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BUGSSESUG 2014 Papers - Index
109 papers. Source: SESUG (SouthEast SAS Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | %Destroy() a Macro With Permutations | Brandon L. Welch, James Vaughan |
The SAS Macro is a powerful tool. It minimizes repetitive tasks and provides portable tools for users. These tools are sometimes delivered to clients and a quality macro is necessary. For example, when developed to perform a complicated statistical test, we want the macro to produce accurate results and a clean log. To accomplish this, we insert parameter checks. Depending on the complexity of the macro it is sometimes difficult to perform a thorough check. We introduce the %Destroy()macro which uses the call RANPERK routine to permute a list of arguments. These arguments are then passed to the macro you test. We show how to add appropriate parameter checks to ensure on subsequent runs of %Destroy() the testing macro produces the desired results. While this article targets a clinical computing audience, the techniques we present offer a good overview of macro processing that will educate SAS programmers of all levels across various disciplines. | macro variable, macro definition | source | 8 |
| 2 | %DISCIT Macro: Pre-screening continuous variables for subsequent binary logistic regression analysis through visualization | Mohamed S. Anany | Prescreening variables to identify potential predictors to enter into the model is an important stage in any modeling process. The goal is to select the variables that will result in the "best" model. In binary logistic regression, when there are many independent variables that could potentially be included in the model, it is always a good practice to perform bivariate analysis between the dichotomous variable (dependent) and the independent variables. The independent variables come in many forms; binary, continuous, nominal categorical, and/or ordinal categorical variables. This presentation is concerned with identifying candidate continuous variables by performing a bivariate analysis. The analysis is based on a two-sample t -test, a graphical panel to visualize the relationship of the continuous variable with the dichotomous dependent variable, recoding the continuous variable into two different ordinal forms and adjusting their scale through odds and log odds transformations if needed, and collapsing similar groups of the recoded form of the continuous variable to ensure (or improve) their linear relationship if exists. Also, we make use of the information value which gives an indication of the predictive power of the independent variable in capturing the dichotomous variable. The Information value is specific to the credit and financial industries. The SAS DISCIT macro was developed to make this prescreening process easier for analysts. | interval variable, binary variable, sample, predictor variable, response variable, logistic regression, logistic distribution, histogram, regression analysis, bivariate analysis, sample size, macro call | source | 6 |
| 3 | 1 How to Build a Data Dictionary - In One Easy Lesson | Gary E. Schlegelmilch | In the wonderful world of programming, the Child Left Behind is usually documentation. The requirements may be thoroughly analyzed (usually on a combination of phoned-in notes, e-mails, draft documents, and the occasional cocktail napkin). Design is often on the fly, due to various restraints, deadlines, and in-process modifications. And doing documentation after the fact, once the program is running, is, well, a great idea - but it often doesn't happen. Some software tools allow you to build flow diagrams and descriptions from existing code and/or comments embedded in the program. But in a recent situation, there was a lament that a system that had been running in the field for quite a while had no Data Dictionary - and one would be really handy for data standardization and data flow. SAS to the rescue! | data dictionary, proc transpose, proc step | source | 7 |
| 4 | @/@@ This Text file. Importing Non-Standard Text Files using @,@@ and / Operators. | Russell Woods | SAS in recent years has done a fantastic job at ad ding newer and more powerful tools to the analyst and developer tool boxes, but these options are only as effective as the data that is available to them. Most of you would have no trouble dealing with a simple delimited or column formatted text file. However, data can often be provided in a non- standard format that must be parsed before analysis can be performed, or the final results have very specific formatting rules that must be adhered to. In these cases we can use some simple SAS operators, '@', '@@' and '/' in conjunction with conditional statements to interact with any flat file that has some kind of distinguishable pattern. In this presentation I will demonstrate the step-by-step process I used to analyze several non-standard text files and create import specifications capable of importing them into SAS | txt, input statement, if-then-else, sample | source | 12 |
| 5 | A Comprehensive Automated Data Management System For Clinical Trials | Heather F. Eng, Jason A. Lyons, Theresa M. Sax |
A successful data coordinating center for multicenter clinical trials and registries must provide timely, individualized, and frequent feedback to investigators and study coordinators over the course of data collection. Investigators require up-to-date reports to help them monitor subject accrual and retention, randomization balance, and patient safety markers. Study coordinators need to know what data are expected or are delinquent, and they need to know about errors to be corrected, such as missing data or data that don't pass validity and logical consistency tests. Frequent monitoring can reveal systemic issues in procedures that require remedial adjustments to keep the project from being at risk. Data managers at the University of Pittsburgh's Epidemiology Data Center in the Graduate School of Public Health have developed an integrated system to collect and import data from multiple and disparate sources into a central relational database, subject it to comprehensive quality control procedures, create reports accessible on the web, and email individualized reports to investigators and study coordinators, all on an automated and scheduled basis. Post-hoc routines monitor execution logs, so that unexpected errors and warnings are automatically emailed to data managers for same-day review and resolution. The system is developed almost exclusively using SAS software. While SAS is best known among clinical trialists as statistical software, its strength as a data management tool should not be overlooked. With its strong and flexible programming capabilities for data manipulation, reporting and graphics, web interfacing, and emailing, it provides the necessary infrastructure to serve as a single platform for the management of data collected in clinical trials and registries. This paper will describe the modules of the system and their component programs as they were developed for the Computer-Based Cognitive-Behavioral Therapy (CCBT) Trial currently underway at the University of Louisville and the University of Pennsylvania, with data coordination at the University of Pittsburgh. | missing value, libname statement, clinical trial, ods html, sql server, proc sql, proc step, data governance, quality control, format statement, sas log, proc printto, excel, file statement, randomization, adverse events | source | 12 |
| 6 | A National Study of Health Services Utilization and Cost of Care with SAS: Analyses from the 2011 Medical Expenditure Panel Survey | Seungyoung Hwang, Johns Hopkins |
Objective: To show how to examine the health services utilization and cost of care associated with mood disorders among the older population aged 65 or older in the United States. Research Design and Methods: A cross-sectional study design was used to identify two groups of elders with mood disorders (n = 441) and without mood disorders (n = 3,822) using the 2011 Medical Expenditure Panel Survey (MEPS). A multivariate regression analysis using PROC SURVEYREG procedure in SAS was conducted to estimate the incremental health services and direct medical costs (inpatient, outpatient, emergency room, prescription drugs, and other) attributable to mood disorders. Measures: Clinical Classification code aggregating ICD-9-CM codes for depression and bipolar disorders. Results: The prevalence of mood disorders among individuals aged 65 or older in 2011 was estimated at 11.38% (5.17 million persons) and their total direct medical costs were estimated at approximately $81.82 billion in 2011 U.S. dollars. After adjustment for demographic, socioeconomic, and clinical characteristics, the additional incremental health services utilization associated with mood disorders for hospital discharges, number of prescriptions filled, and office-based visits were 0.14 ± 0.04, 4.76 ± 1.04, and 17.29 ± 2.07, respectively (all p<0.001). The annual adjusted mean incremental total cost associated with mood disorders was $5,957 (SE: $1,294; p<0.0001) per person. Inpatient, prescription medications, and office-based visits together accounted for approximately 78% of the total incremental cost. Conclusion: The presence of mood disorders for older adults has a substantial influence on health services utilization and cost of care in the U.S. Significant savings associated with mood disorders could be realized by cost effective prescription medications which might reduce the need for subsequent inpatient or office-based visits. Key words: SAS; health services utilization; healthcare costs; mood disorders; older adults. |
sample, health insurance, regression analysis, proc step | source | 12 |
| 7 | A Non-Standard Report Card - Informing Parents About What Their Children Know | Daniel Ralyea | macro variable, proc step, proc sql | source | 5 | |
| 8 | A Quick View of SAS Views | Elizabeth Axelrod | Looking for a handy technique to have in your toolkit? Consider SAS Views, especially if you work with large datasets. After a brief introduction to Views, I'll show you several cool ways to use them that will streamline your code and save workspace. | data step, cpu time, proc step, view, proc sort, dataset, proc freq | source | 7 |
| 9 | A Review of "Free" Massive Open Online Content (MOOC) for SAS Learners | Kirk Paul Lafler | Leading online providers are now offering SAS users with "free" access to content for learning how to use and program in SAS. This content is available to anyone in the form of massive open online content (or courses) (MOOC). Not only is all the content offered for "free", but it is designed with the distance learner in mind, empowering users to learn using a flexible and self-directed approach. As noted on Wikipedia.org, "A MOOC is an online course or content aimed at unlimited participation and made available in an open access forum using the web." This presentation illustrates how anyone can access a wealth of learning technologies including comprehensive student notes, instructor lesson plans, hands-on exercises, PowerPoints, audio, webinars, and videos. | sas base, sas studio, sample | source | 16 |
| 10 | A Strip Plot Gets Jittered into a Beeswarm | Shane Rosanbalm | The beeswarm is a relatively new type of plot and one that SAS does not yet produce automatically (as of version 9.4). For those unfamiliar with beeswarm plots, they are related to strip plots and jittered strip plots. Strip plots are scatter plots with a continuous variable on the vertical axis and a categorical variable on the horizontal axis (e.g., systolic blood pressure vs. treatment group). The strip plot is hamstrung by the fact that tightly packed data points start overlaying one another, obscuring the story that the data are trying to tell. A jittered strip plot seeks to remedy this problem by randomly moving data points off of the categorical center line. Depending on the volume of data and the particular sequence of random jitters, this technique does not always eliminate all overlays. In order to guarantee no overlays we must adopt a non-random approach. This is where the beeswarm comes in. The beeswarm approach is to plot data points one at a time, testing candidate locations for each new data point until one is found that does not conflict with any previously plotted data points. The macro presented in this paper performs the preliminary calculations necessary to avoid overlays and thereby produce a beeswarm plot. | data step, nominal variable, interval variable, macro loop, scatter plot | source | 12 |
| 11 | 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. Furthermore, 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 easier 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 or SAS/INTRNET. 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 |
| 12 | Analysis of Zero Inflated Longitudinal Data Using Proc NLMIXED | Delia C Voronca, Leonard E Egede, Mulugeta Gebregziabher, Ralph H. Johnson |
Background: Commonly used parametric models may lead to erroneous inference when analyzing count or continuous data with excess of zeroes. For non-clustered data, the most common models used to address the issue for count outcomes are zero inflated Poisson (ZIP), zero inflated negative binomial (ZINB), hurdle Poisson (HP) and hurdle negative binomial (HNB) and Gamma Hurdle (HGamma), truncated Normal Hurdle (HTGauss), hurdle Weibull (HWeibull) and zero inflated Gaussian (ZIGauss) are used for for continuous outcomes. Objective: Our goal is to expand these for modeling clustered data by developing a unified SAS macro based on PROC NLMIXED. Data and Methods: The motivating data set comes from a longitudinal study in an African America population with poorly controlled type 2 diabetes conducted at VA and MUSC centers in SC between 2008 and 2011. A total of 256 subjects were followed for one year and measures were taken at baseline and at month 3, 6 and 12. post baseline after the subjects were randomly assigned to four treatment groups: Telephone-delivered diabetes knowledge/information, Telephone-delivered motivation/behavioral skills training intervention, Telephone-delivered diabetes knowledge/information and motivation/behavioral intervention and Usual Care. The main goal of the study was to determine the efficacy of the treatment groups in relation to the usual care group in reducing the levels of hemoglobin A1C at 12 months. We use these data to demonstrate the application of the unified SAS macro. Results: We show that using the unified SAS macro improves the efficiency of analyzing multiple outcomes with zero-inflation and facilitates model comparison. |
proc mixed, poisson distribution, random intercept, likelihood function, random slope, macro definition, macro variable, proc step, binomial distribution, %if-%then-%else, macro parameter, %macro parameters, macro loop, proc calis, keyword parameter, correlation, sample, probability distribution, binary variable, unique key | source | 18 |
| 13 | Analyzing Multilevel Models with the GLIMMIX Procedure | Min Zhu | Hierarchical data are common in many fields, from pharmaceuticals to agriculture to sociology. As data sizes and sources grow, information is likely to be observed on nested units at multiple levels, calling for the multilevel modeling approach. This paper describes how to use the GLIMMIX procedure in SAS/STAT to analyze hierarchical data that have a wide variety of distributions. Examples are included to illustrate the flexibility that PROC GLIMMIX offers for modeling within-unit correlation, disentangling explanatory variables at different levels, and handling unbalanced data. Also discussed are enhanced weighting options, new in SAS/STAT 13.1, for both the MODEL and RANDOM statements. These weighting options enable PROC GLIMMIX to handle weights at different levels. PROC GLIMMIX uses a pseudolikelihood approach to estimate parameters, and it computes robust standard error estimators. This new feature is applied to an example of complex survey data that are collected from multistage sampling and have unequal sampling probabilities. |
proc mixed, covariance, proc glimmix, sample, random intercept, proc step, estimate, likelihood function, proc calis | source | 19 |
| 14 | ANOVA_HOV: A SAS Macro for Testing Homogeneity of Variance in One-Factor ANOVA Models | Diep T. Nguyen, Thanh V. Pham, Patricia Rodríguez de Gil, Tyler Hicks, Yan Wang, Isaac Y. Li, Aarti P. Bellara, Jeanine L. Romano, Eun Sook Kim, Harold Holmes, Yi-Hsin Chen, Jeffrey D. Kromrey |
Variance homogeneity is one of the critical assumptions when conducting ANOVA as violations may lead to perturbations in Type I error rates. Previous empirical research suggests minimal consensus among studies as to which test is appropriate for a particular analysis. This paper provides a SAS macro for testing the homogeneity of variance assumption in one-way ANOVA models using fourteen different approaches. Using simulation met hods, the fourteen tests were compared in terms of their Type I error rate and statistical power. KEYWORDS: Analysis of variance, homogeneity of variance assumption, simulation study | type i error, sample, bootstrap, anova, significance level, sample size, null hypothesis, degrees of freedom, harmean, type i error control, numeric functions, power analysis, response variable, predictor variable, macro definition | source | 12 |
| 15 | Application Development Techniques Using PROC SQL | Kirk Paul Lafler | Structured Query Language (SQL) is a database language found in the base-SAS software. It permits access to data stored in data sets or tables using an assortment of statements, clauses, options, functions, and other language constructs. This Hands On Workshop illustrates core concepts as well as SQL's many applications, and is intended for SAS users who desire an overview of this exciting procedure's capabilities. Attendees learn how to construct SQL queries, create complex queries including inner and outer joins, apply conditional logic with case expressions, create and use views, and construct simple and composite indexes. | proc sql, proc step, dictionary tables, where statement, select, inner join, macro variable, sas library, sas log, macro facility, view, create table, group by, macro call, order by, right join | source | 17 |
| 16 | Build your Metadata with PROC CONTENTS and ODS OUTPUT | Louise S. Hadden | Simply using an ODS destination to replay PROC CONTENTS output does not provide the user with attractive, usable metadata. Harness the power of SAS and ODS output objects to create designer multi-tab metadata workbooks with the click of a mouse! |
proc contents, proc step, excel, xls, data step, proc surveyfreq | 14 | |
| 17 | Build your Metadata with PROC CONTENTS and ODS OUTPUT | Louise S. Hadden | Simply using an ODS destination to replay PROC CONTENTS output does not provide the user with attractive, usable metadata. Harness the power of SAS and ODS output objects to create designer multi-tab metadata workbooks with the click of a mouse! |
proc contents, proc step, excel, xls, data step, proc surveyfreq | source | 14 |
| 18 | Bulletproof Macros: Avoiding Macro Name Collisions | David H. Abbott, Veterans Affairs |
The SAS macro facility provides an opportunity for a SAS programmer to produce chunks of SAS code that function as reusable components and provide value to a broad audience. However, if users run into unexpected difficulties in using a macro, both they and their colleagues quickly lose interest in taking advantage of such macros. One source of unexpected difficulties is macro name collision - the macro intended for reuse inadvertently overwrites a preexisting macro of the same name. Ensuring that the users of a macro don't get caught in this trap is complicated, but this paper provides a practical solution. The two keys to the solution are: 1) a macro (written by Rick Langston of SAS) that checks for the prior existence of a given macro, either already compiled or reachable via SAS autocall facility and 2) a simple second macro that allows the macro intended for reuse to be compiled only if a macro of the same name is not already present | autocall macro, macro definition | source | 5 |
| 19 | Calculating the Most Expensive Printing Jobs | Roger L. Goodwin | As the SAS manual states, the macro facility is a tool for extending and customizing SAS and for reducing the amount of text that the programmer must enter to do common tasks. Programmers use SAS macros for mundane, repetitive tasks. In this application, we present a SAS macro that calculates the top twenty most expensive printing jobs for each Federal agency. Given the request for the top twenty most expensive printing jobs, it became apparent that this request would become repetitive [H. Paulson 2008]. The US Government Printing Office anticipated an increase for financial summaries from government agencies and developed the following SAS macro specifically for Treasury's request. GPO has contracts with most Federal government agencies to procure print. GPO can produce a report for the top twenty most expensive printing jobs for each Federal agency. This, of course, assumes the agency does business with GPO. | macro definition, macro variable, data step, libname statement, macro facility, proc step | source | 5 |
| 20 | Combining Multiple Date-Ranged Historical Data Sets with Dissimilar Date Ranges into a Single Change History Data Set | Jim Moon | This paper describes a method that uses some simple SAS macros and SQL to merge data sets having related data and containing rows with varying effective date ranges. The data sets are merged into a single data set that represents a serial list of snapshots of the merged data, as of a change in any of the effective dates. While simple conceptually, this type of merge is often problematic when the effective date ranges are not consecutive or consistent, when the ranges overlap, or when there are missing ranges from one or more of the merged data sets. The technique described is used by the Fairfax County Human Resources Department to combine various employee data sets (Employee Name and Personal Data, Personnel Assignment and Job Classification, Personnel Actions, Position-Related data, Pay Plan and Grade, Work Schedule, Organizational Assignment, and so on) from the County's SAP-HCM ERP system into a single Employee Action History/Change Activity file for historical reporting purposes | proc sql, missing value, union all, proc step, sample | source | 9 |
| 21 | Configurable SAS Framework for managing SAS OLAP Cube based Reporting System | Ahmed Al-Attar | This paper illustrates a high-level infrastructure discussion with some explanation of the SAS codes that are used to implement a configurable batch framework for managing and updating SAS OLAP Cubes. The framework contains collection of reusable parameter driven SAS Base macros, SAS Base custom programs and UNIX/LINUX shell scripts. This collection manages typical steps and processes used for manipulating SAS files and executing SAS statements. The SAS Base macro collections contains a group of Utility Macros that includes Concurrent /Parallel Processing Utility MacrosSAS Metadata Repository Utility MacrosSPDE Tables Utility MacrosTable Lookup Utility MacrosTable Manipulation Utility MacrosOther Utility Macros and a group of OLAP related Macros, that includes OLAP Utility MacrosOLAP Permission Table Processing Macros | macro variable, sas base, macro call, proc step | source | 24 |
| 22 | Connect with SAS Professionals Around the World with LinkedIn and sasCommunity.org | Kirk Paul Lafler | Accelerate your career and professional development with LinkedIn and sasCommunity.org. Establish and manage a professional network of trusted contacts, colleagues and experts. These exciting social networking and collaborative online communities enable users to connect with millions of SAS users worldwide, anytime and anywhere. This presentation explores how to create a LinkedIn profile and social networking content, develop a professional network of friends and colleagues, join special-interest groups, access a Wiki-based web site where anyone can add or change content on any page on the web site, share biographical information between both communities using a built-in widget, exchange ideas in Bloggers Corner, view scheduled and unscheduled events, use a built-in search facility to search for desired wiki-content, collaborate on projects and file sharing, read and respond to specific forum topics, and more. | sample | source | 15 |
| 23 | Creating a Hyperbolic Graph Using the SAS Annotate Facility | Bill Bland, Liza M. Thompson |
In order to optimize their rate design, electric utilities analyze their customers' bills and costs for electricity by looking at the each hour's use of demand. These graphs of kWh energy usage versus hours of use are produced on a monthly basis (from 0 to 730 hours). The resulting graphs are typically curvilinear. To allow for an easier rate and cost comparison, we wanted the ability to plot hyperbolic hours, as well as cost curves, on the same graph. To do this, we applied a hyperbolic transformation to the hours use axis. This linearized the graph and made it easier to interpret. For our analysis, it is necessary to graph cost and price versus the hyperbolic axis, but at the same time show the original hours use axis. PROC GPLOT does not allow multiple X axes. Therefore, we solve the problem using the annotate facility. In this presentation, we will show you a step-by-step example of how we changed cost graphs for easier analysis and explain the code we used. |
in=, proc gplot | source | 9 |
| 24 | Creating Health Maps Using SAS | Shamarial Roberson, Charlotte Baker |
There are many different programs that have been developed to map data. However, SAS users do not need to always go outside of their SAS installation to map data. SAS has many built-in options for mapping that, with a bit of knowledge, can be just as good as advanced external programs. This paper will give an introduction to how to create health maps using PROC GMAP and compare the results to maps created in ArcGIS. |
proc step, proc gmap | source | 8 |
| 25 | Dashboards with SAS Visual Analytics | Patricia S. Aanderud, Michelle Homes |
Learn the simple steps for creating a dashboard for your company and then see how SAS Visual Analytics makes it a simple process. In this session, you will learn tips for designing dashboards, loading what may seem like impossible data, and build visualizations that guide users toward the next level of analysis. | cpu time, sas administrator, proc step, sas enterprise guide, memory usage, data mining, sas management console, business intelligence, proc hpsplit, proc sql, sample | source | 20 |
| 26 | Debugging SAS Code in a Macro | Bruce F. Gilsen | Debugging SAS code contained in a macro can be frustrating because the SAS error messages refer only to the line in the SAS log where the macro was invoked. This can make it difficult to pinpoint the problem when the macro contains a large amount of SAS code. Using a macro that contains one small DATA STEP, this paper shows how to use the MPRINT and MFILE options along with the fileref MPRINT to write just the SAS code generated by a macro to a file. The ""de-macroified"" SAS code can be easily executed and debugged. |
sas log, options statement | source | 10 |
| 27 | Design of Experiments (DOE) Using JMP | Charles E. Shipp | JMP has provided some of the best design of experiment software for years. The JMP team continues the tradition of providing state-of-the-art DOE support. In addition to the full range of classical and modern design of experiment approaches, JMP provides a template for Custom Design for specific requirements. The other choices include: Screening Design; Response Surface Design; Choice Design; Accelerated Life Test Design; Nonlinear Design; Space Filling Design; Full Factorial Design; Taguchi Arrays; Mixture Design; and Augmented Design. Further, sample size and power plots are available. We show an interactive tool for n-Factor Optimization in a single plot. | sample size, sample | source | 3 |
| 28 | Design of Experiments (DOE) Using JMP | Charles E. Shipp | JMP has provided some of the best design of experiment software for years. The JMP team continues the tradition of providing state-of-the-art DOE support. In addition to the full range of classical and modern design of experiment approaches, JMP provides a template for Custom Design for specific requirements. The other choices include: Screening Design; Response Surface Design; Choice Design; Accelerated Life Test Design; Nonlinear Design; Space Filling Design; Full Factorial Design; Taguchi Arrays; Mixture Design; and Augmented Design. Further, sample size and power plots are available. We show an interactive tool for n-Factor Optimization in a single plot. | quality control, sample size | 4 | |
| 29 | DFAST & CCAR: One size does not fit all | Charyn Faenza | In 2014, for the first time, mid-market banks (consisting of banks and bank holding companies with 10-50 bn in consolidated assets) were required to submit Capital Stress Tests to the federal regulators under the Dodd-Frank Wall Street Reform and Consumer Protection Act (DFAST). This is a process large banks have been going through since 2011; however, mid-market banks are not positioned to commit as many resources to their annual stress tests as their largest peers. Limited human and technical resources, incomplete or non-existent detailed historical data, lack of enterprise-wide cross functional analytics teams, and limited exposure to rigorous model validations, are all challenges mid-market banks face. While there are fewer deliverables required from the DFAST banks, the scrutiny the regulators are placing on the analytical modes is just as high as their expectations for CCAR banks. This session is designed to discuss the differences in how DFAST and CCAR banks execute their stress tests, the challenges facing DFAST banks, and potential ways DFAST banks can leverage the analytics behind this exercise. | stress testing, data cleaning, model validation | source | 9 |
| 30 | Don't be binary! Tales of Non-binary Categorical Regression | Charlotte Baker | It is not always optimal to reorganize your data into two levels for regression. To prevent the loss of information that occurs when categories are collapsed, polytomous regression can be used. This paper will discuss situations in which polytomous regression can be used and how you can write the code. | regression analysis, response variable, logistic distribution, proc logistic, estimate, odds ratio, logistic regression, likelihood function, proc step, predictor variable, nominal variable, maximum likelihood estimation | source | 6 |
| 31 | Enhancements to Basic Patient Profiles | Scott Burroughs | Patient Data Viewers are becoming more prevalent in the pharmaceutical industry, but not all companies use them nor need them for all situations. Old-fashioned patient profiles still have use in today's industry, but how can they be enhanced? Missing data, bad data, and outliers can affect the output and/or the running of the program. Also, relying on analysis data sets that need to be run first by others can affect timing (vacations, out-of-office, busy, etc.). As always, there are things you can do to make them look prettier in general. This paper will show how to solve these issues and make the program more robust | proc step, proc report, ods pdf, macro call, macro definition, %if-%then-%else | source | 12 |
| 32 | Evaluating Additivity of Health Effects of Exposure to Multiple Air Pollutants Given Only Summary Data | Pollutants Given | A research team is interested in determining whether or not health effects of exposure to a mixture of air pollutants is additive, using summary statistics found in published toxicology studies. Additivity is defined as no significant difference between the effects of exposure to the mixture and the sum of the effects of exposure to each individual component of that mixture. The studies of interest to the research team typically did not test for this difference, even though the study design often made it possible. Many, however, did provide three summary statistics sufficient to reproduce the test exactly (number of subjects [n], mean response, and standard deviation). Using SAS, we can perform the analysis of interest given those summary statistics. First, SAS/STAT can be used to generate datasets standardized to those three statistics for each study |
proc step, confidence interval, standard deviation, proc means, raw data, forest plot, proc glm, proc sgplot, proc genmod, anova, macro variable, proc iml, macro definition | source | 9 |
| 33 | Evaluating and Mapping Stroke Hospitalization Costs in Florida | Shamarial Roberson, Charlotte Baker, Jamie Forrest |
Stroke is the fourth leading cause of death and the leading cause of disability in Florida. Hospitalization charges related to stroke events have increased over the past ten years even while the number of hospitalizations have remained steady. North Florida lies in the Stroke Belt, the region of the United States with the highest stroke morbidity and mortality. This paper will demonstrate the use of SAS to evaluate the influence of socio-economic status, sex, and race on total hospitalization charges by payer type in North Florida using data from the State of Florida Agency for Health Care Administration and the Florida Department of Health Office of Vital Statistics. | proc step, proc freq, proc means | source | 6 |
| 34 | Expanding the Use of Weight of Evidence and Information Value to Continuous Dependent Variables for Variable Reduction and Scorecard Development | Alec Zhixiao Lin, Tung-Ying Hsieh |
Weight of Evidence (WOE) and Information Value (IV) have become important tools for analyzing and modeling binary outcomes such as default in payment, response to a marketing campaign, etc. By suggesting a set of alternative formulae, this paper attempts to expand their use to continuous outcomes such as sales volume, claim amount, etc. A SAS macro program is provided that will efficiently evaluate the predictive power of continuous, ordinal and categorical variables together and yield useful suggestions for variable reduction/selection and for segmentation. Modelers can use the insights conveyed by SAS outputs for subsequent linear regression, logistic regression or scorecard development | response variable, regression analysis, numeric variable, call symputx, proc step, nominal variable, create table, proc varreduce, ordinal variable, macro definition, sample, group by, predictor variable, sas output, logistic regression, proc sql, logistic distribution, outlier | 23 | |
| 35 | Exploring the Use of Negative Binomial Regression Modeling for Pediatric Peripheral Intravenous Catheterization | Jennifer Mann, Jason Brinkley, Pamela Larson |
A large study conducted at two southeastern US hospitals from October 2007 through October 2008 sought to identify predictive variables for successful intravenous catheter (IV) insertion, a crucial procedure that is potentially difficult and time consuming in young children. The data was collected on a sample of 592 children that received a total of 1,195 attempts to start peripheral IV catheters in the inpatient setting. The outcome here is number of attempts to successful IV placement, for which the underlying data appears to have a negative binomial structure. The goal of this paper is to illustrate the appropriateness of a negative binomial assumption using visuals obtained from PROC SGPLOT and to determine the goodness of fit for a negative binomial model. Negative binomial regression output from PROC GENMOD will be contrasted with traditional ordinary least squares output |
binomial distribution, regression analysis, negative binomial regression, confidence interval, negative binomial distribution, proc reg, numeric functions, proc freq, poisson distribution, proc step, sample | source | 7 |
| 36 | Flat Pack Data: Converting and ZIPping SAS Data for Delivery | Sarah Woodruff | Clients or collaborators often need SAS data converted to a different format. Delivery or even storage of individual data sets can become cumbersome, especially as the number of records or variables grows. The process of converting SAS data sets into other forms of data and saving files into compressed ZIP storage has become not only more efficient, but easier to integrate into new or existing programs. This paper describes and explores various methods to convert SAS data as well as effective strategies to ZIP data sets along with any other files that might need to accompany them. PROC IMPORT and PROC EXPORT have been long standing components of the SAS toolbox, so much so that they have their own wizards, but understanding their syntax is important to effectively use them in code being run in batch or to include them in programs that may be run interactively but "hands free". The syntax of each is described with a particular focus on moving between SAS, STATA and SPSS, though some attention is also given to EXCEL. Once data sets and their attendant files are ready for delivery or need to be put into storage, compressing them into ZIP files becomes helpful. The process of using ODS PACKAGE to create such ZIP files is laid out and can be connected programmatically to the creation of the data sets or documents in the first place. |
proc step, proc import, proc export, excel, dataset | source | 9 |
| 37 | FORMATs Top Ten | Christianna S. Williams | SAS FORMATs can be used in so many different ways! Even the most basic FORMAT use of modifying the way a SAS data value is displayed (without changing the underlying data value ) holds a variety of nifty tricks, such as nesting formats, formats that affect various style attributes (such as alignment, font etc.), and conditional formatting. Add in PICTURE formats, multi -label FORMATs, using FORMATs for data cleaning, and FORMATs for joins and table look-ups, and we have quite a bag of tricks for the humble SAS FORMAT and the PROC FORMAT used to generate them. The purpose of this paper is to describe a few handfuls of very useful programming techniques that employ SAS FORMATs. Wh ile this paper will be appropriate for the newest SAS user, it will also focus on some of the lesser-known features of FORMATs and PROC FORMAT and so should be useful for even quite experienced users of SAS |
proc step, proc format, proc tabulate, format statement, missing value, char., class variable, proc catalog, libname statement, ods rtf, user-defined format | source | 18 |
| 38 | Formatting Data with Metadata - Easy and Powerful | Leanne Tang | In our organization a lot of efforts are put into building and maintaining our organizational level metadata databases. Metadata databases are used to store information, or metadata, about our data. Many times we have to "decipher" the keys and codes associate with our data to enhance our data presentation. One of the options to interpret our data is to generate user defined formats from metadata using SAS PROC FORMAT. In this paper we are going to explore some of the powerful options available in SAS PROC FORMAT procedure. We will create user defined formats from metadata. We will look into different ways using the formats |
proc format, proc step, dataset, where statement | source | 5 |
| 39 | Google Search Tips and Techniques for SAS and JMP Users | Kirk Paul Lafler, Charles E. Shipp |
Google (www.google.com) is the world's most popular and widely-used search engine. As the premier search tool on the Internet today, SAS and JMP users frequently need to identify and locate SAS and JMP content wherever and in whatever form it resides. This paper provides insights into how Google works and illustrates numerous search tips and techniques for finding articles of interest, reference works, information tools, directories, PDFs, images, current news stories, user groups, and more to get search results quickly and easily | source | 6 | |
| 40 | Hands Free: Automating Variable Name Re-Naming Prior to Export | John J. Cohen | Often production datasets come to us with data in the form of rolling 52 weeks, 12 or 24 months, or the like. For ease of use, the variable names may be generic (something like VAR01, VAR02, etc., through VAR52 or VAR01 through VAR12), with the actual dates corresponding to each column being maintained in some other fashion - often in the variable labels, a dataset label, or some other construct. Not having to re-write your program each week or month to properly use these data is a huge benefit. Until, however, you may need to capture the date information to properly document - in the variable names (so far VAR01, VAR02, etc.) - prior to, say, exporting to MS/EXCEL (where the new column names may instead need to be JAN2011, FEB2011, etc.). If the task of creating the correct corresponding variable names/column names each week or month were a manual one, the toll on efficiency and accuracy could be substantial. As an alternative we will use an approach using a "program-to-write-a-program" to capture date information in the incoming SAS dataset (from two likely alternate sources) and have our program complete the rest of the task seamlessly, week-after-week (or month-after-month). By employing this approach we can continue to use incoming data with generic variable names. |
dataset, label statement, proc contents, excel, proc step | source | 4 |
| 41 | Hidden in plain sight: my top ten underpublicized enhancements in SAS Versions 9.2 and 9.3 | Bruce F. Gilsen | SAS Versions 9.2 and 9.3 contain many interesting enhancements. While the most significant enhancements have been widely publicized in online documentation, conference papers, the SAS-L internet newsgroup/listserv, and elsewhere, some smaller enhancements have received little attention. This paper reviews my ten favorite underpublicized features. Eliminate observations with a unique sort key (BY groups of size one)DATA STEP sortString delimiter on inputString delimiter on outputThe PRINT procedure: printing blank linesData set lists in the SET and MERGE statementsAppend SAS log filesSimpler macro variable rangeTrim leading and trailing blanks from a value stored by the SQL procedure in a single macro variableCreate a directory or folder in a LIBNAME statement |
macro variable, proc step, call sortn, proc sql, call sortc, libname statement, proc sort, sas log, infile statement, merge statement, by group processing, options statement, proc print | source | 12 |
| 42 | How to Make an Impressive Map of the United States with SAS/Graph for Beginners | Sharon Avrunin-Becker | Have you ever been given a map downloaded from the internet and asked to reproduce the same thing using SAS complete with labels and annotations? As you stare at all the previously written, brilliant SAS/GRAPH conference papers, you start feeling completely overwhelmed. The papers assume you already know how to get started and you feel like a clueless chimpanzee not understanding what you are missing. This paper will walk you through the steps to getting started with your map and how to add colors and annotations that will not only impress your manager, but most importantly yourself that you could do it too! |
proc gmap, proc step | source | 9 |
| 43 | I've JMPed in, but how do I get out? Exporting JMP discoveries for reports and publications | Barbara B. Okerson | JMP is an excellent tool for visualizing data, but the only way for others to know what has been discovered in the data is to share the results. When creating graphs and reports in JMP, output options are not always readily evident. To add to this confusion, web searches on outputting graphics from JMP predominantly direct the user to "copy and paste" solutions. While "copy and paste" can be useful for some applications, more control is needed, especially when the results are needed for publications and formal reports. This presentation looks at a variety of options for exporting JMP visualizations and reports. Examples used in this paper were created with JMP 11.1. The techniques represented are not platform -specific and can be adapted by beginning through advanced JMP users | dataset, sas output | 8 | |
| 44 | Identifying Superutilizers in the Medicaid Population - A Simple and Powerful technique | Aran Canes | The purpose of this study is to test whether we can predict high cost recipients in the Medicaid population one year in the future with a high degree of certainty using data from the recipients' claim histories and relatively simple predictive modeling methods. We use logistic regression as the modeling tool and then refine the initial approach by binning continuous predictors and testing whether by clustering or by calculating Spearman and Hoeffding correlation coefficients we can eliminate irrelevant variables. We are able to show that a significant number (over 2,000) of these recipients can be predicted. Also, the positive predictive value for these recipients is close to 90%. This high positive predictive value means that resources do not need to be expended on recipients who are not likely to be true super-utilizers | correlation, precision, predictor variable, response variable, proc step, logistic regression, logistic distribution, regression analysis, proc princomp | 7 | |
| 45 | Impact of Affordable Care Act on Pharmaceutical and Biotech Industry | Salil Parab | On March 23, 2010, President Obama signed The Patient Protection and Affordable Care Act (PPACA), commonly called the Affordable Care Act (ACA). The law has made historic changes to the health care system in terms of coverage, cost, and quality of care. This paper will discuss the impact the law will have on the pharmaceutical and biotech industry. ACA imposes several costs on pharmaceutical, biotech and related businesses. A fee will be imposed on each covered entity that manufactures or imports branded prescription drugs with sales of over $5 million to specified government programs. In addition to the fees on branded prescription drug sales, ACA imposes a 2.3% excise tax on sale of medical devices. The tax is levied on the manufacturer or importer before a medical device is sent to the wholesaler or hospital. Prior to ACA, prescription drug manufacturer had to pay a rebate under Medicaid coverage which was greater of 15.1% of average manufacturer price ("AMP") or the difference between AMP and best price of the drug. ACA increased the rebate percentage to 23.1%. It also modified the definition of AMP, calculation of additional rebate for price increase of line of extension drugs, and expanded rebate program to additional drug sales. Under ACA, manufacturers of drugs that wish to sell their drugs covered under Medicare Part D must participate in coverage gap discount. Along with additional costs on the industry, ACA will bring positive changes. ACA is anticipated to add 35 million uninsured citizens as new customers who will directly impact the industry's bottom line by $115 billion over the next 10 years or so. Under The Qualifying Therapeutic Discovery Project program as part of ACA, a tax credit will be given to companies that treat unmet medical needs or chronic diseases. This will significantly boost innovation, particularly for small to mid-size enterprises and benefit the overall industry. The Biologics Price Competition and Innovation Act (BPCIA), which is part of ACA includes guidelines for market approval of "biosimilar" products, patent provisions, data and market exclusivity, and incentives for innovation. | coverage gap, health insurance | source | 8 |
| 46 | Impact of Q-matrix Misspecification on Cognitive Attribute Estimation in the Crossed Random Effects LLTM Model with the SAS GLIMMIX Procedure | Yi-Hsin Chen | A simulation study is designed to explore the impact of Q-matrix misspecification on parameter estimation in the crossed random effects linear logistic test model using the SAS GLIMMIX procedure. In addition, the impact of the interactions of Q-matrix misspecificati on with other manipulated factors such as population distribution, sample size, and Q-matrix density on parameter estimation are investigated as well. Datasets are simulated based on the model of interest using the SAS/IML package. The population distribut ions include normal, negativ ely-skewed, and positively- skewed distributions. The sample sizes are 50, 250, and 500. The percentages of misspecification in the Q-matrix are 2.4%, 4.8%, and 9.6%. Three types of misspecification are over-, under-, and balanced-misspecifications. For each condition, 1000 replications are ge nerated. Cognitive attribute parameters are estimated by applying the SAS GLIMMIX procedure |
proc glimmix, sample size, sample, correlation, effect size, proc mixed, rmse, main effect, mse, likelihood function, logistic distribution, maximum likelihood estimation | 11 | |
| 47 | Integrating Data and Variables between SAS and R via PROC IML: Enable Powerful Functionalities for Coding in SAS | Feng Liu, Ruiwen Zhang |
Programming in R provides additional features and functions which augment SAS procedures for many statisticians or scientists in areas like bioinformatics, finance, education, etc. It is in high demand for SAS users to be able to call R within their SAS programs. Though existing papers show how to import R data set into SAS or vice versa, there is a lack of a comprehensive solution to transfer variables from anything other than a data set. In this paper, we present solutions to use PROC IML to interface with R software which enables transferring variables and data sets transparently between SAS and R. We also provide examples of calling R functions within IML modules which offers much flexibility for coding in SAS, especially for big projects involving intensive coding. This can also be used to pass parameters from SAS to R. In this paper, you will see a step-by-step demonstration of a SAS project which integrates calling R functions via PROC IML |
proc iml, proc step, dataset, macro variable | source | 4 |
| 48 | Integrating SAS with JMP to Build an Interactive Application | Marve Gurlu | This presentation will demonstrate how to bring various JMP visuals into one platform to build an appealing, informative, and interactive dashboard using JMP Application Builder and make the application more effective by adding data filters to analyze subgroups of your population with a simple click. Even though all the data visualizations are done in JMP, importing and merging large data files, data manipulations, creating new variables and all other data processing steps are performed by connecting JMP to SAS. This presentation will demo connecting to SAS to create a data file ready for visualization using SAS data manipulation capabilities and macros; building interactive visuals using JMP; and, building an application using JMP application builder. For attendees who would like to be able to print the visuals in the application, a few tips and tricks for building PowerPoint presentations will be provided at the end of the presentation. | dataset, sas log, bar chart, sas output, data visualization, sample | source | 16 |
| 49 | Interacting with SAS using Windows PowerShell ISE | Mayank Nautiyal | The most conventional method of using SAS on a Windows environment is via a GUI application. There are numerous SAS users who have a UNIX background and can definitely take advantage of the Windows PowerShell ISE to gain job eficiency. The Windows PowerShell Integrated Scripting Environment (ISE) is a host application for Windows PowerShell. One can run commands, write, test, and debug scripts in a single Windows-based graphic user interface with multiline editing. This paper will demonstrate how frequently used SAS procedures can be scripted and submitted at the PowerShell command prompt. Job scheduling and submission for batch processing will also be illustrated. | cpu time, proc step, sas log, sas output, batch processing, proc sql, proc contents, job scheduling, proc freq | source | 13 |
| 50 | Introduction to Interactive Drill Down Reports on the Web | Michael G. Sadof | Presenting data on the web can be an intimidating project for those who are not familiar with the technology. Luckily, SAS provides users with a method of presenting dynamic reports on a web server utilizing basic SAS syntax and even legacy reports. This w orkshop will walk you through several methods of utilizing PROC REPORT and will teach you some HTML code to present interactive reports on your web server. This technique requires you have several BI components running on your server but does not require a dvanced knowledge of SAS BI or HTML. In this workshop, we will start with the basics and develop a roadmap for producing dynamic reports with Stored Processes without using OLAP cubes or the OLAP Server. In the SAS BI environment, there are many ways of di splaying data on the web. In this workshop, we will describe several of those techniques and create a project that utilizes them. The workshop will start by defining the necessary BI environment |
proc report, title statement, proc step, sample, macro definition, macro variable, macro quoting function | 27 | |
| 51 | JMP, Excel, and SAS Connectivity | Charles E. Shipp, Kirk Paul Lafler |
Isn't Microsoft EXCEL the most used software in the world? Isn't JMP the best in the world for statistical graphics and data discovery? And isn't SAS software the gold standard for robust and reliable analysis? Combine these three with easy connectivity and you have everything you need. Depending on requirements, your (1) input, (2) discovery and analysis, and (3) final display and reporting can begin with any of the three and end with any of the three. We demonstrate the most likely paths that emphasize JMP capabilities. A lively discussion will continue in the hallway! |
excel, dataset | source | 14 |
| 52 | Let SAS Do the Coding for You! | Robert Williams | Many times, we need to create the same reports going to different groups based on the group's subset of queried data or we have to develop many repetitive SAS codes such as a series of IF THEN ELSE statements or a long list of different conditions in a WHERE statement. It is cumbersome and a chore to manually write and change these statements especially if the reporting requirements change frequently. This paper will suggest methods to streamline and eliminate the process of writing and copying/pasting your SAS code to be modified for each requirement change. Two techniques will be reviewed along with a listing of key words in a SAS dataset or an EXCEL file: Create code using the DATA _NULL_ and PUT statements to an external SAS code file to be executed with %INCLUDE statement.Create code using the DATA _NULL_ and CALL SYMPUT to write SAS codes to a macro variable. You will be amazed how useful this process is for hundreds of routine reports especially on a weekly or monthly basis. RoboCoding is not just limited to reports; this technique can be expanded to include other procedures and data steps. Let the RoboCoder do the repetitive SAS coding work for you! |
when, where statement, macro variable, dataset, put statement, substr function, data step, txt, call symput, sample, if-then-else, insurance claims, select, excel | source | 10 |
| 53 | Making Comparisons Fair: How LS-Means Unify the Analysis of Linear Models | Weijie Cai | How do you compare group responses when the data are unbalanced or when covariates come into play? Simple averages will not do, but LS-means are just the ticket. Central to postfitting analysis in SAS/STAT linear modeling procedures, LS-means generalize the simple average for unbalanced data and complicated models. They play a key role both in standard treatment comparisons and Type III tests and in newer techniques such as sliced interaction effects and diffograms. This paper reviews the definition of LS-means, focusing on their interpretation as predicted population marginal means, and it illustrates their broad range of use with numerous examples. |
sample, proc plm, proc step, least squares mean, logistic distribution | source | 22 |
| 54 | Manage Variable Lists for Improved Readability and Modifiability | David H. Abbott, Veterans Affairs |
Lists of variables occur frequently in statistical analysis code, for example, lists of explanatory variables, variables used as rows in demographics tables, and so forth. These lists may be long, say 10-30 or more variables and the same list, or a major portion of it, may occur in multiple places in the code. The lists are often replicated using cut and paste by the programmer during program composition. Readers of the code may find themselves doing repeated "stare and compare" to determine if the list in location A is really the same list as in location B or location C. Simply adding a variable to the list may require changing numerous lines of code since the list occurs in the code numerous times. If managed naively, variable lists can impair code readability and modifiability. The SAS macro facility provides the tools needed to eliminate repeated entry of lengthy variable lists. Related groups of variables can be assigned to macro variables and the macro variables concatenated as needed to generate the list of variables needed at different points in the code. Certain SAS macros can be used to programmatically alter the list, for example, remove specific variables from the list (not needed for a given regression) or change the delimiter character to comma (when the list is used with PROC SQL). The macro variable names can express the purpose of the groups of variables, e.g. ExplanVars, OutcomeVars, DemographicOnlyVars, etc.. Employing this approach makes data analysis code easier to read and modify. |
macro variable, variable list, predictor variable, macro facility, proc sql, macro definition | source | 4 |
| 55 | Maximizing Confidence and Coverage for a Nonparametric Upper Tolerance Limit on the Second Largest Order Statistic for a Fixed Number of Samples | Dennis J. Beal | A nonparametric upper tolerance limit (UT L) bounds a specified percentage of the population distribution with specified confidence. The confidence and coverage of a UTL based on the second to largest order statistic is evaluated for an infinitely large population. This relationship can be used to determine the number of samples prior to sampling to achieve a given confidence and coverage. However, often statisticians are given a data set and asked to calculate a UTL for the second largest order statistic for the number of samples provided. Since t he number of samples usually cannot be increased to increase confidence or coverage for the UTL, the maximum confidence and coverage for the given number of samples is desired. This paper derives the maximum confidence and coverage for the second largest o rder statistic for a fixed number of samples. This relationship is demonstrated both graphically and in tabular form | sample, sample size | source | 5 |
| 56 | Modeling Cognitive Processes of Learning with SAS Procedures | Isaac Y. Li, Yi-Hsin Chen, Chunhua Cao, Yan Wang |
Traditionally, the principal goal of educational assessments is to evaluate students' academic achievement or proficiency in comparison to their peers or against promulgated standards. Both classical test theory (CTT) and item response theory (IRT) modeling frameworks provide measurement in the form of a summative estimate of the outcome variable. In recent years, understanding and exploring the complex cognitive processes that contribute to the learning outcome received growing interest in the field of psychometrics, where various item response modeling approaches have been devised to explain the relationship between the outcome and its componential attributes. Such approaches include the linear logistic test model (LLTM), the crossed random-effects linear logistic test model (CRELLTM), and the multiple regression method (MR). This paper introduces these statistical models and illustrates their implementation with the GLM, NLMIXED, and GLIMMIX procedures over an empirical data set | regression analysis, effect size, proc mixed, proc glimmix, likelihood function, sample size, residual, estimate, sample, maximum likelihood estimation, factorial anova, fixed effects, anova, proc nlmixed, logistic distribution, main effect | source | 11 |
| 57 | More Hash: Some Unusual Uses of the SAS Hash Object | Haikuo Bian, David Maddox |
Since the introduction of the SAS Hash Object in SAS 9.0 and recent enhancements, the popularity of the methodology has been grown. The significant effects of the technique in conjunction with the large memory capacity of modern computing devices has brought new and exciting capabilities to the DATA STEP. The most often cited application of the SAS Hash Object is table lookup. This paper will highlight several unusual applications of the methodology including random sampling, "sledge-hammer matching", anagram searching, dynamic data splitting, matrix computation, and unconventional transposing. |
hash table, random sampling, sample, lookup, proc step, proc sql | source | 11 |
| 58 | Move over MERGE, SQL and SORT. There is a faster game in town! Hash Table | Karen J. Price | The purpose of this paper and presentation is to introduce the basics of what a hash table is and to illustrate practical applications of this powerful Base SAS DATA STEP construct. We will highlight features of the hash object and show examples of how these features can improve programmer productivity and system performance of table lookup and sort operations. We will show relatively simply code to perform typical "look-up" match-merge usage as well as a method of sorting data through hashing as an alternative to the SORT procedure. |
hash table, pdv | source | 4 |
| 59 | Moving Data and Results Between SAS and Microsoft Excel | Harry Droogendyk | Microsoft EXCEL spreadsheets are often the format of choice for our users, both when supplying data to our processes and as a preferred means for receiving processing results and data. SAS offers a number of ways to import EXCEL data quickly and efficiently. There are equally flexible methods to move data and results from SAS to EXCEL This paper will outline the MANY techniques available and identify useful tips for moving data and results between SAS and EXCEL efficiently and painlessly. |
excel, data step, libname engine, proc step, proc export, sas enterprise guide, xls, name range list, proc import, dataset, delimited file, macro variable, txt | source | 23 |
| 60 | Multilevel Models for Categorical Data Using SAS PROC GLIMMIX: The Basics | Mihaela Ene, Elizabeth A. Leighton, Genine L. Blue, Bethany A. Bell |
Multilevel models (MLMs) are frequently used in social and health sciences where data are typically hierarchical in nature. However, the commonly used hierarchical linear models (HLMs) are only appropriate when the outcome of interest is continuous; when dealing with categorical outcomes, a transformation and an appropriate error distribution for the response variable need to be incorporated into the model and therefore, hierarchical generalized linear models (HGLMs) need to be used. This paper provides an introduction to specifying hierarchical generalized linear models using PROC GLIMMIX, following the structure of the primer for hierarchical linear models previously presented by Bell, Ene, Smiley, and Schoeneberger (2013). A brief introduction into the field of multilevel modeling and HGLMs with both dichotomous and polytomous outcomes is followed by a discussion of the model building process and appropriate ways to assess the fit of these models. Next, the paper provides a discussion of PROC GLIMMIX statements and options as well as concrete examples of how PROC GLIMMIX can be used to estimate (a) two-level organizational models with dichotomous outcomes and (b) two-level organizational models with polytomous outcomes. These examples use data from High School and Beyond (HS&B), a nationally-representative longitudinal study of American youth. For each example, narrative explanations accompany annotated examples of the GLIMMIX code and corresponding output. |
proc glimmix, proc step, likelihood function, fixed effects, covariance, random intercept, proc mixed, estimate, predictor variable, odds ratio, proc genmod, sample, covariance structure, multinomial distribution, response variable, random slope, correlation, maximum likelihood estimation | source | 12 |
| 61 | Overview of Analysis of Covariance (ANCOVA) Using GLM in SAS | Abbas S. Tavakoli | Analysis of covariance (ANCOVA) is a more sophisticated method of analysis of variance. Analysis of covariance is used to compare response means among two or more groups (Categorical variables) adjusted for a quantitative variable (Covariate), thought to influence the outcome (Dependent). A covariate is a continuous variable that can be used to reduce the Sum Square Error (SSE) and subsequently increase the statistical power of an ANOVA design. There may be more than one covariate. The purpose of this paper is to overview of Analysis of Covariance (ANCOVA) using GLM with two examples in SAS with interpretation to use for publication. | ancova, response variable, covariance, anova, correlation, regression analysis, interaction effect, interval variable, power analysis, predictor variable, proc genmod, proc glm | source | 10 |
| 62 | Penalizing Your Models: An Overview of the Generalized Regression Platform | Michael Crotty, Clay Barker |
We will provide an overview of the Generalized Regression personality of the Fit Model platform, added in JMP Pro version 11. The motivation for using penalized regression will be discussed, and multiple examples will show how the platform can be used for variable selection on continuous or count data. | regression analysis, maximum likelihood estimation, likelihood function, feature selection, elastic net, proc reg, binomial distribution | source | 8 |
| 63 | Point-and-Click Programming Using SAS Enterprise Guide | Kirk Paul Lafler, Mira Shapiro |
SAS Enterprise Guide empowers organizations exploiting the power of SAS by offering programmers, business analysts, statisticians and end-users with powerful built-in wizards to perform a multitude of reporting and analytical tasks, access multi- platform enterprise data sources, deliver data and results to a variety of mediums and outlets, perform important data manipulations without the need to learn complex coding constructs, and support data management and documentation requirements quickly and easily. Attendees learn how to use the graphical user interface (GUI) to access tab-delimited and EXCEL input files; subset, group, and summarize data; join two or more tables together; flexibly export results to HTML, PDF and EXCEL; and visually manage projects using flowcharts and diagrams |
excel, sas enterprise guide, dataset, proc means, data governance, delimited file, sas output, proc sql | source | 18 |
| 64 | PROC MEANS for Disaggregating Statistics in SAS: One Input Data Set and One Output Data Set with Everything You Need | Imelda C. Go, Abbas S. Tavakoli |
The need to calculate statistics for various groups or classifications is ever present. Calculating such statistics may involve different strategies with some being less efficient than others. A common approach by new SAS programmers who are not familiar with PROC MEANS is to create a SAS data set for each group of interest and to execute PROC MEANS for each group. This strategy can be resource-intensive when large data sets are involved. It requires multiple PROC MEANS statements due to multiple input data sets and involves multiple output data sets (one per group of interest). In lieu of this, an economy of programming code can be achieved using a simple coding strategy in the DATA STEP to take advantage of PROC MEANS capabilities. Variables that indicate group membership (1 for group membership, blank for non-group membership) can be created for each group of interest in a master data set. The master data set with these blank/1 indicator variables can then be processed with PROC MEANS and its different statements (i.e., CLASS and TYPES) to produce one data set with all the statistics generated for each group of interest. |
class variable, proc step, missing value, master data management | source | 8 |
| 65 | PROC RANK, PROC SUMMARY and PROC FORMAT Team Up and a Legend is Born! | Christianna S. Williams | The task was to produce a figure legend that gave the quintile ranges of a continuous measure corresponding to each color on a five-color choropleth map. Actually, figure legends for several dozen maps for several dozen different continuous measures and time periods…so, the process needed to be automated. A method was devised using PROC RANK to generate the quintiles, PROC SUMMARY to get the data value ranges within each quintile, and PROC FORMAT (with the CNTLIN= option) to generate and store the legend labels. And then, of course, these were rolled into a few macros to apply the method for the many different figure legends. Each part of the method is quite simple - even mundane - but together these techniques allowed us to standardize and automate an otherwise very tedious process. The same basic strategy could be used whenever one needs to dynamically generate data "buckets" but then keep track of the bucket boundaries - whether for producing labels or legends or so that future data can be benchmarked against the stored categories. |
proc step, proc format, proc rank, proc means, proc catalog | source | 10 |
| 66 | PROC SQL for PROC SUMMARY Stalwarts | Christianna S. Williams | One of the endlessly fascinating features of SAS is that the software often provides multiple ways to accomplish the same task. A perfect example of this is the aggregation and summarization of data across multiple rows "BY groups" of interest.These groupings can be study participants, time periods, geographical areas, or really just about any type of discrete classification that one desires. While many SAS programmers may be accustomed to accomplishing these aggregation tasks with PROC SUMMARY (or equivalently, PROC MEANS), PROC SQL can also do a bang-up job of aggregation - often with less code and fewer steps. The purpose of this step-by-step paper is to explain how to use PROC SQL for a variety of summarization and aggregation tasks, and will use a series of concrete, task-oriented examples to do so. For each example, both the PROC SUMMARY method and the PROC SQL method will be presented, along with discussion of pros and cons of each approach. Thus, the reader familiar with either technique can learn a new strategy that may have benefits in certain circumstances. The presentation style will be similar to that used in the author's previous paper, "PROC SQL for DATA STEP Die-Hards". |
proc step, proc means, proc sql, group by, missing value, class variable, create table, order by, proc sort, sysdate9, sas programmer, by group processing, raw data, proc freq | source | 18 |
| 67 | PROC TRANSPOSE For Fun And Profit | John J. Cohen | Occasionally we are called upon to transform data from one format into a "flipped," sort of mirror image. Namely if the data were organized in rows and columns, we need to transpose these same data to be arranged instead in columns and rows. A perfectly reasonable view of incoming lab data, ATM transactions, or web "click" streams may look "wrong" to us. Alternatively extracts from external databases and production systems may need massaging prior to proceeding in SAS. Finally, certain SAS procedures may require a precise data structure, there may be particular requirements for data visualization and graphing (such as date or time being organized horizontally/along the row rather than values in a date/time variable), or the end user/customer may have specific deliverable requirements. Traditionalists prefer using the DATA STEP and combinations of Array, Retain, and Output statements. This approach works well but for simple applications may require more effort than is necessary. For folks who intend to do much of the project work in, say, MS/EXCEL, the resident transpose option when pasting data is a handy short cut. However, if we want a simple, reliable method in SAS which once understood will require little on-going validation with each new run, then PROC TRANSPOSE is a worthy candidate. We will step through a series of examples, elucidating some of the internal logic of this procedure and its options. We will also touch on some of the issues which cause folks to shy away and rely on other approaches. |
proc transpose, proc step, excel, out=, proc sort, dataset, date variable, macro variable, by group processing, keep statement, set statement, missing value, proc sql | source | 18 |
| 68 | SAS Debugging 101 | Kirk Paul Lafler | SAS users are almost always surprised to discover their programs contain bugs. In fact, when asked users will emphatically stand by their programs and logic by saying they are bug free. But, the vast number of experiences along with the realities of writing code says otherwise. Bugs in software can appear anywhere; whether accidentally built into the software by developers, or introduced by programmers when writing code. No matter where the origins of bugs occur, the one thing that all SAS users know is that debugging SAS program errors and warnings can be a daunting, and humbling, task. This presentation explores the world of SAS bugs, providing essential information about the types of bugs, how bugs are created, the symptoms of bugs, and how to locate bugs. Attendees learn how to apply effective techniques to better understand, identify, and repair bugs and enable program code to work as intended. | options statement, sas log, data cleaning, hash table, proc step | source | 13 |
| 69 | SAS Enterprise Guide for Institutional Research and Other Data Scientists | Claudia W. McCann | Data requests can range from on-the-fly, need it yesterday, to extended projects taking several weeks or months to complete. Often institutional researchers and other data scientists are juggling several of these analytic needs on a daily basis, i.e., taking a break from the longitudinal report on retention and graduation to work on responding to a USN&WR survey to answering the simple 5 minute data query question from an administrator. SAS Enterprise Guide is a terrific tool for handling multiple projects simultaneously. This interactive tutorial is designed to walk the data analyst through the process of setting up a project, accessing data from several sources, merging the datasets, and running the analyses to generate the data needed for the particular project | sas enterprise guide, data science, excel, calendar year, dataset, proc freq, sas enterprise guide project, proc step | source | 20 |
| 70 | SAS Macro Magic is not Cheesy Magic! Let the SAS Macros Do the Magic of Rewriting Your SAS Code | Robert Williams | Many times, we need to rewrite weekly or monthly SAS programs to change certain key statements such as the conditions inside the WHERE statements, import/export file paths, reporting dates and SAS data set names. If we hard coded these statements, it is cumbersome and a chore to read through the SAS code to rewrite these hard-coded statements. Sometimes, we might miss an important statement that needs to be re-coded resulting in inaccurate data extract and reports. This paper will show how the SAS Magic Macros can streamline and eliminate the process rewriting the SAS codes. Two types of SAS Macros will be reviewed with examples: Defining SAS Macro variables with values using %LET to be rewritten in the SAS statement using & sign.Creating SAS Macro programming using %MACRO and %MEND to write a series of SAS statements to be rewritten in the SAS code. You will be amazed how useful the SAS Magic Macro is for many of your routine weekly and monthly reports. Let the SAS Magic Macro relieve you of the tedious task of rewriting many of the SAS statements! |
macro definition, macro variable, where statement, macro parameter, %macro parameters, sas log, data step | source | 7 |
| 71 | SAS Metadata Querying and Reporting Made Easy: Using SAS Autocall Macros | Jiangtang Hu | Metadata is the core of the modern SAS system (aka, SAS Business Analysis Platform) and SAS offers various techniques to a ccess it via SAS DATA STEP functions, procedures, LIBNAME engines and Java interface. Furthermore, SAS also provides bunch of autocall macros which are well packaged for metadata querying and reporting using techniques above , and might be not well known fo r some SAS programmers . In this paper, I will go through such metadata autocall macros to get quick results against SAS metadata like users, libraries, and most important, permissions. For best display of SAS metedata, SAS ODS Reporting Writing Interface t echnique is also used for this demo (again, it's not new and it's in SAS system folder of sample codes which are omitted by most SAS programmers). All demo codes can be found in Github, https://github.com/ Jiangtang/SESUG |
autocall macro, sas enterprise guide, data step, sample | source | 9 |
| 72 | SAS282 - Useful Tips for Building Your Own SAS Cloud | Peter Villiers | Everyone has heard about SAS Cloud. Now come learn how you can build and manage your own cloud using the same SAS virtual application (vApp) technology. | 23 | ||
| 73 | SDTM What? ADaM Who? A Programmer's Introduction to CDISC | Venita DePuy | Most programmers in the pharmaceutical industry have at least heard of CDISC, but may not be familiar with the overall data structure, naming conventions, and variable requirements for SDTM and ADaM datasets. This overview will provide a general introduction to CDISC from a programing standpoint, including the creation of the standard SDTM domains and supplemental datasets, and subsequent creation of ADaM datasets. Time permitting, we will also discuss when it might be preferable to do a "CDISC-like" dataset instead of a dataset that fully conforms to CDISC standards. | adam dataset, sdtm domain, controlled terminology, adverse events, study day, change from baseline, treatment arm, treatment period, vital signs, imputation, concomitant medications, flag variable, crossover study, medical history, data science, sample, randomization, cdisc | source | 9 |
| 74 | Searching for (and Finding) a Needle in a Haystack: a BASE SAS Exploratory Search Tool to Facilitate Text Mining | Troy Martin Hughes | Text mining can add analytic and business value by uncovering hidden truths and trends in unstructured, semi- structured, and structured text data. While SAS Text Miner presents a comprehensive solution to text mining and content analysis, simpler, targeted questions warrant a more straightforward solution. When triaging a new data set of unknown content or structure, or to the new hire unfamiliar with a customer's complex data repository, answering questions about text data- or even knowing where to begin your search- may represent a formidable challenge. Initial analytic questions may include the need to determine if a word or phrase appears within a database and, if so, in which libraries, data sets, and fields and with what frequency. This text describes a Base SAS tool that iteratively searches SAS libraries and data sets for a single word, phrase, or a list of words or phrases | sas library, proc freq, sas base | source | 8 |
| 75 | Secrets from a SAS Technical Support Guy: Combining the Power of the SAS Output Delivery System with Microsoft Excel Worksheets | Chevell T. Parker | Business analysts commonly use Microsoft EXCEL with the SAS System to answer difficult business questions. While you can use these applications independently of each other to obtain the information you need, you can also combine the power of those applications, using the SAS Output Delivery System (ODS) TAGSETS, to completely automate the process. This combination delivers a more efficient process that enables you to create fully functional and highly customized EXCEL worksheets within SAS. This paper starts by discussing common questions and problems that SAS Technical Support receives from users when they try to generate EXCEL worksheets. The discussion continues with methods for automating EXCEL worksheets using ODS TAGSETS and customizing your worksheets using the CSS style engine and extended TAGSETS. In addition, the paper discusses tips and techniques for moving from the current MSOffice2K and ExcelXP TAGSETS to the new EXCEL destination, which generates output in the native EXCEL 2010 forma. |
excel, sample, proc step, proc template, output delivery system, proc sql, ods tagsets.excelxp, sas output, ods excel, macro definition, macro quoting function | source | 22 |
| 76 | Secure SAS OLAP Cubes with Top-Secret Permissions | Stephen M. Overton | SAS OLAP technology is used to organize and present summarized data for business intelligence applications. It features flexible options for creating and storing aggregations to improve performance and brings a powerful multi- dimensional approach to querying data. This paper focuses on managing security features available to OLAP cubes through the combination of SAS metadata and MDX logic | raw data, sas management console | source | 11 |
| 77 | SESUG2014 Annotation utilizations in customized SAS/Graph bar charts | Yong Liu, Hua Lu, Liang Wei, Xingyou Zhang, Paul I. Eke, James B. Holt |
Bar graphs are generated by using SAS/GCHART to present the distribution of health behaviors or health outcomes among adults aged ≥18 years by selected characteristics and each of 50 states using 2011 Behavioral Risk Factor Surveillance Survey (BRFSS). Due to missing data or unreliable estimates of parameters, annotation facilities are utilized to make the charts more presentable by adding data labels and footnotes. Further, incorporating a SAS Macro variable into the program can definitely make the development of 50 charts for 50 states more achievable and efficient. |
bar chart, macro variable | source | 7 |
| 78 | Simple Rules to Remember When Working with Indexes | Simple Rules | SAS users are always interested in learning techniques related to improving data access. One way of improving information retrieval from a SAS data set or table is to create and use an index. An index consists of one or more columns that are used to uniquely identify each row within a table. Functioning as a SAS object containing the values in one or more columns in a table, an index may be defined as numeric, character, or a combination of both. This presentation will emphasize the rules that users should know when creating and using indexes to make information retrieval more efficient | proc sql, where statement, proc step, index, sas library, dataset | source | 5 |
| 79 | Store and Recall Macros with SAS Macro Libraries | John M. Myers | When you store your macros in a SAS macro library, you can recall the macros with your SAS programs and share them with other SAS programmers. SAS macro libraries help you by reducing the time it takes to develop new programs by using code that has been previously tested and verified. Macro libraries help you to organize your work by saving sections of code that you can reuse in other programs. Macro libraries improve your macro writing skills by focusing on a specific task for each macro. Macro libraries are not complicated - they are just a way to store macros in a central location. This presentation will give examples of how you can build macro libraries using %INCLUDE files, AUTOCALL library, and STORED COMPILED MACRO library. |
stored macro, macro definition, autocall macro, filename statement, libname statement, catalog, options statement, txt, proc catalog | source | 11 |
| 80 | Stretching Data Training Methods: A Case Study in Expanding SDTM Skills | Richard Addy | With CDISC moving towards becoming an explicit standard for new drug submissions, it is important to expand the number of people who can implement those standards efficiently and proficiently. However, the CDISC models are complex, and increasing expertise with them across a large group of people is a non-trivial task. This paper describes a case study focusing on increasing the number of people responsible for creating the SDTM portion of the submissions package (data set specifications, annotated CRF, metadata, and define file). In moving these tasks from a small dedicated group who handled all submission-related activities to a larger pool of programmers, we encountered several challenges: ensuring quality and compliance across studies; developing necessary skills (often, non-programmatic skills); and managing a steep learning curve (even for programmers with previous SDTM experience). We developed several strategies to address these concerns, including developing training focused on familiarizing new folks with where they need to go to look for details , a mentor system to help prevent people from getting stuck, focusing extra attention on domains that consistently caused problems, and creating flexible and robust internal tools to assist in the creation of the submission. | crf, statistical programmer, cdisc | source | 4 |
| 81 | Strimmed_t: A SAS Macro for the Symmetric Trimmed t Test | Diep T. Nguyen, Anh P. Kellermann, Eun Sook Kim |
The independent means t-test is commonly used for testing the equality of two population means. However, this test is very sensitive to violations of the population normal ity and homogeneity of variance assumptions. In such situations, Yuen's (1974) trimmed t-test is recommended as a robust alternative. The purpose of this paper is to provide a SAS macro that allows easy computation of Yuen's symmetric trimmed t-test. The macro output includes a table with trimmed means for each of two groups , Winsorized variance estimates, degrees of freedom, and obtained value of t (with two-tailed p-value). In addition, the results of a simulation study are presented and provide empirical comparisons of the Type I error rates and statistical power of the independent samples t-test, Satterthwaite's approximate t-test and the trimmed t-test when the assumptions of normality and homogeneity of variance are violated | type i error, sample, type i error control, significance level, sample size, trimming, degrees of freedom, proc calis, power analysis, f-test, macro definition | source | 12 |
| 82 | Taking Advantage of SAS Stored Processes to Share SAS Results in a SAS Office Analytics Environment | Taking Advantage | Once a SAS Office Analytics deployment is complete, traditional SAS coders often have the following questions about the benefits of changing the way they work. They wonder what the value is in moving from traditional SAS coding to leveraging SAS Enterprise Guide. This paper intends to demonstrate the value in using SAS Enterprise Guide to create SAS Stored Processes with prompts and how to make them available to information consumers via SAS Add-in for Microsoft Office and the SAS Stored Process Web Application | sas enterprise guide, sas management console | 7 | |
| 83 | Teaching SAS Using SAS OnDemand Web Editor and Enterprise Guide | Charlotte Baker, C. Perry Brown |
The server based SAS OnDemand offerings are excellent tools for teaching SAS coding to graduate students. SAS OnDemand Enterprise Guide and SAS OnDemand Web Editor can be used to accomplish similar educational objectives but the resources required to use each program can be different. This paper will discuss why one might use a SAS OnDemand program for education and the pros and cons of using each program for instruction. | sas enterprise guide, libname statement, proc step | source | 6 |
| 84 | Techniques in Processing Data on Hadoop | Douglas B. Liming | Before you can analyze your big data, you need to prepare the data for analysis. This paper discusses capabilities and techniques for using the power of SAS to prepare big data for analytics. It focuses on how a SAS user can write code that will run in a Hadoop cluster and take advantage of the massive parallel processing power of Hadoop | proc step, data mining, big data, proc sql, macro variable | source | 11 |
| 85 | Text Analytics using High Performance SAS Text Miner | Edward R. Jones | The latest release of SAS Enterprise Miner , version 13.1, contains high performance modules, including new modules for text mining. This paper compares the new High Performance Text Mining modules to those found in SAS Text Miner. The advantages and disadvantages of HP Text Miner are discussed. This is illustrated using data from the National Highway and Transportation Safety Administration for the GMC recall of 258,000 SUVs for a potential fire hazard. | neural network, data mining, logistic regression, proc hpsplit, logistic distribution, regression analysis | source | 7 |
| 86 | The DoW-Loop | Paul M. Dorfman, Lessia S. Shajenko |
The DoW-loop is a nested, repetitive DATA STEP structure enabling you to isolate instructions related to a certain break event before, after, and during a DO-loop cycle in a naturally logical manner. Readily recognizable in its most ubiquitous form by the DO UNTIL(LAST.ID) construct, which readily lends itself to control-break processing of BY-group data, the DoW-loop's nature is more morphologically diverse and generic. In this workshop, the DoW-loop's logic is examined via the power of example to reveal its aesthetic beauty and pragmatic utility. In some industries like Pharma, where flagging BY-group observations based on in-group conditions is standard fare, the DoW-loop is an ideal vehicle greatly simplifying the alignment of business logic and SAS code. In this Hands On Workshop, the attendees will have an opportunity to investigate the program control of the DoW-loop step by step using the SAS DATA STEP debugger and learn of a range of nifty practical applications of the DoW-loop. |
set statement, hash table, missing value, output, by group processing, dataset, automatic variable, proc sql | source | 12 |
| 87 | The Nuances of Combining Hospital Data | Jontae Sanders, Charlotte Baker, C. Perry Brown |
Hospital discharge data can be used for the surveillance of various health conditions in a population. To maximize our ability to tell the story of a population's health, it is often necessary to combine multiple years of data. This step can be tedious as there are many factors to take into account such as changes in variable names or data formats between years. Once you have resolved these issues, the data can be successfully combined for analysis. This paper will demonstrate many factors to look for and how to handle them when combining hospital data across years | character variable, numeric variable, proc contents, put statement, proc step, rename statement, set statement | source | 5 |
| 88 | The Power of PROC APPEND | Ted Logothetti | PROC APPEND is the fastest way to concatenate SAS data sets. This paper discusses some of the features of this procedure, including how much it lessens processing time, some tips and tricks, and a correction to the online SAS documentation. It also lists some limitations of the procedure. |
proc append, proc step, raw data, set statement, cpu time, error message, where statement | source | 7 |
| 89 | The Power of SAS Macro Programming - One Example | Milorad Stojanovic | When we are using SAS macro programming, macro tools and features can make our life more difficult at the beginning and a lot easier at the end. We should envision the work of macros in different combinations of input data and relationships between variables. Also macro code should prevent the processing data if 'critical' files are missing. In this paper we present examples of creating macro variables, using one or more ampers (&), creating dynamic SAS code, using %Sysfunc, %IF, %THEN, %ELSE and delivering flexible reports. Code is data driven by using macro programming tools. | macro definition, macro variable, macro facility, datetime variable, macro function, %if-%then-%else, proc step, macro loop | source | 7 |
| 90 | This is the Modern World: Simple, Overlooked SAS Enhancements | Bruce F. Gilsen | At my job as a SAS consultant at the Federal Reserve Board, reading the SAS-L internet newsgroup, and at SAS conferences, I've noticed that some smaller, less dramatic SAS enhancements seem to have fallen through the cracks. Users continue to use older, more cumbersome methods when simpler solutions are available. Some of these enhancements were introduced in Version 9.2, but others were introduced in Version 9, Version 8, or even Version 6! This paper reviews underutilized enhancements that allow you to more easily do the following. Write date values in the form yyyymmddIncrement date values with the INTNX functionCreate transport files: PROC CPORT/CIMPORT versus PROC COPY with the XPORT engineCount the number of times a character or substring occurs in a character string or the number of words in a character stringConcatenate character stringsCheck if any of a list of variables contains a valueSort by the numeric portion of character valuesRetrieve DB2 data on z/OS mainframes |
proc step, date variable, sysdate9, character variable, libname statement, dataset, proc copy, proc cport, conditional do loop, relational database, proc sql, index | source | 15 |
| 91 | Time Series Mapping with SAS: Visualizing Geographic Change over Time in the Health Insurance Industry | Barbara B. Okerson | Changes in health insurance and other industries often have a spatial component. Maps can be used to convey this type of information to the user more quickly than tabular reports and other non-graphical formats. SAS provides programmers and analysts with the tools to not only create professional and colorful maps, but also the ability to display spatial data on these maps in a meaningful manner that aids in the understanding of the changes that have transpired. This paper illustrates the creation of a number of different maps for displaying change over time with examples from the health insurance arena. | health insurance, proc gmap, proc step, dataset, time series analysis | source | 8 |
| 92 | To foam or not to foam: A survival analysis of the foam head that forms when a soda is poured | Kate Phillips | The goal of this study is to determine which factors influence the dissolve time of the foam head that forms after a soda is poured. This study proposes a hierarchical logistic model in order to estimate a particular soda's probability of being a "small fizzer" (the desired outcome) as opposed to a "big fizzer," with the median dissolve time of 12 seconds serving as the cut point for the binary outcome. A STANDARD procedure for testing foam head dissolve time was developed in order to collect the study data. A sample of 80 Coke products was then tested during fall 2013; characteristics of each product sampled were also recorded. All analyses were then conducted using Base SAS 9.3. After conducting a univariate analysis for each factor of interest, the continuous response variable was then dichotomized into the binary outcome of interest. A bivariate analysis was then conducted; odds ratios with their confidence intervals were examined in order to determine a predictor's significance with respect to the binary outcome. Table row percentages were examined for factors where odds ratios were not given by SAS. It was discovered that the most significant factors were sweetener type and a previously undiscovered (to the author's best knowledge) interaction between test container material and the presence/absence of caffeine ("test container material" refers to the material that the beverage was poured into for testing). According to the study results, this interaction was the most influential factor with respect to foam head dissolve time. The odds ratio for sweetener type was 2.25 (95% CI: 0.91, 5.54). With caffeine present, the odds ratio for test container material was 0.76 (95% CI: 0.23, 2.53). With caffeine absent, the odds ratio for test container material jumped to 11.70 (95% CI: 1.85, 74.19). The final hierarchical logistic model retains the factors "sweetener type," "test container material," and the interaction between test container material and the presence/absence of caffeine. |
bivariate analysis, odds ratio, proc step, sample, sample size, proc power, proc freq, bar chart, univariate analysis, logistic distribution, response variable, anova, confidence interval, proc logistic, logistic regression, histogram | source | 16 |
| 93 | Tools for Managing and Measuring the Value of Big Data Projects | Robert W. Phelps | Big Data and analytics focused projects have undetermined scope and changing requirements at their core. There is high risk of loss of business value if the project is managed with an IT centric waterfall approach and classical project management methods. Simply deploying technology on time, to plan, and within budget does not produce business value for Big Data projects. A different approach in managing projects and stakeholders are required to execute and deliver business value for Big Data and analytics focused initiatives | data science, business intelligence | source | 6 |
| 94 | Top Ten SAS Performance Tuning Techniques | Top Ten | The Base-SAS software provides users with many choices for accessing, manipulating, analyzing, and processing data and results. Partly due to the power offered by the SAS software and the size of data sources, many application developers and end- users are in need of guidelines for more efficient use. This presentation highlights my personal top ten list of performance tuning techniques for SAS users to apply in their applications. Attendees learn DATA and PROC STEP language statements and options that can help conserve CPU, I/O, data storage, and memory resources while accomplishing tasks involving processing, sorting, grouping, joining (merging), and summarizing data |
dataset, proc sql, cpu time, options statement, proc step, where statement, performance tuning | source | 7 |
| 95 | Trash to Treasures: Salvaging Variables of Extremely Low Coverage for Modeling | Alec Zhixiao Lin | Variables with an extremely low occurrences either exhibit very low Information Values in scorecard development or fail to be selected by a regression model, and hence are usually discarded at the stage of data cleaning. However, some of these variables could contain valuable information and are worth retaining. We can aggregate different rare occurrences into a single predictor which can be used in a subsequent regression or analysis. This paper introduces a SAS macro that tries to discover and salvage these variables in hope of turning them into potentially useful predictors. | binary variable, regression analysis | source | 5 |
| 96 | Tricks and Tips for Using the Bootstrap in JMP Pro 11 | Jason Brinkley | The bootstrap has become a very popular technique for assessing the variability of many different unusual estimators. Starting in JMP Pro 10 the bootstrap feature was added to a wide variety of output options, however there has not been much development as to the possible uses of this somewhat hidden feature. This paper will discuss a handful of uses that can be added to routine analyses. Examples include confidence interval estimates of the 5% trimmed mean and median survival, validation of covariates in regression analysis, comparing the differences in Spearman correlation estimates across two groups, and eigenvalues in principal components analysis. The examples will show the extra depth that can be easily added to routine analyses. | bootstrap, sample, confidence interval, correlation, spearman correlation, regression analysis, trimming, proc princomp | source | 13 |
| 97 | Uncover the Most Common SAS Stored Process Errors | Patricia S. Aanderud | You don't have to be with the CIA to discover why your SAS stored process is producing clandestine results. In this talk, you will learn how to use prompts to get the results you want, work with the metadata to ensure correct results, and even pick up simple coding tricks to improve performance. You will walk away with a new decoder ring that allows you to discover the secrets of the SAS logs! | sas log, error message | 6 | |
| 98 | Using Regression Model to Predict Cyclic Resistance Ratio at South Carolina Coastal Plain (SCCP) | Emad Gheibi, Sarah L. Gassman, Abbas S. Tavakoli |
Seismically-induced liquefaction is one of the most hazardous geotechnical phenomena from earthquakes that can cause loss of life and devastating damages to infrastructures. In 1964, a 7.5 Richter magnitude earthquake in Nigata, Japan destroyed nu merous buildings and structures and initiated studies to understand soil liquefaction. One major outcome of these studies was the development of correlations that are used to determine liquefaction resistance of soil deposits from in-situ soil indices. These relations are based on Holocene soils (<10,000 years old) while the sand deposits encountered in the South Carolina Coastal Plain (SCCP) are older than 100,000 years | correlation, standard deviation, pearson correlation, sample, proc step, regression analysis, data science | source | 10 |
| 99 | Using SAS software to shrink the Data used in Apache Flex Application | Ahmed Al-Attar | This paper discusses the techniques I used at the Census Bureau to overcome the issue of dealing with large amount of data while modernizing some of their public facing web applications by using Service Oriented Architecture (SOA) to deploy SAS powered Flex web applications. Techniques that resulted in reducing 142,293 XML lines (3.6 MB) down to 15,813 XML lines (1.8 MB) a 50% size reduction on the server side (HTTP Response), and 196,167 observations down to 283 observations, a reduction of 99.8% in summarized data on the client side (XML Lookup file). | proc step, dataset | source | 8 |
| 100 | Using SAS to Analyze the Impact of the Affordable Care Act | John J. Cohen | The Affordable Care Act being implemented in 2014 is expected to fundamentally reshape the health care industry. All current participants--providers, subscribers, and payers--will operate differently under a new set of key performance indicators (KPIs). This paper uses public data and SAS software to illustrate an approach to creating a baseline for the health care industry today so that structural changes can be measured in the future to assess the impact of the new law. | sample, data visualization, dataset, proc freq, proc step | source | 12 |
| 101 | Using SAS to Create a p-value Resampling Distribution for a Statistical Test | Peter S. Wludyka | One starts with data to perform a statistical test of a hypothesis. A p-value is associated with a particular test and this p-value can be used to decide whether to reject a null hypothesis in favor of some alternative. Since the data (the sample) is usually all a researcher knows factually regarding the phenomenon under study, one can imagine that by sampling (resampling) with replacement from that original data that additional information about the hypothesis and phenomenon/study can be acquired. One way to acquire such information is to repeatedly resample for the original data set (using, for example, PROC SURVEYSELECT) and at each iteration (replication of the data set) perform the statistical test of interest and calculate the corresponding p-value. At the end of this stage one has r p-values (r is typically greater than 1,000), one for each performance of the statistical test. Thinking of the original p-value as a quantile of this distribution of p-values allows one to assess the likelihood that the original hypothesis would have been rejected, which helps put the actual decision in perspective. The resampling distribution of p-values also allows one to retrospectively assess the power of the test by finding the proportion of the p-values that are less than a specified level of significance (alpha). By creating a p-value resampling distribution for a selection of sample sizes one can create a power curve which can be used prospectively to gather sample size information for follow up studies. |
sample, bootstrap, sample size, proc step, null hypothesis, macro definition, confidence interval, standard deviation | source | 12 |
| 102 | Using SAS to Examine Mediator, Direct and Indirect Effects of Isolation and Fear on Social Support Using Baron& Kenny Combined with Bootstrapping Methods | Abbas S. Tavakoli, Sue P. Heiney |
This study presentation examines mediator, direct and indirect effects of isolation and fear on social support by using two methods: Baron & Kenny, and Bootstrapping. This paper used a cross-sectional data from the longitudinal study randomized trial design in which 185 participants were assigned to the therapeutic group (n=93) who received by teleconference with participants interacting in real time with each other and control group (n=92) who received usual psychosocial care (any support used by the patient in the course of cancer treatment. Baron and Kenny (1986) steps and Hayes (2004) were used to examine for direct and indirect effects. Results of Baron indicated that the relationship between fear and social support was significant (c =-1.151 (total effect) (p=.0001)) and that there was significant relationship between isolation and fear (α =1.22 (p=.0001)). Also, previously significant relationship between fear and social support was not significant (c' =-.40 (direct effect) (p=.1876) when both fear and isolation were in the model. The indirect effect was -1.11 and Sobel test was significant (P=.0001). The results of bootstrapping methods indicated the direct effect wares -.41 (95% CI: -.42, -.40 for normal theory and -.41 (95% CI: -.99, .14 for percentile) and indirect effect was -1.06 (95% CI: -1.09, -1.08 for normal theory and -1.09, -1.55 for percentile). The result showed both methods had significant indirect effect. | predictor variable, regression analysis, sample, response variable, bootstrap, proc means | source | 12 |
| 103 | Using SAS/STAT to implement a multivariate adaptive outlier detection approach to distinguish outliers from extreme values | Paulo Macedo | A standard definition of outlier states that "an outlier is an observation that deviates so much from other observations as to arouse the suspicion that it was generated by a different mechanism" (Hawkins, 1980). To identify outliers in the data a classic multivariate outlier detection approach implements the Robust Mahalanobis Distance Method by splitting the distribution of distance values in two subsets (within-the-norm and out-of-the-norm): the threshold value is usually set to the 97.5% Quantile of the Chi-Square distribution with p (number of variables) degrees of freedom and items whose distance values are beyond it are labeled out-of-the-norm. This threshold value is an arbitrary number, though, and it may flag as out-of-the-norm a number of items that are indeed extreme values of the baseline distribution rather than outliers coming from a "contaminating" distribution. Therefore, it is desirable to identify an additional threshold, a cutoff point that divides the set of out-of-norm points in two subsets - extreme values and outliers. One way around the issue, in particular for large databases, is to increase the threshold value to another arbitrary number but this approach requires taking into consideration the size of the dataset as that size is expected to affect the threshold separating outliers from extreme values. As an alternative, a 2003 article by D. Gervini (Journal of Multivariate Statistics) proposes "an adaptive threshold that increases with the number of items N if the data is clean but it remains bounded if there are outliers in the data". This paper implements Gervini's adaptive threshold value estimator using PROC ROBUSTREG and the SAS Chi-Square functions CINV and PROBCHI, available in the SAS/STAT environment. It also provides data simulations to illustrate the reliability and the flexibility of the method in distinguishing true outliers from extreme values. |
outlier, sample, mahalanobis distance, covariance, numeric functions, normal distribution, proc robustreg, robust estimator, proc step, type i error, standard deviation, chi-square distribution, sample size | source | 11 |
| 104 | VBScript-Driven Automation in SAS : A Macro to Update Text in a Microsoft Word Document at Preset Bookmarks | Shayala Williams Gibbs | SAS can harness the power of Microsoft (MS) Visual Basic Scripting Edition (VBScript) to programmatically update MS Office documents. This paper presents a macro developed in SAS to automate updates to a MS Word document. This useful macro invokes VBScript to pass text directly from a SAS data set into a predefined bookmark in an existing Word document | dataset, macro call, character variable | source | 4 |
| 105 | VIF Regression: A SAS Application to Feature Selection in Large Data Sets | Ruiwen Zhang | Data size and dimensionality can grow big easily in data mining problems. Feature selection plays an increasingly crucial role in modern industry to improve predictive model interpretability, avoid overfitting and multicollinarity. More important is that we need the feature selection to be done fast in big data. VIF regression is a fast algorithm which does feature selection in large regression problems. VIF regression handles big number of features streamwise. Such streamwise regression method has its advantages over traditional stepwise regression as it offers faster computational speed without loss of its accuracy. We implement the algorithm in SAS language and provide a comprehensive example so that SAS users can get benefits from SAS platform or server which usually stores their big data sets and also from the VIF regression, a much-needed fast feature selection for large data sets. Keywords: Variable selection; Stepwise regression; Variance Inflation Factor (VIF); Model selection | regression analysis, stepwise selection, feature selection, multicollinearity, data cleaning, correlation, sample | 5 | |
| 106 | Web Scraping with JMP for Fun and Profit | Web Scraping | JMP includes powerful tools for importing data from web pages. This talk walks through a case study that retrieves OS usage share data from the web, and transforms it into a JMP graph showing usage changes over time. When combined with JMP's built-in formulas, value labels, and summarization methods, the end result is a tool that can be used to quickly evaluate and make decisions based on OS usage trends. | dataset, traffic data | source | 12 |
| 107 | Where in the World Are SAS/GRAPH Maps? An Exploration of the Old and New SAS Mapping Capacities | Louise S. Hadden | SAS has an amazing arsenal of tools to use and display geographic information that is relatively unknown and underutilized. This presentation highlights both new and existing capacities for creating stunning, informative maps as well as using geographic data in other ways. SAS provided map data files, functions, format libraries and other geographic data files are explored in detail. Custom mapping of geographic areas are discussed. Maps produced include use of both the annotate facility (including some new functions) and PROC GREPLAY. Products used are Base SAS and SAS/GRAPH. SAS programmers of any skill level will benefit from this presentation. |
proc step, sample, macro variable, proc gmap, dataset | source | 16 |
| 108 | You've used FREQ, but have you used SURVEYFREQ? | Charlotte Baker | PROC FREQ is a well utilized procedure for descriptive statistics. If the data being analyzed is from a complex survey sample, it is best to use the PROC SURVEYFREQ procedure instead. Other than the SAS documentation on PROC SURVEYFREQ, few user examples exist for how to perform analyses using this procedure. This paper will demonstrate why PROC SURVEYFREQ should be used and how to implement it in your research. |
proc surveyfreq, proc step, proc freq, proc means, sample | source | 8 |
| 109 | Your Database Can Do SAS Too! | Harry Droogendyk | How often have you pulled oodles of data out of the corporate data warehouse down into SAS for additional processing? Additional processing, sometimes thought to be uniquely SAS's, such as FIRST. logic, cumulative totals, lag functionality, specialized summarization or advanced date manipulation? Using the Analyical / OLAP and Windowing functionality available in many databases (e.g. Teradata, Netezza ) all of this processing can be performed directly in the database without moving and reprocessing detail data unnecessarily. This presentation will illustrate how to increase your coding and execution efficiency by utilizing the database's power through your SAS environment. | proc sql, order by, group by, data step, data warehouse, excel, proc step | source | 15 |
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