HOME QUICK SEARCH AUTHORS KEYWORDS HELP BUGS

WUSS 2011 Papers - Index

123 papers. Source: WUSS (Western Users of SAS Software) conference proceedings.

Back to Conf
go 2012
go 2010
# Title
×		 Authors
×		 Abstract
×		 Keywords
×		 Extras Pages
1 "Before and After" Models in Observational Research Using Random Slopes and Intercepts David J. Pasta In observational data analyses, it is often useful to use patients as their own controls by comparing their outcomes "before" and "after" some signal event, such as the initiation of a new therapy. It may be useful to have a control group that does not have the event but instead is evaluated before and after some arbitrary point in time, such as their birthday. In this context, the change over time is a continuous outcome that can be modeled as a (possibly discontinuous) line, with the same or different slope before and after the event. Mixed models can be used to estimate random slopes and intercepts and compare patients between groups. A specific example published in a peer-reviewed journal is presented. regression analysis, proc mixed, correlation source 12
2 %GetTweet: A New SAS Macro to Fetch and Summarize Tweets Satish Garla The role of Twitter as a source of valuable information for spotting trends has been much talked about in the popular press. The open Application Program Interface (API) in Twitter makes it one of the most sought after platforms for textual data analysis. While SAS Text Miner provides a robust method for analyzing textual data, the challenge remains to fetch customized Tweets and clean textual data before any text mining. This paper develops and discusses a new SAS macro that can be used easily to fetch the Tweets a researcher wants from Twitter. The macro uses the search API in Twitter and the HTTP procedure in SAS to create a data set of Tweets that are customized using parameters such as combination of keywords, exact phrases, omission of specific words, and so on. The macro also purges terms such as http tags and URLs that might create problems in text mining. proc step, create table, proc http, dataset, group by, keyword parameter, %macro parameters, macro variable, proc freq, proc sql source 12
3 A Guide for Using SAS and OpenXML to Create Charts in MS Excel James J. Van Campen A system of SAS macros developed by the author for generating charts in EXCEL is presented. First, there is an overview of OpenXML as it applies to EXCEL. Second, there is a discussion of how to create EXCEL workbooks with SAS. Finally, an example of an EXCEL workbook including a chart and the SAS code used to create it are presented. Intermediate SAS programming skills are assumed, including f amiliarity with the SAS macro facility. All examples use SAS 9.2 and MS Office 2007. excel, xls, macro variable, macro loop, macro definition source 9
4 A SAS Macro Program to Calculate Medication Adherence Rate for Single and Multiple Medication Use Hao Chu Different medication adherence measurements have been developed as a result of the complexity of medication consumption and different study designs. After an extensive review of methods applied in pharmacy related administrative databases, we found that Medication Possession Ratio (MPR) and Proportion Days Covered (PDC) are the two most common measurements used in pharmacoepidemiological and pharmacoeconomic studies and are also recommended by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR). This paper summarizes and presents a SAS macro program that covers different metrics for calculating medication adherence rate for single medication and multiple medication use. The results will allow clinicians and researchers to compare and select a metric that fits best to their research interest. proc sql, proc step, create table, binary variable, group by, macro definition, macro variable source 11
5 Adding PROC SQL to your SAS toolbox for merging multiple tables without common variable names Susan Wancewicz The SQL procedure offers an efficient method of creating a new dataset by merging tables in SAS. Advantages of PROC SQL over the SAS MERGE statement include: tables do not need to be sorted before joining them; and tables without a common variable can be joined simultaneously. This paper will lead the SAS user through the following steps in PROC SQL: The basic join, join of tables without common variables, a demonstration of grouping variables in order to obtain a new variable containing the average of an existing variable. At the end of this discussion the SAS user will add flexibility to their SAS toolbox for creating a new merged table. proc sql, proc step, proc means source 8
6 An Introduction to Creating Multi-Sheet Microsoft Excel Workbooks the Easy Way with SAS Vincent DelGobbo excel, xls source 2
7 An Introduction to SAS Character Functions Ronald P. Cody character variable, substr function, compress function, length statement, character functions, input statement, verify source 17
8 An Introduction to the Analysis of Rare Events Nathaniel Derby Analyzing rare events like disease incidents, natural disasters, or component failures requires specialized statistical techniques since common methods like linear regression ( PROC REG ) are inappropriate. In this paper, we'll first explain what it means to use a statistical model, then explain why the most common one (linear regression) is inappropriate for rare events. Then we'll introduce the most basic statistical model for rare events: Poisson regression (using PROC GENMOD or PROC COUNTREG ). KEYWORDS: SAS, Poisson regression, PROC COUNTREG . The graphical output in this paper is from SAS 9.3 TS1M0. All data sets and SAS code used in this paper are downloadable from www.stakana.com/RareEvents. regression analysis, proc reg, proc step, poisson distribution, likelihood function, poisson regression, goodness of fit, box plot, proc genmod, akaike information criterion, histogram, response variable source 19
9 Analyze This? Supporting Clinical Decisions Graphically When Not Enough Data is Available A Study Case: Challenges in NCI CTCAE Version 4 Grading Sheila Dayog Working with laboratory clinical data is challenging enough, but another challenge has arisen when applying the updated National Cancer Institute Common Terminology Criteria, Version 4.0 for Adverse Events (NCI CTCAE). Version 4.0 incorporates stronger clinical observation into the some of the laboratory grading descriptions, thus removing straight forward range-based numeric algorithms. Additionally, these observations can present themselves a number of ways and the task of programmatic laboratory grade assignment whilst considering these assessments offers challenges. Potential for inconsistent grading exists, possibly leading to incorrect clinical data interpretation. This paper will discuss these challenges, offering the use of SAS graphics data visualization to assist in identifying any discordant relationships between laboratory grade assignments and the adverse event grades that occur throughout the conduct of an oncology clinical trial. adverse events, vital signs, clinical trial, concomitant medications, clinical research source 4
10 Are You Taking it Day by Day or Having an Episode? Identifying and Describing Patterns in Longitudinal Data Sarah Short Data involving consecutive measurements over time arise in a variety of fields. Detecting specific patterns can be important in analysis, and some patterns that are reasonably easy to explain in common language are surprisingly tricky to identify with code that processes records sequentially one at a time. An example of this is identifying medication dosing patterns in a dataset that contains a record for each day. Clinicians may be particularly interested in looking at clinically meaningful medication dosing patterns, rather than simply calculating minimum, maximum, or mean dose over the entire treatment period. We present a method for outputting medication dose episodes from a patient-day-level dataset using a single DATA STEP with a specific goal of identifying the longest sustained dose for each patient. by group processing, proc sort source 7
11 Array, Hurray, Array; Consolidate or Expand Your Input Data Stream Using Arrays William E. Benjamin Jr. You have an input file with one record per month, and need an output file with one record per year. But you cannot use PROC TRANSPOSE because other fields need to be retained or the input file is sparsely populated. The techniques shown in this paper will enable you to be able to either consolidate or expand your input or output stream of data by using arrays. Sorted files of data records can be processed as a unit using "BY Variable" groups and building an array of records to process. This technique allows access to all of the data records for a "BY Variable" group and gives the programmer access to the first, last and all records in between at the same time. This will allow the selection of any data value for the final output record. numeric variable, excel, dataset, proc step source 11
12 Assessing resource adequacy uncertainty in California electricity summer demand using the MCMC procedure in SAS Enterprise MinerTM Miguel Garcia-Cerrutti Resource adequacy is the ability of the electric system to supply the sufficient resources to satisfy demand reliably at all times. One key challenge for optimally estimating resource adequacy is that demand is difficult to predict with certainty, particularly during summer in California where the main factor affecting it is the temperature and the demand sensitivity to it. Cooling demand responses to extreme high summer temperatures generate sudden changes and large variability in demand testing the ability of the system to meet its highest demand and so predicting it with some degree of certainty is the ultimate goal in resource adequacy planning. This paper discusses how to implement the Markov chain Monte Carlo (MCMC) procedure with the SAS Enterprise Miner platform to effectively fit Bayesian predictive modeling to the extreme cooling demand distribution to accurately assess risk in electricity summer demand. data mining, sample, macro variable, likelihood function, posterior probability, markov chain, monte carlo, proc step, prior probability, acf plot, estimate, proc means, sample size, time series analysis, regression analysis, normal distribution, proc calis, autoregressive model, density plot, quantile, quantile functions, response variable source 12
13 Assigning a User-Defined Macro to a Function Key Mary F. O. Rosenbloom,
Edwards Lifesciences		 Are you entering one or more of the same SAS Display Manager System (DMS) commands repeatedly during a session? The DMS offers a convenient way of capturing and saving frequently entered commands in a user-defined macro, and then saving the macro as a function key of your choosing. Are you typing SAS code for data exploration during program development or validation, only to delete it soon afterwards? If you are, then this code can be placed in a macro, too, and assigned to a function key. This paper illustrates the purpose and steps one would use to assign a user-defined macro to a function key. proc step, macro variable, data cleaning, macro definition, numeric variable, proc sort, proc univariate, macro call source 8
14 At Your Service: Your Roadmap to Support from SAS Kathryn A. Council At your service. How do you make the most of the products and services from SAS Publishing, SAS Education, SAS Technical Support, and support.sas.com? You have world-class support from SAS at your fingertips. But where do you start? How do you navigate the sea of information available to you from SAS? How do you find the resources you need to do your job? This paper will provide you with practical tips, tricks, and techniques to find exactly what you need to use SAS. You'll save time, learn about upcoming content and, best of all, become a more proficient and expert user of SAS. This paper will benefit the new user and the seasoned SAS user alike. The intended audience is anyone interested in learning more about how to effectively use the services available from SAS. source 6
15 Automating file pulls for recurring jobs ad-hoc reports or other SAS processes Ezekiel Budda sysrc, stored macro source 8
16 Becoming a Better Programmer with SAS Enterprise Guide 4.3 Andy Ravenna Both existing and new users of SAS are turning to SAS Enterprise Guide to write and run their code. Long-time users are accustomed to typing all their code in the Program Editor window and simply clicking Submit. New users do not have this same set of expectations and are more willing to point and click on occasion. But the truth is becoming clear; the winning programmer will be the one who has the expertise to use the best of both worlds-either coding or clicking, depending on which is more efficient for a given task. SAS Enterprise Guide 4.3 contains new functionality that can help anyone become a better programmer. This paper addresses the all-important question: When is it appropriate to code, and when to click? The purpose here is to expose new users as well as those familiar with SAS to tips and best practices that will enable them to return to the office as better programmers sas enterprise guide, proc step, excel, dataset, proc transpose, substr function, sas programmer, work library source 40
17 Benefits of JAVA Within SAS: Exploring the Differences of the new JavaObj and SAS/IML Studio Diana Shealy Released into production in SAS 9.2, the JavaObj is a new interface to Java programs and classes. Yet, this is not the first and only way to access Java's abilities for SAS. Another way to work with Java using SAS is with the programming language for SAS/IML Studio called IMLPlus. The purpose of this paper is to compare and contrast these two methods for using library-defined or user-defined Java classes. While this paper does not concentrate on Java's massive GUI and graphics packages, it does focus on using Java programs to calculate complex equations and algorithms. There are two main benefits to using Java to write such algorithms: code reuse, if the code is already written in Java, and the flexibility of the Java language. This paper will walk the user through basic Java code and how to access that code through both the JavaObj and through IMLPlus, as well as educate the user on both the advantages and limitations of each method. sas programmer, sample source 7
18 Best Practices: Clean House to Avoid Hangovers Mary F. O. Rosenbloom,
Edwards Lifesciences		 In a production environment, where dozens of programs are run in sequence, often monthly or quarterly, and where logs can span thousands of lines, it's easy to overlook the small stuff. Maybe a DATA statement fails to execute, but one already exists in the temp library from a previous program. Maybe a global macro assignment is missed or fails to execute, but a global macro of the same name already exists from a previous program. This can also happen with macros. The list goes on. This paper offers some suggestions for housekeeping steps that can be taken at the end of each SAS program to minimize the chance of a hangover. options statement, macro variable, temporary dataset, proc step, dataset, sas log source 7
19 Beyond Breslow-Day: Homogeneity Across R x C Tables Ginny Lai In the epidemiological world, we often encounter the following analytical question: is the relationship between exposure and outcome different for different strata? The Breslow-Day test (with or without the Tarone adjustment) can be used in PROC FREQ to assess homogeneity across a series of 2 x 2 tables, but what if your tables are not 2 x 2? One alternative is to fit a log-linear model to a k x R x C table and test the fit with the three-way interaction removed. This can be done easily in PROC CATMOD or, with some additional effort, in PROC GENMOD and PROC GLIMMIX. A real-world data example will demonstrate the details of different methods to analyze k x R x C tables and tests for homogeneity. proc step, proc freq, proc genmod, proc glimmix, likelihood function, p-value, sample, odds ratio source 7
20 Boost your confidence in a 2x2 standard table Iuliana Barbalau According to note 24170 issued by SAS for version SAS 9. 1.2 in September 2004, there are no statistical procedures which directly produce statistics such as se nsitivity, specificity, pos itive and negative predictive values, false positive and negative pr obabilities and their associate confidence intervals for a 2 x 2 table. In the past, various methods have been used such as formulas implemented as data steps to obtain these results. Starting with SAS 9.2 version, PROC FREQ can produce new confidence limits for the binomial proportion including Agresti-Coull, Jeffreys, and Wilson (score) confidence limits. This paper will show how a macro call can simplify a SAS progr ammer job by providing accurate and easy to use calculations for sensitivity, specificity, positive and negative predic tive values using PROC FREQ options: Wilson(score) or Exact. proc freq, proc step, negative predictive value, type i error, by group processing, binomial distribution, confidence interval source 8
21 Causal Methods for Observational Data Amanda Stevenson Comparative effectiveness research often uses non-experimental observational data (like hospital discharge records or nationally representative surveys) to draw causal inference about the effectiveness of interventions for health. These ex post inferences require the careful use of specialized statistical methods in order to account for issues like selection bias and unmeasured heterogeneity. This paper briefly discusses the strengths and weakness of some of the most common causal methods for comparative effectiveness evaluation and provides instructions for using SAS to implement propensity score matching, double difference, instrumental variables methods and regression discontinuity. treatment effect, propensity score, propensity score matching, regression discontinuity, regression analysis, instrumental variables, potential outcomes source 8
22 ChiD, A Chi-2 - Based Discretization Algorithm Ross Bettinger We have developed a discretization algorithm, based on Kerber's ChiMerge and Liu and Setiono's Chi2, that automatically chooses the best set of cutpoints for dividing a continuous variable into a set of contiguous discrete intervals. The algorithm, ChiD, uses class information to perform supervised discretization based on maximizing the logworth of the significance of a 2χ statistic computed from adjacent intervals of the continuous variable being discretized. The ChiD algorithm generates cutpoints that match the quality of those computed by the Enterprise Miner Decision Tree algorithm as measured by the accuracy of classification models built using ChiD cutpoints versus original, undiscretized data. proc hpsplit, data mining, sample, interval variable, proc freq, significance level source 9
23 Clinical Data Integration for Observational Studies Using Electronic Health Records:Examples with Defining Pregnancy and Gestational Diabetes Beinan Zhao Electronic Health Records (EHRs) have increasing potential for secondary use in observational research studies, including studies of drug safety and comparative effectiveness of interventions. However, clinical data from different sources must be successfully integrated and thoughtfully prepared to create robust research definitions. As one step in using EHR data, it is often necessary to determine episodes of a health condition from multiple data sources. In this paper, we demonstrate one approach in defining such episodes with pregnancy and gestational diabetes as examples of how we integrate clinical data for observational studies using EHRs at a multi-clinic, outpatient healthcare system in Northern California. clinical data, proc sql, raw data, proc step source 7
24 Clinical Data Review on iPad with DEFINE.XML Sy J. Truong Clinical data review on a tablet computer is a natural step in the evolution of automated information delivery. Clinical Research Associates and Medical Monitors require a unique method of accessing information during the conduct of a clinical trial and also at the pivotal conclusion, or submission of a trial. The challenge remains to overcome the technical complexity of metadata models such as DEFINE.XML. This paper describes these challenges as it elaborates upon methods of using SAS to deliver clinical data in a format that can be easily navigated on an iPad. clinical data, dataset, clinical research, proc contents, adverse events, data review, proc step, clinical trial, cdisc source 10
25 Combining PROC SQL Summary Functions with Logical Expressions Glenn Wright In SAS, a true expression such as "2+2=4" or "3>=0" resolves to the numeric value 1, and a false expression such as "2+2=5" resolves to the numeric value 0. Such expressions can be used as input for PROC SQL summary functions in order to create customized summary variables that draw upon multiple records. For example, one could use this technique on a database of healthcare claim lines in order to create client summary records showing which clients received a certain combination of services on at least one day during a certain time period. One can also combine these techniques with PROC SQL JOINs to create summary variables that combine data from multiple sources. proc sql, proc step, group by, flag variable, by group processing, proc means source 5
26 Comparison of K-Means, Normal Mixtures and Probabilistic-D Clustering for B2B Segmentation Using Customers Perceptions Satish Garla Cluster Analysis is a popular technique used by businesses and analysts for market segmentation. For segmentation, clustering is used to split customers in a market into meaningful groups such that the customers within a group are similar and customers between the groups are dissimilar. Several clustering methods and numerous clustering algorithms are available in existing software packages and new ones frequently appear in the literature. These methods and algorithms vary depending on how the similarity between observations is defined or on other assumptions about shapes of clusters, distributions of variables, etc. This paper describes a comparative study of three clustering methods (K-means, Normal Mixtures and Probabilistic-D) for segmenting business-to-business (B2B) customers using their perceptions. data mining, cluster analysis, euclidean distance, outlier source 9
27 Conditional Processing Using the Case Expression in PROC SQL Kirk Paul Lafler The SQL procedure supports conditionally selecting result values from rows in a table (or view) in the form of a case expression. Similar to an IF -THEN construct in the DATA STEP, a case expression uses one or more WHEN -THEN clause(s) to conditionally process some but not all the rows in a table. An optional ELSE expression can be specified to handle an alternate action should none of the expression(s) identified in the WHEN condition(s) be satisfied. This presentation will illustrate the basic syntax associated with the two forms of case expression and examples of each. proc sql, if-then-else, character variable, missing value, proc step, select source 8
28 Consistency of Treatment Effect cross Regions Yunfeng Li Multi-Regional Clinical Trials are becoming increasingly common. The evaluation of the consistency of treatment effect cross regions is not only important to support the robustness of the conclusions, but is also frequently requested by local regulatory agencies in support of registration. There are numerous recent publications on this topic. Most of these focus on superiority trials with a continuous endpoint. In this presentation, we will explore the consistency of treatment effect across regions in the context of a non-inferiority trial with a binary endpoint using simulations. It is anticipated that this approach will help plan for the potential impact of observed regional differences and thus help to improve study design and understand the impact on sample size calculations. treatment effect, sample, sample size, clinical trial, confidence interval, treatment arm, regression analysis source 6
29 Create compelling visualizations with geographic data and JMP 9 Jeff Perkinson JMP 9 introduces exciting graphical support for geographic data. In this presentation, you learn how to use the built -in background maps in any plot of geographic data. You also see how to connect to a Web mapping service to display specialty maps like satellite images, radar images, or roadways. Because JMP 9 can plot geographic shapes based on place names, we demonstrate how to use the flexible architecture to create your own shape files to plot any location, like a floor plan or a campus. Lastly, we show how to convert SAS map data sets for use in JMP. dataset source 10
30 Creating SAS Datasets from Varied Sources Sofia Shamas,
Mansi Singh		 Often SAS programmers find themselves dealing with data coming from multiple sources and usually in different formats. Steps have to be taken to logically relate the process and convert the variety of data into SAS data sets before it can be analyzed. Since these sources do not follow a similar pattern, this paper is to serve as a collection of examples illustrating the conversion of data coming from various sources, such as extensible markup language (XML), comma separated values (CSV), Microsoft EXCEL (XLS), or tab delimited (TXT) files to SAS data sets. dataset, infile statement, proc step, filename statement, txt, xml, excel, attrib statement, character functions, proc import source 8
31 Creating Stylish Multi-Sheet Microsoft Excel Workbooks the Easy Way with SAS Vincent DelGobbo This paper explains how to use Base SAS 9 software to create multi-sheet EXCEL workbooks (for EXCEL versions 2002 and later). You learn step-by-step techniques for quickly and easily creating attractive multi-sheet EXCEL workbooks that contain your SAS output using the ExcelXP ODS tagset and ODS styles. The techniques that are presented in this paper can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! Creating and delivering your workbooks on-demand and in real time using SAS server technology is discussed. Although the title is similar to previous papers by this author, this paper contains new and revised material not previously presented. excel, xls, output delivery system, sas output, proc report, proc template, sas base, proc step, by group processing, ods xml, sample, proc format, dataset source 22
32 Customized Graphics Made Simpler Sofia Shamas,
Mansi Singh		 With the production release of SAS Graph Template Language (GTL) and the introduction of Statistical Graphics (SG) procedures, it has become much easier to customize graphical output. These new SAS features combined with the Output Delivery System (ODS) allows user to customize graphs with less efforts than traditional SAS graph methods. This paper will illustrate using specific SAS codes, how to utilize GTL and SG procedures to create eye- catching graphs. Scatter Plot and Kaplan Meier will be used as examples in this presentation. scatter plot, proc step, kaplan-meier plot, proc sgplot source 6
33 Data Visualization of User Interface Navigation Using Interactive Treeview Diagrams Aruna Buddana source 8
34 Effects of Timing of Surgery in Children with Ureteropelvic Junction Obstruction: A Pediatric Health Information System (PHIS) Database Study using SAS Andrea Masias Ureteropelvic junction (UPJ) is an obstruction in the ureter that can lead to hydronephrosis which is a swelling of the kidney due to a lack of urine passage. It is the most common cause of congenital hydronephrosis and affe cts roughly 60,000 children annually1. Pyeloplasty is a surgical intervention used to correct the condition . The increased number of antenatal ultrasounds being performed has resulted in an earlier diagnosis of the condition and in some cases earlier pyeloplasty. Based on previous research, we hypothesize that those who receive early pyeloplasty will require more readmissions to the hospital within one year following the initial pyeloplasty, will have longer lengths of hospital stay and will have higher overall adjusted hospital charges compared to those who receive delayed pyeloplasty. We will test these hypotheses using SAS software version 9.2. chi-square test, proc step, proc freq, histogram source 8
35 Efficient Techniques and Tips in Handling Large Datasets Jason Shilong Kuang When we work on millions of records, with hundreds of variables, it is crucial how we are processing o ur data. To make SAS really ROCK, we need to pay more attention to SAS program efficiency, since a single DATA STEP or some SQL query may take a few hours in dealing with such large datasets. In this paper, we present a f ew practical efficient techniques and hands-on tips in handling large datasets, including the application of INDEX, separating one single step into multi-step to improve efficiency, the classic Where vs. If statement, some tips in jo ining large datasets in PROC SQL etc. To see the efficiency of those techniques, we also provide for each case with experimental example output, how much for the time-resource consuming, "apple-to-app le" comparison between the processes with and witho ut those techniques . cpu time, proc sql, data step source 5
36 Estimating Disease Prevalence from Clinical Data Using Capture-Recapture Carol Conell Capture-recapture analysis provides a natural way to model disease prevalence using the longitudinal diagnostic and treatment data from Electronic Medical Records. This technique corrects for ascertainment bias due to not yet diagnosed incident cases as well as cases not being actively treated. This paper shows how SAS can be used to transform routinely generated data into a series of repeated measures of disease identification and to apply capture-recapture analysis to the resulting series. A simple example combining survey and clinical data on alcohol and substance use disorders is presented. likelihood function, confidence interval, clinical data, sample, repeated measures, interaction effect, proc genmod, proc step source 10
37 Estimating Sample Size through Simulations Wuchen Zhao Determining sample size is one critical and important procedure for designing an experiment. The sample size for most statistical models can be easily calculated by using the POWER procedure. However, the PROC POWER cannot be used for a complicated statistical model. This paper reviews a more generalized method to estimate the sample size through a simulation approach by using SAS software. The simulation approach not only applies to the simple but also to a more complex statistical design. sample size, sample, null hypothesis, numeric functions, proc power, proc ttest, standard deviation, type i error, type ii error, sas output, interaction effect source 6
38 Evaluation of the Psychometric Properties of the Inventory of the Dimensions of Emerging Adulthood Nadra Lisha It is now presumed that youth do not move directly from adolescence to adulthood, but rather pass through a transitional period, "emerging adulthood." The Inventory of the Dimensions of Emerging Adulthood (IDEA) is a self- report instrument developed to examine the attributes of this period. In the present study, a 21-item version of the IDEA was administered to a sample of 1676 "at risk" continuation (alternative) high school students in Southern California. Principal component factor analysis with orthogonal rotation revealed three factors. Overall, the measure demonstrated high internal consistency. Construct validity analyses indicated that the measure was correlated with demographics, risk behaviors, and psychological measures. We conclude that the IDEA is a useful instrument for measuring emerging adulthood in at-risk populations. sample, factor analysis, correlation, orthogonal rotation, proc princomp, regression analysis source 33
39 Exporting Variable Labels as Column Headers in Excel using SAS Chaitanya Chowdagam EXCEL output is the desired format for most of the ad-hoc reports requested for an easy interpretation and exploration of data. However, to make these reports more informative variable labels could be used instead of variable names as column header. This is harder to achieve by the conventional SAS EXPORT procedure, PROC EXPORT. This paper will provide easy and quick methods of exporting SAS data to multiple EXCEL sheets with variable labels as column headers using EXCEL LIBNAME and ODS Markup excel, proc step, label statement, proc export, output delivery system, ods excel, ods listing, sas 9 source 5
40 Fitting latency models in epidemiological studies Bryan Langholz Latency models address important questions about the timing of exposure and subsequent disease risk in epidemiologic research. However, the non-standard form of such models and the complexity of time-varying exposure histories that characterize many epidemiologic studies make such models difficult to fit using standard software packages. SAS offers software tools that can be used to fit "non-standard" latency models. In particular, SAS procedures NLP and NLMIXED provide the flexibility to specify quite general latency models and maximize the likelihood for latency parameters. While the methods require more sophistication than using a package or procedure that is specialized to the usual log-linear form, the additional programming complexity is not great. The methods are illustrated and compared by fitting latency models for radon exposure and lung cancer mortality rates from a cohort study of Colorado Plateau uranium miners. likelihood function, logistic distribution, proc step, proc calis, estimate, relative risk source 7
41 Getting Freqy with PROC REPORT Ethan Miller Are you unfamiliar with PROC REPORT or do you feel a bit overwhelmed by it sometimes? Are you familiar with PROC REPORT but want to take it to the next level? This paper is for you! With just a brief investment of time, you too will be able to incorporate PROC REPORT into your SAS repertoire. This paper will illustrate how to produce frequencies, cross tabulations, and means using PROC REPORT, and show various style options that can be used with ODS. It will also show how to (1) create totals using compute blocks; (2) format the report; (3) add titles, subscripts, superscripts, and custom footnotes that include statistics; and (4) report missing data in different ways. proc report, proc step, class variable, proc freq, missing value source 10
42 Getting Up to Speed with PROC REPORT Kimberly J. LeBouton Learning the basics of PROC REPORT can help the new SAS user avoid hours of headaches. PROC REPORT can often be used in lieu of PROC TABULATE or DATA _NULL_ reporting--two areas that have driven the ne w SAS user crazy!!! With the added capabilities of ODS, P ROC REPORT can look as sharp as an EXCEL report. Th is paper will show how to use PROC REPORT in both a wi ndowing and non-windowing environment using SAS Version 9. proc report, proc step, proc print, proc tabulate, proc means, variable source 8
43 Graphing a Progression of Time Series Plots with ODS Graphics Nathaniel Derby Graphing is an essential step for exploratory data analysis and statistical modeling. However, when graphing an ordered progression of time series plots, it can be difficult to effectively display the progression without looking disorganized and chaotic. This paper shows a couple of approaches to this problem using the GPLOT procedure from SAS/GRAPH software and the LAYOUT OVERLAY,LAYOUT DATAPANEL andSERIESPLOT statements from the Graphic Template Language (GTL) in ODS statistical graphics. KEYWORDS: SAS, graph, GTL, time series. All data sets and SAS code used in this paper are downloadable fromhttp://nderby.org/docs/RUG11-TSPlots.zip. time series analysis, exploratory data analysis, data science, series plot, random intercept, macro definition source 11
44 Graphing Made Easy with SG Procedures Susan J. Slaughter New with SAS 9.2, ODS Graphics introduces a whole new way of generating graphs using SAS. With just a few lines of code, you can create a wide variety of high-quality graphs. This paper covers the three basic Statistical Graphics (SG) procedures-SGPLOT , SGPANEL and SGSCATTER. SGPLOT produces single-celled graphs. SGPANEL produces multi-celled graphs that share common axes. SGSCATTER produces multi-celled graphs that may use different axes. This paper shows how to use each of these procedures to produce different types of graphs , how to send your graphs to different ODS destinations, how to access individual gr aphs, and how to specify properties of graphs, such as format, name, height and width. bar chart, phase ii, scatter plot, proc sgpanel, density plot, histogram source 18
45 Has Anybody Opened My File? Find Out Before Trying to Update it Olena Galligan We've all been in the situation where we run a long code just to find out several minutes later that somebody opened a file which your program is trying to update. Thus, no file update for you at this time. The macro demonstrated in this paper saves your time by letting you find out right away whether the file (.RTF, .DOC, DOCX, .XLS, XLSX, SAS7BDAT, .PDF) is opened by somebody and thus, whether it could be updated by your program. SCL (SAS Component Language) does most of the work, and I will describe not only my macro, but also some of the SCL elements you'll find useful in your everyday programming activities. macro variable, error message, sysrc, macro parameter, dataset source 12
46 HELP, My SAS Program isn't Working: Where to Turn When You Need Help Kimberly J. LeBouton Instead of a quick code fix, I often assist the SAS user by troubleshooting their issue with my knowle dge of SAS and the SAS community. For over 20 years, I have provid ed SAS Technical Support, and this paper will prese nt strategies I have used to work through simple to co mplex technical problems, and will include access t o my "cheat sheets". put statement, sample, sas log, macro put source 4
47 Helping Students Become Effective Industry Statisticians: Supplementing Science with Data Savvy Aleksandra Stein In the strictly regulated environment of the pharmaceutical industry, a Contract Research Organization (CRO) provides a wide range of services including, but not limited to, design of a protocol, selection or monitoring of investigations, evaluation of reports, and preparation of materials to be submitted to the Food and Drug Administration. With regulatory agencies' recent endorsement of CDISC data submission standards, statistical programmers and statistical scientists now need a solid, functional understanding of data to be effective. Academia, however, has not yet embraced a data-focused training of statisticians. This paper examines the emerging discrepancy between the scientific knowledge students are equipped with and the focus on data required to work in industry. pharmaceutical industry, data science, statistical programmer, proc step, clinical data source 5
48 How to easily convert clinical data to CDISC SDTM Ale Gicqueau sdtm domain, clinical data, data cleaning, excel, label statement, data governance, cdisc source 9
49 Hunting for Columbus' Eggs in the SAS Programming World: A Guidance to Creative Thinking for SAS Programmers Alice M. Cheng A Columbus' Egg, according to Wikipedia, refers to a brilliant idea or discovery that seems simple or easy after the fact. In this paper, the author presents several Columbus' Eggs that may be helpful to fellow SAS programmers. SAS examples are given, followed by learning tips to reminder readers what they actually have learned. Examples include catchy phrases to remember basic SQL structure, an innovative use of a SAS function, methods to white out/cover up unwanted information, horizontal vs. vertical approach to resolve a problem, as well as, divide and conquer technique. It is the author's intention to not only provide readers with useful techniques, but more importantly, to stimulate them to think outside the box and hunt for their own Columbus' Eggs! regression analysis, confidence interval, proc sql, prediction interval, sas programmer, sas log, clinical trial, proc gplot, proc sgplot, macro flow control, proc step, sas programming source 12
50 If you imbed it... ? Donald Price Businesses and organizations, faced with ever increasing competitive pressures, need to make better decisions. Better decision making requires superior analytics - the extensive use of data, statistical, quantitative, and qualitative analysis, exploratory and predictive models, and fact -based management. These same companies are faced with a growing talent shortage - a shortage of skilled analysts. Recent anecdotal articles and academic studies highlight the need to incorporate contemporary information technology developments in the practice of marketing. This paper provides insights into the efforts of Metropolitan State College of Denver to incorporate these developments into undergraduate marketing studies by imbedding the subject of analytics into marketing curriculum. excel, business intelligence source 5
51 Improving Your SAS Skills and Expertise - Career Path Considerations Kirk Paul Lafler Many SAS users believe they have mastered the necessary skills to be successful. But as technology evolves, it becomes increasingly more important to continue learning, and consider specializing. Even if you already consider yourself a good SAS programmer or user, with interests and abilities in several areas of the SAS software, specialization is becoming more of a necessity. This presentation will explore the many options available to users for improving their skills and expertise. sas 9, sas base source 6
52 Introduction to SAS Programming Lily Xu Data is everywhere today and SAS skills are in high demand. This workshop is designed for those who have little or no previous programming experience in SAS. From the workshop, you will learn how to: write simple SAS programs read raw data files and existing data sets create temporary and permanent SAS data sets generate simple statistical reports You will also have the opportunity to gain some hands-on programming experience on basic SAS procedures. dataset, proc step, proc print, proc freq, libname statement, proc means source 10
53 Is the Legend in your SAS/Graph Output Still Telling the Right Story? Alice M. Cheng In clinical studies, researches are often interested in the effect of treatment over time for multiple treatments or dosing levels. Usually, in a graphical report, the measurement of treatment effect is in the vertical axis and a second factor, such as time and visit number, in the horizontal axis. Multiple lines are displayed in the same figure; each line represents a third factor, such as treatment or dosing groups. It is critical that the line appearances (color, symbol and style) are consistent throughout the entire clinical report, as well as, across clinical reports from related studies. Flavin and Carpenter (2004) showed that the GPLOT procedure, by default, did not guarantee consistency in line appearances. They provided macro and non-macro solutions to this problem. With the introduction of Statistical Graphics in SAS version 9.2, there are multiple approaches to resolve this problem. proc sgplot, class variable, proc gplot, graph template language, proc step, in= source 12
54 Is Your SAS Library a Disk Hog? Here's How to Put it on a Diet Ross Bettinger We have developed a set of SAS macros to deal with big SAS libraries that hog large amounts of disk space. We may put them on a diet by squeezing the unnecessary bytes out of them without losing any accuracy of numeric variables or the contents of character variables. Using these macros may result in significant reductions in disk space. In one case, a SAS library containing 177 datasets which required 44.9 GB was reduced to 21.3 GB, a reduction in disk space of 52.6%. sas library, character variable, dataset, numeric variable, error message, macro definition source 6
55 JMP Scripting: Advanced Techniques Erin Vang An introduction to the JMP Scripting Language (JSL) by Erin Vang, author of the first JMP Scripting Guide (JMP version 4) and custom scripting consultant with Global Pragmatica (http://globalpragmatica.com), this talk will present an example of how to build a custom analysis with a nice point-and-click interface. raw data, proc freq, dataset source 12
56 Let's Give 'Em Something to TOC about: Transforming the Table of Contents of Your PDF File Bari Lawhorn In PDF files, the table of contents provides a map that helps your audience to navigate the document easily. However, the default table of contents that is generated by the SAS Output Delivery System (ODS) destination, while informative, is fairly utilitarian. Your procedures and DATA steps generate tables and graphs that have meaning to you and your audience. Likewise, the table of contents should also be as meaningful as possible by clarifying the contents of your PDF. This paper explains and demonstrates step by step how to use the following statement, options, and procedures to customize your table of contents: the ODS PROCLABEL statement the CONTENTS= and the DESCRIPTION= options the DOCUMENT destination and procedure the TEMPLATE procedure These tools provide you with the flexibility and the power to customize your table of contents so that you really leave your audience with something to TOC about! proc step, proc report, sample, proc gchart, output delivery system, ods pdf, proc greplay, proc gplot, proc freq, proc print, proc reg, proc sql, missing value, sas programmer, proc sgpanel, proc tabulate, macro variable, proc template, file statement, sas output source 18
57 Linkage of hospitalization records with time series data to define chronic disease events in a prospective epidemiologic study Nadia Chung Hospital administrative databases provide a rich source of detailed information on a wide range of diagnoses, procedures, and clinical endpoints. Prospective epidemiologic studies are useful for a ssessing exposure-disease relationships, but identifying and verifying the multiple disease endpoints that occur among study participants during follow-up is costly and time-consuming. Direct linkage of hospital administrative databases with prospective epidemiologic data enables investigators to study temporal trends in disease incidence and mortality and assess potential changes in both risk factors and outcomes over time. However, resolving multiple records of hospitalizations among individual patients , assigning rules and priorities to different hospitalizations, and resolving conflicting information from successive patient visits in order to maintain consistent and high -quality endpoint definitions among these linked data can be challenging. data step, proc print, proc step, proc freq source 10
58 Macro Quoting to the Rescue: Passing Special Characters Mary F. O. Rosenbloom,
Edwards Lifesciences		 We know that we should always try to avoid storing special characters in macro variables. We know that there are just too many ways that special characters can cause problems when the macro variable is resolved. Sometimes, however, we just do not have a choice. Sometimes the characters must be stored in the macro variable whether we like it or not. And when they appear we need to know how to deal with them. We need to know which macro quoting functions will solve the problem, and even more importantly why we need to use them. This paper takes a quick look at the problems associated with the resolution and use of macro variables that contain special characters such as commas, quotes, ampersands, and parentheses. macro variable, macro call, macro quoting function, call execute, where statement, macro function, macro trigger, proc print, macro facility, ods rtf, proc step, macro loop, do loop, output delivery system, autocall macro source 10
59 Macro Tabulating Missing Values, Leveraging SAS PROC CONTENTS Adam Chow Metadata, in a broad context, is data about data. Executing a SAS PROC CONTENTS is a form of metadata. When you run a PROC CONTENTS on a SAS data set, the results tell you various attributes of the data set. Moreover, it details each variable in the data set, such as the name, the variable type (numeric or character), the variable label that makes the name's mnemonic a little bit more meaningful, and the variable format that instructs the SAS System on how the values are to be displayed externally. As insightful as PROC CONTENTS is, however, it does not tell you anything about missing values. Accordingly, by leveraging the results from PROC CONTENTS, the %CHK_MISSING macro described in this paper, tabulates missing values for each variable in a data set and reports the count and the corresponding percentage in a separate data set. This paper will: 1) summarize the macro specification; 2) examine the design methodology and syntax; and 3) demonstrate how it is used with an illustration. proc step, proc contents, proc sql, missing value, macro variable, dataset, macro loop, sas log, macro flow control, macro definition, %if-%then-%else, create table, missing function, macro put, macro function, sas output, options statement source 9
60 Making the Most Out of Multilabel Formats Tasha Chapman This paper will discuss how to use multilabel formats, how to control the sort order of your formats, how to print rows with missing data, and how to maintain indentations and leading spaces in ODS output. Multilabel formats allow a SAS user to display over-lapping ranges. Among other things, multilabel formats allow a user to create hierarchical formats, showing multiple levels of groups and sub-groups in the same table. Multilabel formats have been available since SAS Version 8, and are currently compatible with PROC TABULATE, PROC MEANS and PROC SUMMARY. Multilabel formats cannot currently be used with PROC REPORT (as of version 9.2). The following examples were created in SAS version 9.1.3, using multilabel formats in PROC TABULATE and output using ODS PDF (SASweb style) proc tabulate, proc step, proc format source 7
61 Managing Your Intellectual Property: Optimizing Your Investment of Resources by Building a Searchable SAS Program Repository Joe Perry The sad fact is that after years of accumulating programs either individually or as an organization, the width, breadth and innovative programming techniques can get lost. Unfortunately, when looking for key code, MS Windows' search engine can miss character strings and, important for the task of managing your intellectual property (IP), lack the tools to properly search this source of IP. This paper discusses application design techniques and a macro that will create a searchable code repository that can help answer the key questions individuals and organizations should be asking about the library of SAS programs they have accumulated. quality assurance, quality control source 3
62 Modeling Interactive Advertising on TiVo: Overcoming the Challenges of Collinearity, Efficiency and Practicality Rachel Poulsen For interactive advertising on TiVo, benchmarking the performance of one advertisement against similar advertisements is important to many areas of the business. One of the measures of success of an interactive advertisement is the Click-through rate (CTR). Understanding the combination of ad placement and air time that will maximize the CTR for each advertisement provides the sales team with an attractive product, the advertising client with the best success, and the design team with an effective user-interface. There are many challenges associated with modeling CTR. The business demand for a practical and timely model coupled with the large amount of data associated with CTR presents a computational challenge. Many of the variables used to model CTR are measurements of human behavior, resulting in problems with collinearity. Some of the variables that appear to be unrelated will describe similar human behaviors. predictor variable, proc princomp, proc step, proc reg, feature selection, regression analysis, correlation, sample source 10
63 Now You Can Annotate Your Statistical Graphics Procedure Graphs Dan Heath Have you ever had a situation where you wanted to add custom labeling or other graphical features to your statistical graphics (SG) procedure output but could not find a way to do it? Even with all of the plot types and options of the SG procedures, you might have run into this situation. With SAS 9.3, you can add these features directly by using the new annotation support in the SG procedures. The annotation data set definition used by these procedures has been redesigned to take advantage of ODS Graphics functionality. This paper will discuss this functionality in detail, highlighting new features in this system that include the following: rich text support (including superscripts, subscripts, and Unicode) transparency support for all primitives more drawing spaces for easier placement of annotations bar chart, forest plot, proc sgplot, proc step source 18
64 On Deck: SAS/STAT 9.3 Maura E. Stokes SAS/STAT 9.3, coming soon to a site near you, delivers numerous enhancements to the statistical software. The PHREG procedure supports frailty models for incorporating random effects in Cox regression, and the MCMC procedure provides a RANDOM statement to facilitate fitting Bayesian models with random effects. The NLIN procedure has been updated, and the MI procedure offers additional flexibility by providing a fully conditional specification method. The new SURVEYPHREG and HPMIXED procedures are also outfitted with additional capabilities. This talk reviews the highlights of SAS/STAT 9.22 and then describes important SAS/STAT 9.3 enhancements with practical illustrations, mainly from the SAS/STAT 9.3 documentation. poisson distribution, likelihood function, proc mixed, sample, prior probability, maximum likelihood estimation, poisson regression, proc step, regression analysis, estimate, proc phreg, response variable, zero-inflated poisson, confidence interval, logistic distribution, proc freq, binomial distribution, numeric functions, proc nlin, proc genmod source 20
65 Output SAS DATA Step Views: an Experimental Feature Thomas E. Billings Output DATA STEP views are a specialized experimental feature in the Base SAS system. They are compiled _NULL_ DATA steps that are executed as output from a PROC or DATA STEP. Simple examples of output DATA STEP views are demonstrated and explained. Differences between output views and other types of views supported in SAS are described. The limitations/constraints of output views are specified, e.g., views are compiled hence the PDV (program data vector) must be fully defined and cannot change, and _NULL_ DATA steps (by themselves) don't create an output SAS data set. Possible approaches to mitigate these limitations are discussed, including macros to create a set of ATTRIB statements to define the PDV, and the possibility of using a hash object to produce an output SAS data set from a view. Potential applications of output views are described. DATA STEP views: an overview SAS DATA STEP views are compiled SAS DATA STEP programs that are executed when invoked. dataset, hash table, proc step, attrib statement, data step, set statement, sample, character variable, proc contents, macro variable source 9
66 Power Analysis: What is Available and What You Need to Know Gerry Hobbs Planning should be the first step in any scientific experimentation. A critical part of planning is the determination of the sample size necessary to achieve the goals of the particular project. It is clearly a waste of resources to have too many experimental units (subjects, animals, ingots or whatever). It is equally a waste of resources to carry out an investigation with so few experimental units that there is little hope of demonstrating whatever effect it is that we wish to demonstrate. SAS Software provides tools that can be used in order to guide us in our efforts to determine the appropriate sample size. sample, sample size, proc step, proc power, proc power and sample size, standard deviation, proc calis, binomial distribution, proc glmpower source 11
67 Powerful and "Sometimes" Hard-to-find PROC SQL Features Kirk Paul Lafler The SQL Procedure contains many powerful and elegant language features for SAS users in the know. This presentation explores topics that will help users unlock the many features, options, and other hard -to-find gems found in the SQL universe. Topics include querying and subsetting data; conditional processing and restructuring data by using case expressions; con structing and using virtual tables known as a view; access information from Dictionary tables; joining two or more tables to explore data relationships; and query performance tuning. proc sql, proc step, where statement, dictionary tables, select, macro variable, inner join, sas library, sas log, macro facility, view, create table, group by, macro call, order by, right join source 17
68 Practically Perfect Presentations Cynthia L. Zender PROC REPORT is a powerful reporting procedure, whose output can be "practically perfect" when you add ODS STYLE= overrides to your PROC REPORT code. This hands-on workshop will feature several PROC REPORT programs that produce default output for ODS HTML, RTF and PDF destinations. Workshop attendees will learn how to modify the defaults to change elements of PROC REPORT output, such as HEADER cells, DATA cells, SUMMARY cells and LINE output using ODS STYLE= overrides. In addition, attendees will learn how to apply conditional formatting at the column or cell level and at the row level using PROC FORMAT techniques and CALL DEFINE techniques. Other topics include: table attributes that control interior table lines and table borders, use of logos in output and producing "Page x of y" page numbering. The rest of this document is divided into two parts: the slides used in the Hands-On Workshop presentation and the attendee worksheet. proc report, proc step source 40
69 Principal Component Regression as a Countermeasure against Collinearity Chong Ho Yu There are different approaches to counteract the threat of multicollinearity in regression modeling, such as centered-score regression, orthogonalization, partial least square, and ridge regression. Principal component regression (PCR) is an under-use option because it takes multiple steps to accomplish the goal. This paper will illustrate different steps of performing PCR using the data set compiled by Programme for International Student Assessment (PISA) and a few other databases. Typically, PCR consists of four steps: 1. Principal component analysis (PCA), 2. Principal component regression (PCR) under partial least squares (PLS), 3. Factor analysis, and 4. OLS regression. Initially, PCA is run to verify whether collinear predictors could be combined to form a composite score. Further, the component structure is verified by principal component regression under PLS. proc princomp, regression analysis, factor analysis, r-squared, predictor variable, data cleaning, index, response variable, dimensionality reduction, varimax rotation source 7
70 Probabilistic Record Linkage in SAS Glenn Wright Record linkage is the process of deciding which records from one or more databases refer to the same entity, even when those records do not match exactly on any combination of identifiers. For example, we may wish to determine that a record for "Glenn Thomas Wright" living at "1234 West Fifth Street" refers to the same person as a record for "Glen T Wright" living at "1234 Fifth Street," but not the same person as a record for "Denis Hulett" living at "678 Ninth Street." Probabilistic record linkage is a family of record linkage techniques that assigns similarity scores to pairs of records and treats all pairs that score above a certain threshold as matches. This paper describes a method for implementing probabilistic record linkage in SAS, using PROC SQL and other tools. proc sql, record linkage, proc step, inner join, create table, proc freq, histogram source 13
71 PROC TABULATE: Doing More Arthur L. Carpenter Although PROC TABULATE has been a part of Base SAS for a very long time, this powerful analytical and reporting procedure is very under utilized. TABULATE is different; it's step statement structure is unlike any other procedure. Because the programmer who wishes to learn the procedure must essentially learn a new programming language, one with radically different statement structure than elsewhere within SAS, many do not make the effort. The basic statements will be introduced, and more importantly the introduction will provide a strategy for learning the statement structure. The statement structure relies on building blocks that can be identified and learned individually and in concert with others. Learn how these building blocks form the structure of the statement, how they fit together, and how they are used to design and create the final report will be presented. proc step, proc tabulate, missing value, proc format, output delivery system, format statement source 19
72 PROC TABULATE: Getting Started Arthur L. Carpenter Although PROC TABULATE has been a part of Base SAS for a very long time, this powerful analytical and reporting procedure is very under utilized. TABULATE is different; it's step statement structure is unlike any other procedure. Because the programmer who wishes to learn the procedure must essentially learn a new programming language, one with radically different statement structure than elsewhere within SAS, many do not make the effort. The basic statements will be introduced, and more importantly the introduction will provide a strategy for learning the statement structure. The statement structure relies on building blocks that can be identified and learned individually and in concert with others. Learn how these building blocks form the structure of the statement, how they fit together, and how they are used to design and create the final report will be presented. proc step, proc tabulate, missing value, proc format, output delivery system, format statement source 19
73 Producing Cumulative Distribution Frequency Figures with Minimal Important Difference Reference Lines to Assess Quality of Life Treatment Effects Xiao Liu Quality of Life (QoL) data are generally collected in clinical trials at baseline and follow-up. One method of assessing treatment effects on QoL involves generating cumulative distribution frequency (CDF) figures. These figures illustrate the probability that a QoL score will have changes by at least a specific amount. By including vertical lines representing the Minimal Important Difference (MID) - i.e., the smallest change in QoL score perceived to be important - the cumulative percentage of patients who achieve at least the MID can be obtained for each treatment group. If a data point does not exist at the exact value of the MID, interpolation using adjacent data points is necessary. The treatment group with larger percentages at the MID is considered superior. cumulative frequency, proc freq, treatment effect, sample source 7
74 Protecting Macros and Macro Variables: It Is All About Control Arthur L. Carpenter In a regulated environment it is crucially important that we are able to control, which version of a macro, and which version of a macro variable, is being used at any given time. As crucial as this is, in the SAS macro language this is not something that is easily accomplished. We can write an application that calls a macro that we have validated, but the user of the application can write and force the use their user written un-validated version of that same macro. Values of macro variables that we have populated can be 'accidentally' replaced by user written assignments. How can you guarantee that the end results are accurate if you cannot guarantee that the proper programs have been executed? Although our tools are limited, there are a few options available that can be used to help us control our macro execution environment. These, along with management programs, can give the application developer better control, and greater protection, during the execution of the application. macro variable, stored macro, symbol table, macro definition, options statement, autocall macro, local symbol table, proc sql, global symbol table, macro facility, call symputx, proc step, catalog, macro call, proc printto, sas programming, macro quoting function source 11
75 Quick ODS OUTPUT Statements Patrick Thornton The two short SAS macro programs in this paper facilitate almost instantaneous access to the data sets of results that are available from a given procedure through ODS OUTPUT. Typically programmers manually create ODS OUTPUT statements by submitting a procedure bracketed by ODS TRACE ON and ODS TRACE OFF, copy the ODS object names from the log, and resubmit the procedure with an ODS OUTPUT statement that incorporates the object names. Using the macro programs in this paper, you can often achieve the same result easier by bracketing the first run of a procedure with calls to the macro programs, followed by a second run of the procedure and an ODS OUTPUT CLOSE. The first macro program, called just prior to a procedure, uses PROC PRINTTO and FILENAME to direct the procedure log and output to temporary locations. ODS TRACE ON is used to list the ODS object names in the log. ods output, output, proc step, proc contents, proc printto, output delivery system source 3
76 Quick Results with PROC SQL Kirk Paul Lafler Structured Query Language (SQL) is a universal language that allows you to access data stored in relational databases or tables. This h ands-on workshop presents core concepts and features on using PROC SQL to access data stored in relational database tables. Attendees learn how to define, access, and manipulate data from one or more tables using PROC SQL quickly and easily. Numerous examp les are presented on how to construct simple queries, subset data, produce simple and effective output, summarize data with summary functions, and join two tables. proc sql, proc step, where statement, dictionary tables, select, inner join, macro variable, sas library, sas log, macro facility, view, create table, group by, macro call, order by, relational database, right join, data merging source 17
77 Reading and Writing RTF Documents as Data: Automatic Completion of CONSORT Flow Diagrams Arthur L. Carpenter Whenever the results of a randomized clinical trial are reported in scientific journals, the published paper must adhere to the CONSORT (CONsolidated Standards Of Reporting Trials) statement. The statement includes a flow diagram, and the generation of these CONSORT flow diagrams is always problematic, especially when the trial is not the typical two-arm parallel design. Templates of the typical two-arm design flow diagram are generally available as RTF documents, however the completion of the individual fields within the diagram is both time consuming and prone to error. The SAS Macro language was used to read a the RTF template file for the CONSORT flow diagram of choice, fill in the fields using information available to the SAS program, and then rewrite the table as a completed RTF CONSORT flow diagram. This paper describes the process of reading and writing RTF files. macro facility, clinical trial source 7
78 Ready to Become Really Productive Using PROC SQL? Sunil Kumar Gupta Using PROC SQL, can you identify at least four ways to: select and create variables, create macro variables, create or modify table structure, and change table content ? Learn how to apply multiple PROC SQL programming options through task -based examples. This hands -on workshop reviews topics in table access, retrieval, structure and content, as well as creating macro variables. References are provided for key PROC SQL books, relevant webinars, podcasts as well as key SAS technical papers. proc sql, macro variable, proc step, order by source 19
79 Sam and Max's Adventure in SAS Macro Land. (Quick and Easy tips for Macro programming) Maxwell Wise,
Seunghee Chung		 Sam was walking through SAS world one day when she started seeing strange characters all around her. She was in Macro Land! She had been there a few times before but, %SYSFUNC, &SQLOBS, and CALL SYMPUT were all strange and kind of scary. Then she spotted a tall figure leaning against a PROC. He said "Hi, I'm Macro Max. Do you need some help?" When Sam said "Yes," and Macro Max said "Don't be scared, %SYSFUNC is really a nice guy who just wants to help. And &SQLOBS can save you a few lines. Why don't I introduce you to them and some other macro buddies who can make macro coding easier and increase your efficiency?" That day, Sam made many macro friends that make her life much easier. macro variable, do loop, macro loop, call symput, macro function, cpu time, proc step, mlogic, proc sql, %if-%then-%else, sas function, macro definition source 8
80 SAS Data Query and Edit Checks Web 2.0 Sy J. Truong Querying data used to require sophisticated algorithms and programming with SQL expertise or SAS DATA STEP techniques. Now, there are many user-friendly graphical tools to build expressions without having to program code to access relational databases. However, there are few tools for creating queries from a web interface upon SAS data. This paper describes a real world project where data managers who extract data from a relational database and transfer it to SAS data. Data managers need a user-friendly query tool to verify the cleanliness of the resulting SAS data. Since data managers are not SAS programmers, the web interface eases the learning curve allowing them to perform tasks such as: creating compound query expressions, deriving temporary imputed variables, saving and combining saved queries. These are some of the methods described in this paper as it explains how to use SAS DATA STEP, HTML and XML to accomplish an effective user experience. dataset, proc step, proc sql, relational database, sas programmer, clinical data, data cleaning, data step, proc freq source 15
81 SAS Data Step Debugger: Your Liberator from Logic Errors Brandi Rhoads Have you ever written an intricate DATA STEP only to find that when you run the program the results are not as you expect? The SAS log is free of errors meaning you have rid your program of syntax errors; however, you know something is wrong. It must be a frustrating logic error! Using a simple example, this paper will illustrate basic debugger features you can use to find logic errors even in the most complex data steps. These techniques are useful to any programmer of SAS data steps, particularly those without prior knowledge of the DATA STEP debugger. data step, data step debugger, if-then-else source 5
82 SAS Date refresher: cleaning up legacy code using ANYDTDTEw., ANYDTDTMw. and ANYDTDTMEw. informats Laura Kelly Busy SAS programmers often spend too much time dealing with legacy SAS code surrounding the handling and formatting of incoming date data. SAS version 9 introduced some useful data informats -- ANYDTDTEw., ANYDTDTMw. and ANYDTDTTMEw. This is a quick review of how to use these functions to clean up some old- fashioned legacy code that is cumbersome and often a headache to maintain. date variable, sysdate9, dataset source 6
83 SAS DM Statements Unveiled Megha Agarwal SAS programmers usually lose a lot of time (and tem per) when setting up a correct environment before submitting a program. For example, every time you r un a program, you need to close all the datasets th at are created in the program, clear the log and outpu t windows, save the program etc. Then once the prog ram is run, you need to open a specific dataset to chec k the results. In a nutshell, you always follow som e fixed, time-consuming processes, before and after executing the program. Before knowing about the DM (Display Manager), I us ed to say, "if only all this could be done automati cally". Then DM statements came to my rescue. A DM statemen t stands for Display Manager Statements. It submits SAS Program Editor, Log, Procedure Output o r text editor commands as SAS statements. This paper will explain the purpose, functionalities and usage of DM statements using several examples. The intended audience is all levels of SAS users. proc step, sas log source 4
84 SAS Essentials I: SAS Functions for a Better Functioning Community AnnMaria De Mars Employers want work done by experienced programmers. New programmers want to gain experience. Communities want, but often can't afford, more people to analyze data and to explain those analyses in plain language. One way to break out of that vicious circle of can't get a job without experience, can't get experience without a job is to take advantage of the many opportunities to use freely available data to help inform your community. The beauty of it is that using these data sets can provide you the experiences that will advance you from being a novice programmer. This paper uses open data to demonstrate how new SAS programmers can download, analyze and present results from large and complex data sets. In the process, examples are presented of the experiences programmers can gain in making the appropriate design choices and applying a broad range of functions. proc step, macro parameter, macro variable, proc print, libname statement, proc contents, proc sql, %macro parameters, proc format, missing value, proc transpose, sas log, standard deviation, proc freq, sas enterprise guide, numeric variable, where statement, array, data cleaning, sample, proc means, dataset, if-then-else, options statement, macro definition source 15
85 SAS Essentials II: Better-Looking SAS for a Better Community AnnMaria De Mars Experienced programmers don't just write code that runs, they also look professional. That doesn't refer to their designer wardrobe (a quick glance around at your co-workers probably told you that) but to their code, log and output. In this paper, better-looking and better-designed programs are demonstrated using PROC FREQ and macros. SAS system level options are examined for their effectiveness in producing better-looking logs and output. PROC FORMAT, PROC TABULATE, PROC PRINT and ODS are used to create better-looking reports. SAS/GRAPH and Graph-N-Go are used to make good-looking graphs. Now here's the catch - where do you get the time and opportunity to try out new techniques? The examples used here are from projects done for various community service organizations, from sports organizations to public schools. In the end, your program looks good, your output looks good, and your community benefits. proc step, proc format, proc freq, proc tabulate, bar chart, proc print, regression analysis, e., character variable, footnote statement, format statement, numeric variable, proc reg, where statement, sample, libname statement, proc gchart, proc gmap, macro definition source 17
86 SAS Essentials III: Statistics for Hamsters AnnMaria De Mars For next year, I would like to see a workshop offered on statistics so easy a hamster can understand it. Bring your own hamster. - Workshop Attendee. Here it is! No actual hamsters were involved, but the statistics in this session were all previously presented to many classes of middle school students (who sometimes have the attention span of hamsters). The examples use the national dataset and a 1% sample of California residents, data from the American Community Survey, downloaded from the U.S. Census website. Teachers suggested questions of interest to students, including employment, income and education by race and ethnicity. Statistics were produced using SAS. Graphics were created using JMP 8 and SAS 9.2. These results were incorporated as part of a lesson that used SAS output and graphics to illustrate the concepts of frequency distribution, histogram, mean, median, mode and related statistics. sample, proc step, standard deviation, proc freq, proc calis, frequency analysis, normal distribution, central tendency, estimate, proc reg, proc means, proc ttest, proc univariate, histogram, proc sgplot, sas enterprise guide, proc genmod, outlier, univariate analysis source 18
87 SAS Formats and the Format Procedure Christine Riddiough source 68
88 SAS Macro Programming for Beginners Susan J. Slaughter Macro programming is generally considered an advanced topic. But, while macros certainly can be challenging, it is also true that the basic concepts are not difficult to learn. This paper is designed for people who know the basics of SAS programming, but know nothing about SAS macro programming. We explain how the macro processor works, and how to use macros and macro variables. Using these techniques you can create flexible, reusable code that can save you time and effort. macro variable, call symput, proc step, proc print, macro definition, cpu time, dataset, %if-%then-%else, proc sort, sas log, if-then-else, options statement source 12
89 SAS Macros for the Working Statistician William Krebs In the conventional wisdom of the pharmaceutical industry, the SAS Macro facility is regarded as a data processing tool, not as a statistical tool in its own right. In contrast to that perception, this paper presents simple SAS Macros that implement advanced statistical techniques not provided in the SAS System. Examples include Fieller's theorem, errors-in-variables regression, and robust regression methods. regression analysis, macro definition, macro variable, proc step, proc robustreg, proc reg, macro facility, degrees of freedom, macro loop, residual, macro function, pharmaceutical industry, student t distribution, outlier, sample, call symput, confidence interval, proc sql, macro parameter source 9
90 SAS Macros in an Era of Apps Sy J. Truong Software development is a dynamic environment shifting from large enterprise systems to small utilities or "apps". The success of marketplaces such as Apple's App Stores has proven that specialized small apps used for specific purposes are essential for large groups of users with diverse requirements. This paper takes a new view of SAS macros in the framework of mobile apps. SAS Macros can thus be showcased to a community of users such as an App Store and then automate the process of downloading and installing, analogous to mobile apps. Mobile App Stores has been successful in delivering millions of apps since it has made it easy for users to find their app through user reviews, documentation and screenshots. Some even allow users to test drive the apps. The Macro App Store allows users to submit the macro with sample data as a test drive. Once an app is found, it can be instantly installed and used upon a single click. macro definition, proc sql, sas programmer, proc step source 9
91 SAS Procedure Proc Mixed to Assess the Effects of School Health Policy Requirements on Change in Student Health Outcomes Lori Miller The SAS procedure PROC MIXED is a powerful yet flexible program for fitting complex hierarchical linear models. As such, a school effects and a school health policy effects model is generally designed for and statistically analyzed using hierarchically structured data sets in which individuals are nested within naturally occurring hierarchies (e.g., students within schools). However, using the SAS procedure PROC MIXED does not preclude the substantial data processing needed to prepare the data for analysis of a large secondary database that includes a racial/ethnically diverse student population of various socioeconomic levels. It is worth noting that the current thought among highly experienced SAS users is that the creation and integration of dummy variables for categorical variables used within a hierarchical linear model was not necessary and could not be executed using the SAS procedure PROC MIXED. proc mixed, proc step, proc model, missing value, nominal variable, data science, data wrangling, numeric variable, dataset source 9
92 SAS Program as a Backup Tool for an Adaptive Randomization System Yajie Wang The randomization of patients in a clinical trial depends on the continuous availability and normal functioning of the Internet and IVR (Interactive Voice Response) system. Interruptions to the availability of the Internet or the ability of the IVR system to pick up calls from trial sites require prompt action. This is especially troublesome for adaptive randomization because the new treatment assignment is not pre-coded and is based on information from all the previously randomized patients. A backup tool is presented here to allow the continuation of adaptive randomization during interruptions of the Internet or the IVR system. randomization, clinical trial, dataset source 9
93 SAS since 1976 Zubair Shaik In the past four decades SAS software has been extensively used by many businesses for effective decision making. With advancements in research and technology many researchers have proposed different ways of using SAS from decade to decade. It is interesting to explore the trends in research topics for various industries in SUGI/SAS Global Forum conferences since 1976. We apply Text Mining using SAS Enterprise Miner 6.2 to discover trends in the usage of SAS tools in various industries via analyzing all abstracts published in SUGI/SAS Global Forum since 1976. proc freq, data step source 10
94 SAS Style Templates: Always in Fashion Cynthia L. Zender The syntax for style templates changed in SAS 9.2 and became much easier to use. With the new CLASS statement, there is no need to debate the use of the STYLE statement versus the now defunct REPLACE statement. Yes, the REPLACE statement has gone away, and now we have the CLASS statement. In addition, you can import a CSS file into a SAS style template. All of these exciting new changes add up to stylish and fashionable output using ODS style templates. This paper provides an introduction into the use of style templates in SAS 9.2. Methods of determining the correct style elements will be shown along with several concrete examples of making style template changes. The use of the IMPORT statement will also be demonstrated. In addition to these topics, a job aid will be provided that outlines the most commonly used style elements and their attributes. proc step, output delivery system, proc freq, proc report, title statement, ods pdf, proc template, ods html, options statement source 22
95 Sending Emails in SAS to Facilitate Clinical Trial Frank Fan Email has drastically changed our ways of working and communicating. In clinical trial data management, delivering reports to users is part of our daily activities. Most of our colleagues prefer receiving reports by email rather than on the web or in a shared drive. Email is indeed one of the most convenient ways of communication. In this paper, we will present how to use SAS V9 to send reports via email. Before an email can be sent via SAS, the SAS configuration file needs to be modified. After addressing the configuration file, SAS syntax to send an email will be discussed. We will use several examples to illustrate how we can implement such practices. clinical trial, proc step, put statement, sample, file statement, filename statement, data governance source 10
96 Solve Eight Queens Puzzle with SAS Macro Jian Dai We demonstrate how to solve eight queens puzzle by using recursive SAS macro to implement the depth-first search algorithm together with backtracking. macro definition, dataset source 3
97 Statistical Analyses Using SAS Enterprise Guide R. Scott Leslie Conducting statistical analyses involves choosing proper methods, understanding model assumptions and displaying clear results. The latest releases of SAS Enterprise Guide offer conveniences, such as point-and-click wizards and integrated syntax help, to ease the burden on users. This tutorial demonstrates how to perform statistics quickly and easily using some handy features of Enterprise Guide. Examples of multiple linear regression, logistic regression and survival analysis are covered as well as some hints on how to navigate Enterprise Guide menus. This tutorial is intended for SAS users with beginning to intermediate experience with the above mentioned statistics or those with less experience with Enterprise Guide. sas enterprise guide, regression analysis, proc reg, survival analysis, multiple regression, logistic regression, sashelp, data science, logistic distribution, proc step source 11
98 Statistical Graphics for Clinical Research Using ODS Graphics Designer Wei Cheng Statistical graphics play an important role across various stages in clinical resea rch. They help investigators to explore and understand raw data in the early stage of statistical analysis, as well as present final analysis result in the formal publications. The graphs need to be specifically designed and carefully drawn to best represe nt data and analysis. While this can be done by SAS programming using traditional SAS DATA steps and SAS/GRAPH procedures, the process is time consuming and time is spent to find the right options or annotation syntax. With SAS 9.2M3, ODS Graphics Designer (Designer) becomes production software. This is an interactive "point and click" application that can be used to design and create custom graphs. This new application greatly enhances the ability to effectively generate statistical graphs for clinical research. bar chart, vertical bar chart, proc freq, treatment arm, histogram, clinical research, treatment period, clinical trial, scatter plot, sas syntax source 23
99 Tag It, Bag It, Put It Out into Excel Ethan Miller So you have to put data out from SAS to EXCEL, again. You don't want the clients to ignore your output like they did last time. To make your EXCEL output command attention, PROC REPORT and TAGSETS.MSOFFICE2K are your weapons of choice. This paper will illustrate how to create hyperlinks, highlight rows, and conditionally shade cells (traffic lighting). This technique is an easy way to put out data in EXCEL for people who do not use SAS. This paper was written for those with beginning skills; the code was written using SAS version 9.2 on Windows OS. excel, proc report, proc step, xls source 5
100 The Many Uses of SQL Subqueries Tasha Chapman proc sql, where statement, group by source 10
101 The Perfect Marriage: The SAS Output Delivery System (ODS) and Microsoft Office Chevell T. Parker A compatible relationship is one in which the associated entities complement each other's strengths and compensate for each other's weaknesses. This relationship is certainly the case when you combine the power of ODS and the flexibility of Microsoft Office. This paper describes how you can create outstanding spreadsheets via the harmonious union of ODS with Microsoft Office applications. The paper explores the relationships among ODS destinations (for example, ExcelXP and MSOffice2K) as well as custom TAGSETS (for example, the TableEditor tagset). In addition, the paper explains how these components enable you to generate highly presentational spreadsheets. excel, output delivery system, sample, proc step, proc template, mod, ods tagsets.excelxp, proc report source 22
102 The Wonderful Wizard Of ODS - Using The EXCELXP Tagset Coupled With Proc Template, And Proc Report, To Twist Your World From Gray To All The Colors Of The Rainbow Steven C. Black In this paper I provide an easy to follow example using the power of ODS to get out of the gray and white into a world of color using the powerful EXCELXP tagset, TEMPLATE and REPORT procedures. I will show several methods of traffic lighting while providing a wonderful example of an EXCEL document full of colors, borders, and printability, melting away the wicked witches of confusion, bewilderment, and post processing. Enabling each SAS user the ability to lift off on their own without getting caught up by the flying monkeys. excel, proc step, proc report, proc template, output delivery system, proc format, ods excel source 12
103 This is how we do it: teaching SAS in the Cal Poly Statistics Department Rebecca Ottesen Providing students with a strong foundation in SAS programming is extremely valuable for those working towards a degree that involves data management or statistical analysis. This training is useful not only for students who will enter the job force right after graduation, but also for those who intend to pursue a graduate degree. Statisticians and data analysts with SAS programming skills are in high demand, and by preparing students with solid SAS programming, data analysis, and research skills they will be ready to fill real-world positions right after graduation. Our program at Cal Poly San Luis Obispo is one of many university-level programs that help provide such a foundation. Our success lies in our methods of teaching SAS to our undergraduates, and also in the resources we utilize that are available to students and faculty using SAS. sas programming, sas base, data science, sas programmer, sas enterprise guide, proc step, do loop, macro facility, sas output source 12
104 Tips and Techniques for Automating the SAS Add-In for Microsoft Office with Visual Basic for Applications Tim Beese Do you want to run SAS Stored Processes in Microsoft EXCEL and set the prompt values from your worksheet? Do you want to filter your SAS data sets based on the values of specific cells? Do you want to customize your Microsoft Office content with buttons and other controls that allow you to open data, run SAS code and refresh results? This is now possible! The SAS Add-In 4.3 for Microsoft Office adds many new features that allow users to interact with their content through Visual Basic for Applications (VBA). Users can insert and refresh data, stored processes, and reports using VBA. It is also possible to provide prompt values to stored processes, filter and sort strings for data, and control where results are displayed. Combining these features with the existing functionality provided by VBA in Microsoft Office, integration of SAS within Microsoft Office will become easier and more powerful! excel, xls, sample, ods excel source 15
105 Tips and Techniques for the SAS Programmer Helen D. Carey If you are new to programming or even are an experienced programmer, you will benefit by learning tips and techniques that will help you be more productive. This presentation offers tips in programming, efficiency, work habits, and where to find answers to your SAS questions. These tips come from our own experience and from learning from others through their presentations and papers. proc step, sas enterprise guide, proc sql, output delivery system, dataset, macro variable, proc format, macro facility, user-defined format, sample, proc tabulate, sas programmer, proc freq, dictionary tables, macro function, output, proc sort, proc transpose, proc univariate, excel, macro definition source 19
106 Tips and Tricks for SG Procedures and GTL for Clinical Graphs Sanjay N. Matange Did you know that you can create an adverse event graph using a vector plot? Or, that you can label dosage levels for a medications plot using a scatter plot? How do you place a reference line between two values on a category axis? Statistical graphics (SG) procedures and the graph template language (GTL) provide you myriad ways to mix and match statements to create graphs. What you can achieve is based on creative usage of the statements. This presentation includes tips and tricks you can use in SG procedures and GTL programs to build your graphs. We use examples from clinical trials and health and life sciences domains to illustrate the techniques using real- world graphs like LFT panels, patient profiles, adverse event plots, and more. Most examples use the second maintenance release of SAS 9.2, but this presentation also includes a sneak preview of some powerful new features to be released with SAS 9.3. scatter plot, box plot, proc sgplot, adverse events, forest plot source 16
107 Top Ten SAS Sites for Programmers: A Review Kirk Paul Lafler What are the top ten SAS sites for users, beginning with sas.com and jmp.com. We then expand to sasCommunity.org, support.sas.com, and six other popular sites that assist you in training and programming. If you use Google or another search engine to search for content, you will get over a million hits. In this presentation we narrow the list down to ten. bar chart, sas programmer source 5
108 Turbocharging Your Microsoft Office Environment with version 4.3 of the SAS Add-In for Microsoft Office Kevin Bickford Some of the most commonly used tools by users of all skill levels across organizations are Microsoft Office tools, such as Word, EXCEL, and PowerPoint. The SAS Add-In for Microsoft Office turbocharges these tools, giving users the power to explore and analyze information using SAS software within their familiar Microsoft Office environment. The SAS integration with Microsoft Office brings a large set of capabilities enabling users to access reports, stored processes, and dashboards; carry out multidimensional OLAP analysis in spreadsheets with auto-charting; run interactive reports for quick what-if analysis; and securely work with data directly. It brings capabilities allowing users to reutilize the work done, thereby increasing user productivity. This paper highlights the key capabilities provided by the SAS Add-In 4.3 for Microsoft Office and the application of these capabilities across the Microsoft Office products. excel, business intelligence, sas enterprise guide, data science, sensitivity analysis, dataset source 21
109 Understanding the Anatomy of a SAS Deployment: What's in My Server Soup? Connie Robison Did you want to better understand the pieces of a SAS metadata-based deployment and where they go and what they do? This paper highlights the major components of a metadata-based SAS deployment of solutions and business intelligence. Using the concepts of topology and tiers as a framework, this paper illustrates the location of key components and how they can be distributed across machines. sas base, business intelligence, sample, data mining, sas enterprise guide, proc sql, dataset source 18
110 Up To Speed With Categorical Data Analysis Maura E. Stokes Categorical data analysis remains a substantial tool in the practice of statistics, and its techniques continue to evolve with time. This paper reviews some of the basic tenets of categorical data analysis today and describes the newer techniques that have become common practice. The use of exact methods has expanded, including additional assess- ments of statistical hypotheses, conditional logistic regression, and Poisson regression. Bayesian methods are now available for logistic regression and Poisson regression, and graphics are a regular component of many analyses. This paper describes recent techniques and illustrates them with examples that use SAS/STAT software. confidence interval, regression analysis, logistic regression, logistic distribution, sample, proc genmod, poisson regression, likelihood function, predictor variable, binomial distribution, poisson distribution, proc step, bayesian statistics, conditional logistic regression, maximum likelihood estimation, null hypothesis, proc freq, estimate, sample size, clinical trial, exact test, proc logistic, covariance, posterior probability, randomization source 25
111 Use CDISC SDTM as a data middle-tier to streamline your SAS infrastructure Kalyani Chilukuri In many life science companies, SAS is used to streamline biometric processes. Some of these activities include but are not restricted to Edit Checks, study metrics, patient profile, analysis data sets, TLGs for submission or even CDISC conversion. Unfortunately, we have a tendency to reinvent the wheels for each new study and adapt the existing code to match the new study's data structure. This process is often tedious and can lead to errors, as we do not fully understand the assumptions of the modified legacy SAS programs. The convergence of open innovations such as the CDISC SDTM standard and the free SAS-based CDISC Express has brought a better alternative by providing an easy way for sponsor companies and CROs to develop an internal SDTM middle-tier. CDISC SDTM has provided a common data structure for raw clinical data while CDISC Express makes it drastically faster and cheaper to convert any clinical database to the SDTM format using free software sdtm domain, clinical trial, data cleaning, clinical data, excel, data transformation, cdisc source 8
112 Using affinity Models for Buyer Behaviors Matthias Kehder Companies in the early stages of adopting analytics are testing and exploring strategies that produce ROI to justify the capital investment of implementing analytics. Other companies farther down the adoption path have made that investment and found the required ROI early on. By doing so, the early investors created a repertoire of analytical business strategies and their tactical results. It is these companies that are working on the next level of analytics - global optimization at the macro and micro level. Examples are: yearly marketing spend optimized across all channels down to the most logical entity, campaign spend of a charity to reduce funding gaps or increase donations for topics of interest, reducing a bank's risk exposure of each investment instrument on a position basis, or assisting a global company in ensuring that local product development can be adopted world wide. This last topic is the focus of this paper and is addressed using affinity models. logistic regression, response variable, logistic distribution, regression analysis, data cleaning source 2
113 Using PROC CONTENTS and a Macro to Convert Internal Data Values to their Associated Format Values Mary F. O. Rosenbloom,
Edwards Lifesciences		 Working with formatted data can be cumbersome and error-prone. Presented here is a method for using PROC CONTENTS and a macro to generate data-driven code that can be used to effectively convert the numeric internal data values to the character values dictated by the associated format. do loop, character variable source 5
114 Using PROC SQL to Calculate FIRSTOBS David Tabano The power of SAS programming can at times be greatly improved using PROC SQL statements for formatting and manipulating data, and there is considerable literature available on comparing the strengths of using PROC SQL over the DATA STEP. This paper expands on such programming comparisons by reviewing the use of the DATA STEP and PROC SQL to select first observation(s) of a particular incident with a specified date range across repeated measures in datasets. proc sql, proc step, cpu time, repeated measures, relational database, group by, longitudinal data, having, proc compare source 11
115 Using SAS GTL to Visualize Your Data when there is Too Much of it to Visualize Nathaniel Derby In many of the SAS Institute publications about the new ODS statistical graphics, there is an introductory statement that defines an "effective" graph as one that reveals "patterns, differences and uncertainty that are not rea dily apparent in tabular output" (Kuhfeld, 2010; Rodriguez, 2008; Rodriguez and Cartier, 2009 ). Good graphs are also said to "provoke questions that stimulate deeper investigation, and ... add visual clarity and rich content to reports and presentations." Developing a good graph becomes a challenge, however, when input data map to crowded displays with overlapping points or lines. Such is the case with the Framingham Heart Study of 5209 subjects captured in the sashelp.heart data set and a series of 100 cumulative booking curves for the airline industry. In addition, interleaving time series plots can be difficult to interpret, and patterns can be missed when lattice plot panels are charted out-of- order. proc step, scatter plot, histogram, series plot, time series analysis, proc template, proc freq, contour plot, order by, graph template language, missing value, proc sgpanel, proc univariate, proc means, nominal variable, cumulative frequency, line chart, numeric variable, proc sgplot source 22
116 Using Vovici API in SAS 9.2 Richard Koopmann Jr. With the release of SAS 9.2, programmers were able to communicate directly with third-party services via two new procedures (depending on the type of service being accessed): the HTTP procedure for Representational State Transfer (REST) Protocol-based services, and the SOAP procedure for Simple Object Access (SOA) Protocol-based services. Given these tools, SAS can interact with data on remote servers directly. The current paper demonstrates how the SOAP procedure is being employed for more automated ETL processes from Vovici, a third-party survey hosting service, using the SOA Protocol. The returned XML-based survey results, when paired with the XML-based survey definition, are used as input for regular reporting processes. web service, macro call source 4
117 VBA Microsoft Access 2007 Macros to Import Formats and Labels to SAS Maria Melguizo Castro It is a common practice for statisticians and data analysts to import data from Microsoft Access into SAS for analysis. Although it is relatively straightforward to transfer data between the two programs, currently there is not an option to fully transfer descriptions or data formats into SAS. This paper proposes a solution using both Visual Basic for Applications (VBA) and SAS macros to address this issue and create a more complete SAS dataset with descriptions as variable labels and with appropriate data formats. KEYWORDS: Data integration, Macros proc sql, label statement, txt, proc format, dataset, create table, proc step, format statement source 10
118 What Is a SAS Mentor and Why Do I Need One? Stanley E. Fogleman A SAS mentor can be a valuable resource to junior programmers just starting out, or programmers who may have used other high level languages and are just starting to use SAS. The paper will focus on preparing a plan for a hypothetical junior programmer, with the goal of becoming a more proficient programmer over a one or two year period. SAS, unlike some other languages does not lend itself to being self-taught and some things that might look like an obvious choice can actually lead to performance penalties. The most important thing that mentors can provide is guidance - for things worth studying more and things that might be interesting, but you might only use infrequently. sas programmer, output delivery system source 5
119 What's happening if there is "B" in the general linear modeling output? Jason Shilong Kuang The SAS GLM procedure is an efficient tool in statistical data analysis, especially when we have categorical variables (also called class variables) as predictors. In SAS practice, you may see the letter "B" showing up sometimes in the parameter estimate output, and you may wonder what's happening in our model. What is the cause of this? Can we still trust our model? Can we verify the modeling output by some alternative procedures? Where should we put more attention for the similar situations in future? In this paper, we answer those questions by demonstrating some intuitively understandable examples, with the corresponding theoretical statistical background attached. With those in mind, you can easily turn the general linear modeling into a more powerful tool! SAS ROCKS! Keywords: General linear model, estimable function, singular matrix, generalized inverse. proc genmod, proc glm, nominal variable source 5
120 Why Dummy Variable Makes You SMART, and How to Do it SEXY Brian Sun When considering a categorical variable as an independent variable in a regression analysis, should you directly include the categorical variable into your model by using CLASS statement or instead include multiple dummy variables converted from levels of the categorical variable? Theoretically those two approaches are equivalent in terms of estimation results. The CLASS statement offers various possibilities of variable parameterization. However, the availability and power of CLASS statement differs in different SAS regression procedures. So the dummy variable approach is still very appealing for the consistency, flexibility and transparency of variable parameterization. In practice the dummy variable approach also has advantages in terms of ODS statistical graphics output, variable Boolean calculation, and model identification for better fit. However, coding dummies can be very tedious and painful. binary variable, nominal variable, macro variable, regression analysis, proc reg, macro loop, %if-%then-%else, proc genmod, logistic distribution source 12
121 Working the System: Our Best SAS Options Patrick Thornton This paper provides an overview of SAS system options, and discusses our best options-those we have found most beneficial, interesting or both. There are many ways to work the SAS System by changing the settings of one or more of 100s of system options. These settings may have sweeping effects that influence, for example, DATA steps (e.g. REPLACE, MERGENOBY=,), the log (MSGLEVEL=), data sets (e.g. VALIDVARNAME), listing output (e.g. NODATE, NONUMBER, ORIENTATION=, FORMDLIM=), and other destinations (e.g. PDFSECURITY=). Option settings are invaluable for working with format catalogs (e.g. FMTSEARCH=) and they are essential for using, developing and debugging macro programs (e.g. MPRINT, MLOGIC, MPRINT, and SYMBOLGEN, or new to SAS 9.2 MCOMPILE, MCOMPILENOTE). Designed for an intermediate user of SAS, this paper demonstrates our best options. options statement, macro variable, proc step, proc options, macro function source 10
122 WUSS Conference Android App Sy J. Truong As information transitions from hidden databases and obscure networks into mainstream social networks like Twitter, Facebook and YouTube, users leverage the social aspects of communication to make sense out of the deluge of data. Social media can provide extra context and add more meaning when it is recommended from a trusted friend as compared to spam. Attending a WUSS SAS Conference in the past used to involve carrying a thick proceedings and a program book while deciphering what presentation is most relevant and useful for each attendee. As SAS conferences grow and the number of sections and papers become more diverse, this process becomes overwhelming. Since W USS by its own nature is a social event, it lends itself to social networks and mobile apps as it helps individuals make sense and bring context to the presentations. excel, sas programmer source 8
123 You Could Be a SAS Nerd If . . . Kirk Paul Lafler Are you a SAS nerd? Wiktionary, a wiki -based Open Content dictionary, defines "nerd" as a person who has good technical or scientific skills, but is generally introspective or introverted. Another definition is a person who is intelligent but socially and physically awkward. Obviously there are other definitions for "nerd", many of which are associated with derogatory terms or stereotypes. This presentation focuses not on the negative descriptions associated with being a nerd, but on the many positive traits SAS users possess. So let's see how nerdy you actually are using the mostly unscientific, but fun, "Nerd" detector. sas programmer, sas log source 4

Back to Conf go 2012 go 2010

This site serves as a convenient hosting location and does not work as a publisher. All copyrights belong to their respective owners.

The information on this site is provided for general reference purposes only. Whilst every effort is made to ensure that the site is up to date and accurate, no warrant, nor any responsibility or liability for, the accuracy or completeness of the content or for any loss which may arise from reliance on information contained in this site is provided. Links to other sites are provided for your convenience but no responsibility or liability for the content of those sites or of any external site which links to this site is accepted. References to any specific commercial product, process, service, or corporation are also for information only, and do not constitute endorsement, recommendation or favoring.

SAS is a registered trademark of SAS Institute Inc. SAS, Statistical Analysis System, and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.