. Propensity matching is a technique designed to simulate an experimental design, controlling for selection bias and creating almost equivalent experimental and comparison groups on key indicators. Propensity score matching using key characteristics such as diagnostic/placement test scores, Pell status, age, gender and race/ethnicity will be used to select the experimental and comparison groups. Comparisons of student outcomes using propensity matching has been used to yield less biased results than are derived using simple comparisons (Rojewski et al., 2010). | propensity score, binary variable, propensity score matching, sample, predictor variable, proc step, response variable, logistic distribution, regression analysis, logistic regression, outlier, sample size, effect size, missing value, proc logistic, proc ttest, standard deviation, proc univariate, sas output, box plot, proc freq | source | 19 |
| 140 | Using SAS to examine the relationship between nursing attitudes towards suicidal patients in the Emergency Department (ED) | Abbas S. Tavakoli, Belinda Beaver, Stephanie Burgess |
Mental illness visits in Emergency Department (ED) are annually 12 million in United States. There is need to prepare staff with mental issues. The purpose of this paper is using SAS to examine the impact of an educational initiative on nurses' attitudes about patients expressing suicidal ideation in the emergency departments 2 rural hospitals. Two rural communities ED were used for this study. Deliberate Self-Harm Questionnaire (ADSHQ) used to assess attitudes included confidence, empathy, dealing effectively, and coping. The outcomes were attitude of nurses towards suicidal patients were measured at baseline and after intervention. The intervention for this study required that specialized training be provided to nursing staff working. Aggregated data was collected from health stream when the study was completed. Each Item was analyzed and reported in a percentage or rate per item. PROC Mean, Freq, ttest, and univariate used to analyze this data. T-Test and Wilcoxon signed ranked test used to examine the difference of means for attitude of nurse pre and post of intervention. The results showed there was improvement in attitude nurses towards suicidal patients after intervention. However, the results were not statistically significant. |
standard deviation, wilcoxon signed-rank, proc step, rank test, proc means, paired t-test, parametric test, nonparametric test, p-value | source | 6 |
| 141 | Using SAS to Proactively Monitor Drug Alerts: Implementing the URAC "Drug-Drug Interactions" Measure for a Specialty Pharmacy | Karen E. Wallace | To satisfy annual URAC accreditation requirements, one of the mandatory reported "Safe Care" domain measures is "Drug-Drug Interactions" (DM2012-13). This measure relies upon pharmacy and enrollment data to assess the percentage of patients who: 1) received a prescription for a target medication during the measurement period, and 2) were dispensed a concurrent prescription for a precipitant medication. The resultant data are stratified by line of business (URAC, 2016). Using SAS Base programming, in conjunction with technical and clinical review, the preliminary methodology: 1) imports and joins several files using the data elements of patient identifier as well as medication and insurance plan, 2) cleanses the mixed-case, punctuated MediSpan-generated drug alert text, 3) categorizes the text into discrete flags based upon predetermined criteria, 4) sums the number of flags per observation, and finally, 5) segments data by insurance type. The reporting showcases de-identified, summarized descriptive statistics that may be used to better monitor drug alerts and, more importantly, intervene where there is perceived harm in the patient population. This paper is intended for the SAS novice who may be challenged on ways to perform on-the-fly textual analysis with a mindset towards implementing standardized, automated reporting after receiving requisite oversight and sign-off. | medical history, excel | source | 8 |
| 142 | Using the Force of Python and SAS Viya on Star Wars Fan Posts | Grace Heyne | The wealth of information available on the Internet includes useful and interesting data, and it is easy to gather using the connection between SAS Viya and Python. Since the announcement at SAS Global Forum 2016 that SAS Viya supports running CAS actions within Python, SAS programmers have been exploring the many ways that SAS works well with Python. With easy transitions between the Python language and SAS, this connection allows programmers to use the best of both languages to build more powerful tools for data analysis. This paper includes specific examples of ways to use both SAS and Python together in Jupyter Notebooks to leverage Python's data collection, cleaning, and management tools, along with ways to gather data from the web and perform necessary transformations to prepare and analyze the data | sas viya, cas action, proc step, proc sql | source | 8 |
| 143 | Visualization of Patient Electronic Records to Support Exploratory Analysis and Variable Derivation of Categorical Data | Steven Thomas, Costel Chirila, Mary Beth |
Background Electronic medical records (EMR) have become a standard data source for epidemiological, outcomes, and health services research. However, there are challenges caused by the size and complexity of EMR data. Data are collected continuously across multiple systems and are stored in a variety of structures. Systems and structures can include free text, long or wide forms, and complex temporal information. These complexities make EMR data similar to an evolving ecosystem rather than a static source found in most studies. In a natural ecosystem, data sources are assessed to ensure that information is consistent with expectations. Institutions should approach EMR data in a similar manner to provide insight and build confidence among team members with diverse backgrounds. New tools and processes need to be developed that support assessment of analytic decisions and are available to all members of the team. Methods This paper proposes three visual tools to utilize in exploratory analyses before variable derivation. These tools are designed to promote discussion and build consensus between team members using EMR data. They allow examination of individual patient records and trends across time so common operational considerations (e.g., defining variables via multiple features, selection of time-windows) are addressed using both the data and therapeutic expertise. This paper will present SAS graphic language templates for patient profiles, cumulative heat maps, and Sankey diagrams with example discussions and decisions that each visual is designed to support. Results/Conclusions Studies seeking to maximize use of EMR data involve multiple stakeholders that need to understand nuances in the data. Visualizations can facilitate team discussions and improve the process of feature extraction, variable construction, and project planning. These visuals can be particularly useful for sequential analysis, treatment patterns, and defining episodes of care, but all studies using EMR can benefit from the use of visuals. | spaghetti plot, do loop | source | 12 |
| 144 | Wait, I don't want to be the Linux Administrator for SAS VA Jonathan Boase; Zencos Consulting | Jonathan Boase | Whether you are a new SAS administrator or switching to a Linux environment, you have a complex mission. This job becomes even more formidable when you are working with a system like SAS Visual Analytics that requires multiple users loading data daily. Eventually a user will have data issues or create a disruption that causes the system to malfunction. When that happens, what do you do next? In this paper, we will go through the basics of a SAS Visual Analytics Linux environment and how to troubleshoot the system when issues arise | sas administrator, sas management console | source | 8 |
| 145 | When ANY Function Will Just NOT Do | Richann J. Watson | Have you ever been working on a task and wondered whether there might be a SAS function that could save you some time? Let alone, one that might be able to do the work for you? Data review and validation tasks can be time-consuming efforts. Any gain in efficiency is highly beneficial, especially if you can achieve a standard level where the data itself can drive parts of the process. The ANY and NOT functions can help alleviate some of the manual work in many tasks such as data review of variable values, data compliance, data formats, and derivation or validation of a variable's data type. The list goes on. In this poster, we cover the functions and their details and use them in an example of handling date and time data and mapping it to ISO 8601 date and time formats | raw data, time variable | source | 8 |
| 146 | Working in SGPLOT: Understanding the General Logic of Attributes | James E. Blum | ODS Graphics use common style elements for distinct graphical entities-text, symbols, fills and lines- through attribute options. Most graphical entities produced, either by default or requested via a specific option, can have their styles modified with an ATTRS-type option. The syntax of such options is built to be consistent across instances of these graphical entities for most plotting statements that generate them. Knowing what graphical entities are generated by various statements and options, along with understanding how SAS categorizes graphical entities and their style elements, can help make style modification much easier to understand. Examples will focus on the commonalities present across a wide variety of plotting statements and options within them. Some non-standard style elements will be considered |
proc sgplot, ods graphics, output delivery system, proc step, bar chart, sample | source | 12 |
| 147 | YAGI: a tool to help Newcomers to SAS efficiently learn Code and Syntax | Steph-Yves Louis | SAS is one of the most widely used software packages for statistical calculations and data manipulation purposes across many fields. While books and online resources already exist to help with syntax, researching these materials can be tedious and time - consuming. For a new student, the learning can be overwhelming. Even for an individual familiar with the program, attempts to retrieve code from other users' codes may take longer than expected. Considering the above issues, YAGI, a software designed to facilitate the learning for novice coders and to represent a personal dictionary of codes for any returning user, may present a solution | proc means, clinical programming | source | 7 |
| 148 | You as a Brand: Tips for Managing Your Data Career | Patricia S. Aanderud | Yes! You are a brand. The branding you advertise to the target market (your manager and colleagues) is very similar and just as important as the branding that goes into a product or company. All brands require management to be successful. During this talk, we will review how you establish your brand, using social media to promote your brand, and simple ways you can maintain your brand. You will walk away with some strategies you can implement immediately | data science, data visualization | source | 5 |
| 149 | Zen and the Art of Problem Solving | David B. Horvath | Although software development is taught as a STEM out of Science or Engineering schools, it is as much an art or craft - a creative process - as a science. This presentation focuses on innovative problem solving techniques the tools and techniques to use when your normal process just doesn't seem to get you to a solution. Much of the information in this talk is based on Robert Pirsig's Zen and the Art of Motorcycle Maintenance, which, although it focuses on Motorcycles, applies to all kinds of problem spaces (and Pirsig was a tech writer for IBM). These techniques have served me well over the years. The difference in art versus science approaches is actually supported by the way the brain works | problem solving, account | source | 8 |
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