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BUGSNESUG 1997 Papers - Index
137 papers. Source: NESUG (Northeast SAS Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | %SYSFUNC - The Brave New Macro World | Christopher Yindra | The new macro function %SYSFUNC (SAS rel 6.12) allows access by the macro processor to most DATA STEP functions and several SCL functions. Solutions to many common macro tasks can now be simpler and more efficient. In addition, many of the SCL functions allow the programmer to manipulate external files directly from the macro processor without forcing step boundary conditions with data steps. %SYSFUNC also allows the macro processor to do floating point arithmetic. These features are also supported in the DATA STEP with the SYSFUNC function |
macro variable, macro function, dataset, data step, macro put, macro facility, macro definition, %if-%then-%else | 6 | |
| 2 | A Closer Look At FIRST.var and LAST.var | Marge Scerbo | Understanding how to use the temporary SAS variables, BY recid dosad dosdis; FIRST.var and LAST.var, is straightforward when there is only one BY group defined. But as more BY variables are IF first.dosad then firstad = 1; added, the correct analysis of these temporary variable s RUN; may beco me cloudy and cause their misuse. This paper will step through the creation of a table to demonstrate how Table 3 displays the outcome of this code. Each new date these variables are set and how to use them in a DATA STEP. of admission is highlighted. If this is the intent of the code, |
by group processing, flag variable | 2 | |
| 3 | A Macro for Centralized Ordering, Numbering, and Titling of Tables | Wayne Schuck | In a Pharmaceutical Research Organization (CRO), programming for statistical listings and tables that summarize clinical trials data if often completed prior to complete review by medical writers. At the time of review, table numbers and titles can and do change. A macro program has been developed which, with the appropriate driver program and program modifications, will centralize all table numbers and titles. Tables can run and output to a single file in the order desired to minimize then need for extraneous collation | macro variable, work library | 2 | |
| 4 | A Method for Sorting a Large Data Set with Limited Memory | Leslie LaBrecque | You're asked to do the impossible: perform a sort on a large data set that resides on a server or PC with limited memory. In the case where a more practical approach, such as indexing, is not feasible because data retrieval patterns have not been established, or perhaps a quick look at the data is needed, it is possible to break the sort down into chunks that your system can handle. Writing a small reusable macro which sorts the data chunks and merges the chunks can accomplish this task | macro variable, proc sort, macro definition | 1 | |
| 5 | A Multivariate Analysis of Airline Service Quality | Vasanthakumar Bhat | In this paper, we analyze the association between the customer service quality. It is hard to measure customer financial and operating characteristics of an airline and service in terms of one variable. Several factors affect its service quality using canonical correlation analysis. customer service. Unlike a tangible product, service The service quality variables are denoted by the quality can neither be measured nor inspected before it complaint rates, delays, and involuntary denials of is delivered. Punctuality, schedule reliability, quality of boarding; and operating and financial variables by food, fare, frequency of flights, time of flight, baggage market share, revenue per passenger mile, revenue delivery and cabin service are some factors that growth rate, debt-to-capital ratio, current ratio, load represent customer service. Development of a composite factor, employees per departure and operating revenue indicator to measure customer service is difficult. In this per employee | correlation, proc cancorr, capital ratio, response variable, binary variable, regression analysis, predictor variable | 7 | |
| 6 | A New Adaptive Model for Prediction With the Time Series | Edward Aronov | A new model for prediction has been developed with the standard SAS7 procedures and new original programs. The process is decomposed into typical structural components, such as trend, seasonals, week day and pure random variation. These parameters are estimated by a multistep iterative procedure that minimized the error of residuals. Predictions, derived from the current data and the history data are used with the optimal procedure, that has been developed. Automated calibration and adaptive estimation further reduces the error significantly. Results of the data processing have proven efficacy of this algorithm | sample, time series analysis, regression analysis | 9 | |
| 7 | A Non-Memory Intensive Alternative to PROC MEANS and PROC SUMMARY | Jack Yang | PROC MEANS and PROC SUMMARY offer powerful ways to summarize and report data. However, these procedures have machine dependent memory limitations that become all too apparent when dealing with large data sets. This paper features a powerful but yet easy to implement alternative to PROC MEANS or PROC |
proc step, proc means, proc sort, dataset, class variable, pdv, raw data, sample, by group processing | 4 | |
| 8 | A Self-Organizing Median-Estimation Model | Susan B. Garavaglia | This research combines the basic concepts of self-organizing neural networks with measurement by rank ordering to produce a variant of a one-dimensional self-organizing map that derives the median of a population. The value of the resulting model is that it is memory-efficient and can be applied when observations are presented one at a time as opposed to being available in a complete data set. In this four-layer model, the first layer is a single input node. Then, the second computational layer is a ranking layer, which groups the observations according to the number of nodes allocated. The third layer computes a cumulative distribution and determines which ranking node contains the median value. Ranking nodes compete for the median. The range of any winning ranking node is recomputed to "shrink" closer to the input value which caused it to win, while its two nearest neighbors have their ranges expand to cover the parts of the range left uncovered by the shrinking | cumulative frequency, mae, confidence level, exponential distribution, neural network, outlier, normal distribution, confidence interval, network optimization, monte carlo | 9 | |
| 9 | Accessing a SAS Variable Label: An Example of the Use of Macro Variables | Carl E. Pierchala | In this paper, I give a short overview on the use of SAS macro variables. The latter can be a valuable tool for many SA S programmers. The paper introduces macro variables as a way to ease making routine changes to SAS programs. Then, I illustrate how to use the SYMPUT function in conjunctio n with PROC TRANSPOSE in order to put a SAS data se t internal variable label into a macro variable, thus making the label available to be put into a TITLE statement. To illustrate the method, some random number functions are used t o generate a test data set. A restriction on the use of th e random number functions is coincidentally given. Finally, I close with a tip on macro variable syntax |
macro variable, label statement, proc step, random number functions, variable, proc transpose, histogram | 2 | |
| 10 | All the Data That Fits, We Print | Janet E. Stuelpner | The testing has begun. In order to see if the different parts of your SAS( ) program are working, you need to use PROC PRINT or some other procedure to output the data quite often. You practically have one after each DATA STEP or PROC STEP. What a pain in the neck it is to pull them all out when your testing is done and the program is perfect. Well, now you don't have to do it. A simple little macro will allow you to leave the source code there. Another great use for this macro is to comment out many lines of code where documentation is plentiful. Sometimes it is difficult to remove sections of code with standard comments, where there are comments that exist . Read on to find out an easy way to skip over code with %skip |
proc step, proc print, macro definition | 2 | |
| 11 | An Evaluation of SAS/PH-Clinical Software as a Pharmaceutical Reporting Tool | Phil Giangiulio | batch processing, clinical trial, quality assurance | 11 | ||
| 12 | An Introduction to SAS/GRAPH Software via the GPLOT Procedure | Michael S. Zdeb | proc step, proc gplot, line chart | 9 | ||
| 13 | And All With The Push of a Button! | Ray Pass | macro variable, excel, macro definition, proc step, macro call, dataset | 7 | ||
| 14 | Application of SAS/ACCESS Software & DCL to Simplify Management and Analysis of Clinical Data For In Vitro Diagnostic Medical Devices | Denise Cervelli | Clinical evaluation of in vitro diagnostic analyzers is a critical step in the product development process. The data produced during these evaluations must be carefully managed and analyzed since it is used to substantiate product claims for regulatory submissions and technical support. Dade International Inc. has developed an efficient, cost-effective data management system which meets the needs of users who must rely on the SAS System for reporting and analysis, but who have limited SAS knowledge. CEDAS (Clinical Evaluation Data Access System) provides a user-friendly interface between ORACLE Rdb and the SAS System; VAX DCL was used for the user interface. Modules utilized include (1) SAS/ACCESS, (2) base SAS software, (3) SAS/STAT, and (4) SmartStar ISQL |
regression analysis, sample, data cleaning, data governance, outlier, proc step, clinical data, response variable, dataset | 6 | |
| 15 | Applications of GEE Methodology Using the SAS System | Maura E. Stokes | The analysis of correlated data arising from repeated measurements when the measurements are assumed to be multivariate normal has been studied exten- sively. In many practical problems, however, the normality assumption is not reasonable. When the responses are discrete and correlated, for example, different methodology must be used in the analysis of the data. Generalized Estimating Equations (GEEs) provide a practical method with reasonable statistical efficiency to analyze such data. This paper provides an overview of the use of GEEs in the analysis of correlated data using the SAS System. Emphasis is placed on discrete correlated data, since this is an area of great practical interest | 6 | ||
| 16 | Army Hearing Evaluation Automated Registry System (HEARS) Corporate Data Reporting System - A Customized EIS Solution | Krista Elspas | The Army HEARS Corporate Data Reporting System is a customized enterprise information system operating in a client/server environment, utilizing SAS/AF Frame entries for its user interface. The system is designed to provide the Army Hearing Conservation Program managers and consultants with the ability to easily generate a series of standard and customized reports that utilize corporate data collected from all of the Army hearing conservation programs operating worldwide. Standard reports are generated using base SAS software on an MVS mainframe, and then stored on a Windows NT server for quick access locally. Custom report requests can also be submitted locally from within Windows 95 via SAS/CONNECT for execution on the MVS mainframe using SAS Macros and DATA steps. When a custom report is completed, it can then be downloaded to the requester's PC for viewing and/or printing via the REPORT procedure |
excel, dataset, proc step, proc report, macro variable, %if-%then-%else, proc download, sas base, automatic macro variable, where statement, sysrc, libname statement, macro definition | 9 | |
| 17 | Attribute Smoothing: A Pattern Forecasting Technique | David R. Roberts | correlation, residual, time series analysis, seasonality, proc forecast, proc arima, outlier, proc step | 4 | ||
| 18 | Automate the Process of Concatenating Partitioned Data Set Members on MVS | Winston Lai | Often people must create a master SAS data set by pooling together a number of partitioned data set members in text format with same data structure. It becomes hardly manageable when the number of partitioned data set members to be pooled is large. This paper demonstrates how to use SAS PROC PDS and SAS MACRO facilities to automate this process on MVS operating system |
proc step, macro loop, macro variable, call symput, macro definition | 2 | |
| 19 | Automatically Generate SAS Data from MS ACCESS Tables | Alexa Parliyan | Using dynamic data exchange (DDE), SAS code may now be written to directly retrieve data from Microsoft Access. However, the coder must still know how many and the types of the fields in the MS Access table. A SAS program generator may be used to automatically write the code needed to read these tables, and print the data without having any knowledge of the design of the MS Access tables | infile statement, input statement | 2 | |
| 20 | Automating Creation of Subsets in SAS/FSP Using Pulldown Menus, SCL and Macros | Steve Hinnenkamp | While browsing and updating datasets using SAS/FSP software, users frequently wish to create subsets contain- ing specific records of interest. This may be a cumbersome task, however, if users are unfamiliar with field names and the s yntax requirements of the WHERE command. To sim- plify the creation of subsets, you may use pulldown menus as a front-end to WHERE clause processing. This paper will show a way to automate execution of WHERE com- mands through the use of pulldown menus, SCL and macro commands. Upon completion of the paper, you will have templates you may use to implement your own sub- setting menus |
where statement, character variable, %if-%then-%else, macro definition, macro variable, macro quoting function | 10 | |
| 21 | Bootstrapped Confidence Intervals for Parametric Estimates of Survival Distribution Function | Vadim Pliner | PROC LIFEREG fits parametric models to failure PROC LIFEREG can handle left- or interval- time data that may be right-, left-, or interval- censored data as well. Programs 2 & 3 can be easily censored. Though PROC LIFEREG can estimate modified for the cases of left- or interval-censored survival distribution functions (SDF) if a parametric data, but the part of program 1 that computes and class of distributions is specified, confidence uses Kaplan-Meier (KM) estimates (Kalbfleisch and intervals for SDF values are not computed. PROC Prentice, 1980) cannot, since PROC LIFETEST can LIFEREG allows the following classes of handle only right-censored data. distribution functions: normal, three-parameter gamma (with Weibull and exponential distributions as special cases), and two-parameter logistic, log- logistic, and log-normal. The paper presents some PROC LIFEREG allows the following distributions: SAS code that does two things |
bootstrap, proc step, proc lifereg, logistic distribution, censoring, probability distribution, likelihood function, sample, confidence interval, normal distribution | 8 | |
| 22 | CATALOGER: An Application Development Tool to View, Compare and Document SAS Catalogs and Data Files | Christopher A. Roper, Michael Gilman |
This paper will present an application written entirely in SAS that greatly enhances the developer's ability to develop, compare, and manage SAS catalog entries and datasets. This application has point and click capabilities for printing catalog entries, including images of frame entries and complete descriptions of associated widgets directly into Microsoft Word documents, creating publication quality documentation at the push of a button. The batch printing facility allows for printing of all catalog entries of virtually any type, including Program, Source, SCL, and Slist (including expanded sublists) entries. The text searching facility will search through all text-based entries for character strings and allow point and click access to the locations in any catalog entry containing the text string | catalog entry, catalog, dataset | 6 | |
| 23 | Choosing a Method for Connecting Java to the SAS System across the Internet CGI, JDBC, Socket, or Jconnect? | Larry Hoyle | proc sql, histogram, proc step | 9 | ||
| 24 | Clustering for Market Segmentation | Frederick E. Pratter | proc step, proc freq, proc cluster, proc fastclus, cluster analysis, hierarchical clustering, customer segmentation, sample | 10 | ||
| 25 | CP-Nexus: A Clinical Data Warehouse at Columbia Presbyterian Medical Center | Philip Burrowes | Clinical trialist, healthcare providers, hospital administrators and clinical researchers have a need to access various types of clinical and administrative data. The current multiple sources of clinical and administrative data require extensive programming by information system professionals to extract, integrate and massage. This introduces delays in delivering information to those who need it. There does not exist now at Columbia-Presbyterian Medical Center (CPMC), an integrated data source to facilitate clinical and administrative data analysis. One of the greatest challenges for an organization, especially an academic medical center, is to provide the means for anyone, at every level of the organization, to access the accurate and timely data necessary to support effective decision making, research and process improvement. CP-Nexus is just such an integrated data warehouse that provides a data resource to any application or anyone that needs it | data warehouse, data mart, clinical data, proc sql, sql server, data science | 4 | |
| 26 | Creating a Home Page on the Internet: An HTML Basic Guide | JoAnn R. Matthews | Interest in the Internet and web-based technologies are increasing exponentially. A basic understanding of web-site development and hypertext markup language (HTML) concepts will significantly broaded a SAS programmer's skill base. This paper is designed to be an introduction to homepage development. Whether creating a home page for a user group, a company, or a personal website, the HTML language remains the same. This paper provides a brief overview of the history of the Internet. It also addresses browsers, which are the "window" to the Internet, explains uniform resource locators (URL's), addresses where web documents reside, and describes how to "hyperlink" one web page to another using the URL address. Finally, basic HTML language is described and a template containing basic necessary elements of a home page is provided | ascii, sas programmer | 7 | |
| 27 | Creating a PC-based Information System: An Evolutionary/Revolutionary Visionary Approach (Part II) | Peter Parker, R. Keith Daly |
This paper is intended for former mainframe users who now use PCs for their data processing. It is a continuation of a paper we presented at SUGI 21, which described our switch to the PC platform, the considerations we made, and the problems we encountered. Here's what we've done since then- especially new innovations initiated and problems encountered. The Office of Textiles and Apparel (OTEXA) of the U.S. Department of Commerce has moved all databases and all computer programs from the NIH mainframe computer system onto a PC based system. Details on this transition can be found in our paper in the SUGI 21 proceedings. Since we've developed this PC-based system, we have become more proficient in writing more: - efficient code(using indexes and advanced DATA STEP statements). - elaborate SAS macros and macro windows to automate production and data queries. - elegant applications that tie SAS software programs with other software applications |
data step, macro definition, data wrangling, macro variable, dataset | 4 | |
| 28 | Creating Column Delimited Files with Little or No Effort | John Charles Gober | (This paper base on a presentation given at | proc step, macro variable, proc univariate, proc means, proc rank, macro definition, call symput, macro loop, data science | 7 | |
| 29 | Creating Complex Graphics for Survival Analyses with the SAS System | Steven E. Elkin | It is frequently of interest to evaluate a therapy across several studies (e.g., meta-analysis) or across several subgroups within a study (e.g. studies with great patient heterogeneity like cancer trials). Comprehensively displaying data from such analyses in a succinct fashion can be difficult; two types of unique graphics are presented here with the SAS system. Simulated clinical trial data from an oncology study is analyzed by stratified Log-Rank and standard survival analyses with the PHREGi and LIFETESTii procedures. The primary graph (see figure 1) includes a plot of hazard ratios and their 95% confidence intervals on a logarithmic scale, observed minus expected values and their variance, event and patient frequencies, stratification categories, and relevant p-values. The Annotate facility and the GSLIDE iii procedure from SAS/GRAPH software enables the presentation of all statistical data to appear along side of the corresponding plot on a single page |
hazard ratio, proc step, character variable, data step, sample, bar chart, survival analysis, confidence interval | 7 | |
| 30 | Creating External Files Using SAS Software | Clinton S. Rickards | This paper will review the techniques for creating external files for use with other software. This PROC- ess is the flip-side of reading external files. We will review the business need, statements for pointing to and creating the file, and common options. A number of specific applications will be used to illustrate the various techniques |
data science, flat file | 8 | |
| 31 | Creating Tables with Summary Statistics When PROC Tabulate is Insufficient | David Collins | and the solution that was developed using In Pharmaceutical research, it is often desirable TRANSPOSE, PROC SORT and PROC to show subject values and summary statistics PRINT. on specified parameters across study periods in tabular form. PROC TABULATE gives some DESCRIPTION OF PROBLEM |
proc step, proc tabulate, proc means, proc transpose, proc sort | 4 | |
| 32 | Creating Test Data for an American Community Survey Edit | Adeline J. Wilcox | dataset, variable list | 2 | ||
| 33 | Creative Use of SAS Outputs | Vincent Law | Most SAS outputs use fixed syntax. We can take advantage of this fixed syntax to quickly locate information from the outputs. With the help of text processing languages, we can apply this technique in various ways | txt, sas output, proc contents | 3 | |
| 34 | Data Mining at IMS America: How We Turned a Mountain of Data into a Few Information-rich Molehills | Paul Kallukaran | IMS America, a division of Cognizant Corporation, is the principal source of information used in marketing and sales management by health care organizations throughout the United States. Of the various potential applications of neural networks, pattern recognition is considered one of major importance. This paper presents the results of using neural networks to classify time-series data into several trend pattern classifications [e.g., Increasing Trend, Decreasing Trend, Shift Up, Shift Down, Spike Up, Spike Down, and No Pattern] , and the information generated from the classifier is used to detect various marketing related phenomena [e.g. , Brand Switching, Brand Loyalty, and Product Trends]. The data used for the test consisted of prescription data for 12 months , for 600,000 prescribers writing four drugs in the Anti -ulcer market | neural network, network optimization, dataset, time series analysis, proc sql | 8 | |
| 35 | Data Step vs. PROC SQL in a Many-to-Many Match-Merge | Douglas L. McAllaster | proc sql, proc step, by group processing, do loop | 6 | ||
| 36 | Delivering Geographic Information: For Those Who Can't Read a GMAP and Won't Stop to Ask for Directions | Jeanne M. Spicer | Many have lost their way on the road to spatial visualization. This tutorial plots a course through the steps necessary to map US data by state, county, and other levels. The route followed is to generate the basic map with SAS/ASSIST, getting as close to your final destination as possible. Then the finishing touches are added by modifying the code ASSIST generates. Common roadblocks are discussed including: hitting potholes in getting your response data ready to map and what to do when you get pulled over by the GDEVICE squad. The paper takes a detour through other PROCS that are often used with GMAP to demonstrate combining county boundaries with GREMOVE and customizing a map with ANNOTATE data sets. Also included are side- trips though the uncharted territory of using boundary files that are not part of the MAP LIBRARY and the new MAP object in SAS/AF. The paper concludes with a pit stop to discuss whether or not to tow the whole thing over to SAS/GIS |
proc step, proc gmap, sample | 5 | |
| 37 | Digging For Gold: Business Usage for Data Mining | Kimberly A. Foster | Current trends in data mining allow the business community to take advantage of sophisticated analytical techniques to assess future directions and manage strategic planning. Yet, these tools and techniques are not being used to their full capacity by business managers to solve every day business problems. Why? Perhaps, this can be attributed to an underlying fear of complex mathematical and statistical methods found within data mining and neural network models. The purpose of this paper is to demystify the art of data mining by outlining practical examples of usage applicable to business managers and professionals. By focusing on the competitive advantage that can be obtained with data mining, the author hopes to provide a better understanding of the practical application of this type of data analysis | data mining, proc hpsplit | 6 | |
| 38 | Disk I/O Considerations for SAS Processing | John Hall | An understanding of the operation of Disk Input/Output (I/O) can be fundamental to a successful SAS processing environment. This paper explores various configuration options that can be used when designing a SAS processing server. The particular implementation discussed is a Hewlett-Packard server using the HP-UX operating system. We begin with a discussion of file systems implementations and explore attributes and benefits of each. We next focus on considerations for SAS server processing, including partitioning and striping. Real world examples are referenced to demonstrate our experiences in these areas. General Concepts It is necessary to understand some fundamental concepts to effectively use the various options available in creating and using file systems in HP-UX. Because the SAS system utilizes the underlying file system of the host OS, an understanding of the file system implementation on HP-UX becomes important in configuring a SAS server environment | dataset, cpu time | 4 | |
| 39 | Drill Down Till You Drop: OLAP Without Windows | Ralph Winters | On-Line Analytic Processing, using Drill Down Techniques has become enormously popular in recent years using GUI front-end programs. Do you know that you have always had the capability to perform standard drill downs using existing tools such as PROC |
proc means, proc step, class variable, proc print, sample, where statement, proc sql, out=, variable list | 10 | |
| 40 | Efficiency Ideas for Large Files | Meimei Ma | proc step, dataset, numeric variable, proc sql | 8 | ||
| 41 | Eliminating Tedium by Building Applications That Use SQL Generated SAS Code Segments | Dave Mabey | When SAS applications are driven by data-generated code segments, the programmer or operator is freed from tedious code modifications. SAS/SQL now adds powerful ways of generating these segments as simple macrovariables, macrovariable "arrays", and macrovariables that contain delimited strings up to 32K long. This paper will discuss three.busness needs and propose solutions that SQL in conjunction with other code generation techniques: 1. A macro that generates a MS/EXCEL friendly ".txt" file (tab delimited) from any SAS dataset. (%FLATFILE revisited) This solution is driven by the metadata in DICTIONARY.COLUMNS. 2. A macro that generates "dummy variables" with labels. This solution is driven by the data in the dataset or a user written format. 3. An application that does a full outer join on two or more datasets that have the same structure but different data. This solution can be either table or Frame driven |
proc sql, proc step, macro definition, macro variable | 6 | |
| 42 | Enhancing SAS/GRAPH Software Output | George P. Sharrard | This paper is a series tips and techniques for beginning SAS programmers. There is nothing presented here that cannot be found in one or more of the SAS manuals on your bookshelf. I selected these 7 topics because I use one or more on a daily basis. These are real world problem solvers. I. Process only what you need. Avoid reading the same data twice Consider the following code: data new ; set old ; tenure = date() - start_dt ; run ; You take it for granted that as each record is read from the OLD data set the variable TENURE will be calculated and then the record will be written to the NEW data set. There is no need to explicitly tell your program to write each record to NEW; SAS assumes that is what you want to do and does it | macro variable, proc step, proc sort, proc sql | 5 | |
| 43 | Estimation of Direct, Period and Carryover Effects in Crossover Studies | Mitchell Kotler | While the crossover design has been criticized in recent years, it remains a common method of testing differences among treatments with greater power than the simple parallel group design. We consider studies in which carryover (previous treatment) and period (time slot in which treatment was given) are effects on the response. The purpose of this paper is to describe procedures for analyzing data from such a crossover study (no missing data) using SAS . Specifically, PROC GLM in SAS/STAT is used, with ESTIMATE and LSMEANS statements to generate appropriate estimates. The assumptions underlying the analysis and the meaning of the estimates will be discussed. We also discuss alternative designs administering a placebo in the study |
proc glm, proc genmod, proc step, degrees of freedom, least squares mean, crossover study, fixed effects, anova | 7 | |
| 44 | Estimation of Sample Selection Models with SAS/IML Software | Charles B. Hallahan | sample, likelihood function, maximum likelihood estimation, regression analysis, proc calis | 9 | ||
| 45 | Evaluation of Client/Server Configurations for Analytic Processing | Barry R. Cohen | I participated in a project to design and performance test a statistician's work station in a client/server environment. The mix included Oracle on HP/UNIX servers as the data warehouse, SAS Software for analytic processing, and both the HP/UNIX servers and Windows PC clients as candidate sites for the SAS program execution and data serving. The performance test results were presented in my SUGI21 paper. Much time was required in that paper to detail the hardware and software involved, and to simply present the performance results. Little time was available for critical analysis of the findings. I now build upon the information in that paper, taking a critical look at what the performance numbers tell us about setting up a client/server environment for data analysis with SAS Software | dataset, traffic data, data science | 10 | |
| 46 | Exploiting SAS Software Using JAVA Technology | Barbara Walters | This paper describes how to use Java technology with SAS software. SAS Institute currently offers several Java development tools that allow applications to access SAS data and take advantage of SAS computational capabilities. This paper describes how to use these class libraries and address client/server configuration and performance issues | 5 | ||
| 47 | Exploring Multi-Dimensional Relationships with SAS/GRAPH Software | Gail F. Kirk | When an outcome variable cannot be measure d reliably, substitution of related variables for dat a analysis is often necessary. Graphs help us to evaluate proxies and to understand the relationships among the surrogates and the outcome measure. This poster will show a graphing technique that we have used t o investigate possible relationships. In a recent clinical trial four laboratory tests an d measurements of an outcome variable X were obtained at baseline and follow-up. This outcome measure X is of questionable reliability. It is thought that changes in the laboratory test values may reflect changes in X . The strength of the relationships among the variable s requir ed assessment. We will show how we put fou r graphs and a table of background information on a single page by using the GPLOT and GSLID E procedures, an Annotate data set, the GREPLA Y procedure and a 5-panel template |
response variable, proc calis | 7 | |
| 48 | FREQ-OUT: An Applied Presentation of the Options and Output of the FREQ Procedure | Pamela Landsman | Have you ever been told "compare the rate of death by gender", or "give me the p-value for the difference between these 2 percentages", or "is there a trend in this association", or even "check the p-value that's reported in this paper for this 2x2 table"? You know you need PROC FREQ, but how do you get the data in? Which statistic should you report; SAS presents 4 p-values just by running the procedure with the CHISQ option for a simple 2x2 table. This presentation should put you on the right path. Guidelines for when to use the different tests of significance and interpretation of the output from PROC FREQ will be presented. Designed for the non- statistician, this presentation will teach you about different data structures (e.g., binary versus ordinal data); how to determine what the investigator really wants (e.g., when to run a stratified analysis and when to use the BY statement); and which p-value to report |
proc step, proc freq, likelihood function, fisher exact test, proc format, exact test, missing value | 6 | |
| 49 | From 50,000,000 Claims to One Analytical File | Louise S. Hadden, Mike Murphy, Alan J. White |
Researchers often confront the fact that the most common repositories of data are the least suitable for careful analysis. This is an outline of a small-area study performed by Abt Associates Inc. under one of its health care study contracts, during which a small mountain of data was turned into information that may be useful to our customer. This paper illustrates the power of the SAS System for processing a great quantity of disparate data, its statistical analytical capability to make use of this information, and its flexibility for producing quality tables and graphs for presentation. Some of the examples will be specific to the UNIX operating system. Base SAS, SAS/FSP , SAS/GRAPH , and SAS/STAT are used in this application. For the purpose of confidentiality, the MSA- level statistics shown in each exhibit are fictitious |
proc step, regression analysis, format statement, proc gmap, proc sort, insurance claims, sample, standard deviation | 6 | |
| 50 | From Surfing-to-Jet Skiing: A Comparison of SAS Web Integration Tools Before and After SAS/IntrNet Software | Eric Lefebvre | This paper compares and contrasts the tools and techniques for integrating SAS and the Web from third parties and the SAS Institute. Comparisons are made between techniques used prior to the beta release of SAS/INTRNET , specifically the use of Common Gateway Interface (CGI) using the Practical Extraction and Reporting Language |
macro variable, data step | 6 | |
| 51 | Fundamentals and Flourishes to the TABULATE Procedure | Dianne Louise Rhodes | proc step, proc tabulate, class variable, proc means | 7 | ||
| 52 | Generating a Table of Contents for a Format Library | Craig Dickstein | A feature lacking in PROC FORMAT is the ability to produce a listing of just the members available and some metadata about them. The FMTLIB option will generate reams of output when all you may be after is a listing of the available formats to remind you of their presence, type, or spelling. A simple but elegant routine is presented to produce just this kind of listing. By extension, the technique presented here can be used in a variety of similar situations |
proc step, proc format, proc printto | 2 | |
| 53 | Gentle Introduction to Information Theoretic Model Selection Criteria and Their Application in Clinical Trials | Xi Chen | Landscape data files refer to those created by spreadsheet programs when the rows are used for variables and the columns for records. The landscape data structure is very useful in clinical studies where instruments administered to evaluate subjects can each contain hundreds of questions. However, reading the landscape data files into SAS to make the SAS dataset is not straightforward because the SAS dataset is in a portrait arrangement where the rows are records and the columns are variables. The process of converting the landscape data files to the portrait SAS dataset involves the use of SAS DATA STEP and PROC TRANSPOSE. This tutorial will review these methods. The focus will be on how to handle the landscape data files with mixed data types using logical control, techniques of splitting and combining data files, INPUT function, etc., how to control data reading errors, as well as how to organize the code efficiently through using the SAS Macro Language |
dataset, proc transpose, proc step, error message, out=, input statement, numeric variable | 4 | |
| 54 | Graphically Enhancing Univariate Descriptive Statistics | James R. O'Hearn | The problem of analyzing univariate data is investigated. In this paper an enhanced graphical approach based on a template of numerical summary statistics and graphical plots using SAS/GRAPH , SAS/QC , and SAS/STAT software is presented. It is shown that this template provides a more powerful tool for assessing statistical assumptions, for suggesting corrective actions when assumptions are not met, and for elucidating patterns and relationships than that of the UNIVARIATE procedure. It is anticipated that this template will be useful for the data analyst for gaining greater insight into the structure of univariate data. An example using fictitious exercise stress test data will be given to illustrate this methodology |
histogram, box plot, proc means, proc univariate, data science, macro variable | 3 | |
| 55 | Hot-Deck Imputation with SAS Arrays and Macros for Large Surveys | John Stiller | SAS arrays and macros are used to implement a hot-deck class variables may be specified; however, two or three imputation routine to fill in missing survey data for all housing dimensions are used in most cases. The sixth dimension is items of a large demographic survey, the American reserved for storing hot-deck values and imputation counts for Community Survey (ACS). This paper describes the use of a each cell (see Figure 1). In our system, the hot-deck has a DATA STEP with multi-dimensional arrays and macros to perform depth of four values. Storing four donors per cell prevents the imputation on a set of variables simultaneously. This reuse of the same donor in cases where consecutive differs from other approaches which sort the data by classing occurrences of missing are present in the data. This is an variables and use data steps with RETAIN statements to issue for us in the ACS, since our item non-response rates impute for each variable, one at a time |
imputation, class variable, data step, missing value, proc step, macro definition | 6 | |
| 56 | Hot-Deck Imputation: A Simple DATA Step Approach | Lawrence W. Altmayer | imputation, macro variable, sample, macro loop, proc step, call symput | 6 | ||
| 57 | How Old Are You? | Jack N. Shoemaker | date variable, sysdate9 | 2 | ||
| 58 | How to Convert a SAS Data Set to a Quicken QIF Format File | Helen-Jean Talbott | Registration activities for the NorthEast SAS Users Group (NESUG) annual conference make use of Quicken and SAS software on personal computers. Quicken software is very useful for managing financial data for home and small business applications. SAS/FSP and Base SAS software are very effective for validated data entry, data management, and report production. The difficulty lies in transferring data from SAS data sets to Quicken files. In order to import transaction data from software other than Quicken, the data must be available as an ASCII file in Quicken Interchange Format (QIF). The QIF file format is not one of the standard data conversion formats for SAS software at this time. This paper demonstrates how to write SAS program code to create an ASCII file in QIF file format from a SAS data set |
dataset, sas base | 3 | |
| 59 | How To Generate Complex Tables Using SAS Report Writing Tools | Michael Bretz | proc step, proc tabulate | 6 | ||
| 60 | How to Plot a Line Chart with a Standard Error | Hany Aboutaleb | No single statistical tool is as powerful as a graph. Graphs can display a large amount of information, reveal patterns and relationships, support hypothesis, and picture new ideas. The SAS system and SAS/GRAPH software provide basic tools for statistical analysis and graphical display. This article will describe and illustrate how to plot a line chart with a standard error and how to produce custom legends using the annotate data set |
proc calis, line chart, proc step | 3 | |
| 61 | Implementation and Comparison of the Back Propagation Neural Network in SAS Software | John S. DaPonte | This paper describes how to implement the Backpropagation Neural Network, using existing SAS Procedures to classify storm and non-storm regions of interest from remote sensed cloud images collected at the Goddard Institute for Space Studies. In this study, square regions of interest from satellite images were supplied and a number of quantitative features were computed for each of those regions using SAS. These features and their classifications were then input both into SAS statistical discriminant analysis procedures and into neural network macros suggested by the SAS Institute. Both the Nearest Neighbor Algorithm and the Backpropagation Neural Network were used so that their classification results could be compared. The major advantage of having both pattern recognition methods available in a single software package was that consistent preprocessing of image features and random selection of training and testing data sets could be accomplished | neural network, cohen's kappa, proc step, training set, discriminant analysis, test set, proc freq, standard deviation, accuracy, stepwise selection, regression analysis | 10 | |
| 62 | Information as a Unifying Measure of Fit in SAS Statistical Modeling Procedures | Ernest S. Shtatland | This paper shows that there is a "common denominator " for many goodness-of-fit criteria in such popular statistical modeling procedures as REG, LOGISTIC, GENMOD, PHREG and others, and that the most natural "common denominator " is an information-based measure. The necessity to have some common, universal and easy to interpret criterion is almost obvious. PROC REG has eleven criteria of fit, LOGISTIC has four, GENMOD - nine, and PHREG - three. Some of these criteria look alike, but others look absolutely unrelated. With this abundance of criteria, the questions of which one to use, why and how to interpret the criterion of our choice are far from being trivial |
proc step, likelihood function, logistic distribution, regression analysis, proc logistic, proc reg, proc genmod, significance level, correlation, sample, predictor variable, proc cancorr | 7 | |
| 63 | Inter-Platform Communication Using SAS/Connect Software: A Method To Moderate Remote Processing | Christopher Zogby | SAS software can be used to create client / server applications. Distributing an application's tasks has many benefits, however communication between platforms is critical to maintain the application's elegance and efficiency. The outcome of a process running on the remote host may restrict the proceeding number of logical client requests. SAS/CONNECT software ships with the macro, %SYSRPUT, which is capable of passing information to the client (local host) from the server (remote host). This poster demonstrates how to use the %SYSRPUT macro to pass information from the remote to the local host. An additional example is presented to show how %SYSRPUT can be used to establish a feedback regulated loop to moderate client / server requests. All examples are presented in SAS macro code and SAS screen control language (SCL) |
macro definition, libname statement, %if-%then-%else | 2 | |
| 64 | Introduction to Arrays | Bob Virgile | 5 | |||
| 65 | Iterative Code Development: Two Testing Techniques | Craig Dickstein | In the iterative development method there needs to be frequent testing and data visualization. Once the routine is ready, the code should run without the laborious step of stripping out all the testing and data visualization modules. This paper addresses these issues via two independent techniques. First, a simple macro will be shown that aids in the quick visualization of data. Second, a simple but elegant use of macro variables is shown to increase programmer productivity when developing and executing code. These techniques are sure to help the reader conceptualize additional testing techniques | macro variable, proc step | 2 | |
| 66 | Macro to Create a Comma Delimited Extract File | Thomas Garvin | This macro is used to create a comma delimited extract file. It is designed to create an external PC readable file from any SAS/G01 data set. There is no need to know the name, type or order of the variables to be extracted. This macro was adapted from an example printed in the 1QTR95 issue of SAS Observations. (Pages 77-78) |
put statement, macro variable, macro loop, numeric variable, do loop, %if-%then-%else, character variable, macro definition, if-then-else | 3 | |
| 67 | Macro to Determine the Significance of the Main Effects of Two Categorical Factors for the PHREG Procedure | Robert J. Gallop | This poster presents a macro to determine the main effects of two categorical factors in a Two-Factor ANOVA/ANCOVA linear model with a predetermined link function, where the PHREG requires dummy variable coding for each categorical factor. One of the major problems in using a procedure with dummy variable coding is the analysis of the main effects of each categorical variable can not be immediately determined. All estimates are compared to the default category of the dummy variable coding. Analysis of the main effects requires use of the TEST statement, for which coding gets more complicated with the increase in levels of each factor. This macro will allow you to enter: the categorical factor names, the size of each, and the other independent variables. The macro will create the dummy variables for each factor and the interaction between the two factors and generate the results of the test that the main effects for each categorical factor are equal to 0 | main effect, binary variable, hazard function, proc phreg, proc genmod, hypothesis testing | 9 | |
| 68 | Macros from Beginning to Mend - A Simple and Practical Approach to the SAS Macro Facility | Michael G. Sadof | The macro facility is an important feature of the SAS Language. Mastering this facility will enable the novice user to become an accomplished programmer. This tutorial session will use a building block | macro definition, macro variable, macro facility, macro loop, macro put, title statement, global symbol table, %if-%then-%else, put statement, symbol table, proc step, dataset | 7 | |
| 69 | Managing a Help Desk Contacts Database Using the WWW and SAS Software | Mary-Elizabeth Eddlestone | Web-based forms can be an substitute for paper forms used to collect data. For example, every time a contact was made through our consulting services at Cornell, a note was made of the person's affiliation, the type of contact, and the topic(s) discussed. Historically this information was recorded on paper and then ta llied by hand at the end of each month, quarter and fiscal year: a painstaking, labor-intensive process begging for automation. This paper w ill illustrate how the WWW in conjunction with SAS became an elegant, useful and efficient alternative to the common tasks of collecting data and in the process streamlined reporting. The form- based Web pages and SAS programs designed for our consulting database will be used as an illustration | sas output, proc step, data cleaning, proc freq, data step, macro conditional, macro definition | 10 | |
| 70 | Managing SAS Data Resources in the Display Manager Environment | Larry Gertzog | proc step, data governance, dataset, proc contents, libname statement, catalog | 4 | ||
| 71 | Mining Your Data for Health Care Quality Improvement | Greg Rogers | Quality improvement in the health care industry can best be defined by examining the driving forces that are effecting the industry. The evolution of the health care in this country is driven by a single purpose. How can health care organizations lower cost, raise quality, and still remain competitive? The implementation of patient-focused quality improvement programs are at or near the top of the list for both information technology (IT) and business unit managers alike in their efforts to lower cost. For IT managers, the key benefit SAS software brings to the table an environment of seamless integration of tools, data, and solutions across the enterprise. Data is at the heart of any patient-focused quality improvement program. The SAS System's access methods and data warehousing capabilities give IT managers what they need to implement the infrastructure necessary for a successful quality improvement and data mining solution | data mining, neural network, proc hpsplit, sample, data governance | 7 | |
| 72 | Missing Value Imputation Methods for Large Databases | Leonardo Auslender | imputation, missing value, proc calis, predictor variable, mean imputation, standard deviation, quality control | 8 | ||
| 73 | Nonlinear Mixed Effects Models, a tool for analyzing repeated-measurements data whose function is nonlinear in the parameters. | Suzette Blanchard | macro loop, %if-%then-%else, proc mixed, macro definition, likelihood function, proc step, proc print, covariance, predictor variable, maximum likelihood estimation, response variable | 7 | ||
| 74 | One Bar Chart, Two Variables, Three Axes | Anne Horney | It is often useful to show two variables with differen t ranges of values on the same bar chart. For a study of hypertension we needed a bar chart showing both systolic and diastolic blood pressure on differen t scales and grouped within antihypertensive treatment regim ens. This was a problem since bar chart s produced by GCHART have only one response axis. We will show how the Annotate facility can be use d with GCHART to produce a vertical bar chart with a left and a right axis. For our study the mean systolic blood pressure was to be plot ted against the left vertical axis and th e diastolic against the right axis. We also wanted each bar labeled with its mean and 95% confidenc e interval. Systolic values were plotted in the usua l manner with the axis ranging from 130 to 154 | bar chart, confidence interval, proc calis | 5 | |
| 75 | Optimization Techniques for Processing Large Files in an OpenVMS Environment | Adrien Ndikumwami | Despite the recent technological advances being made in the computer industry, both in terms of availability of enhanced processing power and large storage devices, the demand for these resources is also increasing considerably. Unlike in the past, large data files spanning several gigabytes are now being routinely processed for generating regular and ad hoc reports. Such regular use of large files is forcing programmers to look for new techniques that enable optimal use of scarce computer resources. While the SAS system provides advanced capabilities to handle and manipulate large files, an appreciation of the host operating system by the programmer is essential to efficiently use the scarce computer resources. For large data files, the way data are read, processed, and stored can make a lot of difference in the optimal utilization of resources. This paper examines the issues that a SAS programmer needs to address while processing large files in an OpenVMS environment | elapsed time, work library, dataset, length statement, cpu time, libname statement | 5 | |
| 76 | Paper-less Reporting: Online Data Review and Analysis Using SAS/PH-Clinical Software | Eileen Ching | The paper presents the applications of SAS/PH- Clinical in clinical trials reporting and analysis, and describes how clinical researchers can utilize its capabilities to analyze and explore the information with timeliness and efficiency during the drug development process. Since it is essential to extract useful information from the vast amount of data collected, statistical report generation is a representative and usually time-consuming activity in the world of clinical research and development. SAS/PH-Clinical, a data review and analysis tool developed by SAS Institute, provides the functionalities of a data browser, a query builder, as well as a flexible reporting system that is capable of creating templates which users can invoke and customize in a visual interface environment. Instead of receiving and reviewing reports printed on stacks of paper, the clinical scientists can view them on-line within the same system that is used for the generation of those displays |
clinical trial, raw data | 5 | |
| 77 | Portable Data Process | Denis Michel | In the past, we wrote SAS programs and printed the output for users. Advances in technology and user sophistication have changed the way we operate. In the pharmaceutical industry, we now transfer data and programs among global affiliates, other companies (e.g. contract research organizations) and regulatory agencies (e.g. FDA). This paper presents "portable data processing" as it applies to providing users with data and programs. Issues discussed are SAS transport files, transferring and converting SAS files to other formats, documentation of data contents, and writing programs for portability. SAS code is provided to illustrate the various concepts of portable data processing | dataset, proc step, libname statement, txt, user-defined format, character variable, proc contents, time variable, macro definition, macro variable, %if-%then-%else | 7 | |
| 78 | Preparing Final Reports Using DDE to Link SAS Software and Microsoft WORD | Dean Bross | The Department of Veterans Affairs conducts surveys of its patients to measure how satisfied they are with the medical care provided by VA medical centers. The data is assembled in a central location and analyzed using the SAS software. It is important to provide each medical center with individualized feedback so that facility level managers can assess their own performance and identify opportunities for improvement. The effectiveness of information feedback can be maximized if the data is provided in an attractively laid out document such as those produced by word processing software. This presentation will focus on the detailed techniques that can be applied to transfer SAS generated analyses into a Microsoft Word document. A type of linkage known as DDE can be used to allow the SAS system to control Microsoft Word, and nearly all functions available in that software package are accessible through this link | filename statement, proc means | 10 | |
| 79 | PROC SQL - What Does It Offer Traditional SAS Programming? | H. Ian Whitlock | PROC SQL plays two important roles in SAS: 1. To connect SAS with other data base systems 2. To help the typical SAS data processing programmer This paper looks at the second role. SAS program- ming problems are presented, where PROC SQL has a distinct advantage over SAS programs that do not use this procedure. By focusing on such problems with coded solutions I hope to answer the question, "When is it most appropriate to consider using PROC SQL in a SAS program?" |
proc sql, proc step, group by, create table, order by, sas programmer, macro variable, macro loop, macro definition, %if-%then-%else | 6 | |
| 80 | PROC SUMMARY and PROC FORMAT: A Winning Combination | Alan Dickson | proc step, proc means, proc print, proc format, class variable | 7 | ||
| 81 | PROC TABULATE: Controlling Table Appearance | August V. Treff | PROC TABULATE provides one, two, and three dimensional tables. Tables may be constructed from class variables, analysis variables, and statistics keywords. Descriptive statistics can be displayed in table format with a minimum of code. This paper will use a five-response survey to demonstrate how PROC TABULATE features translate to a printed report. We will examine table dimensions, crossing and concatenating variables, and percent calculations. Controlling table appearance through formats and customized column headings also will be discussed. Many programmers do not use PROC TABULATE because they find that there is too much to learn about the procedure. PROC TABULATE has many more features than other procedures. We will examine the effect that these features have on the report display |
proc tabulate, proc step, proc means, class variable, label statement, proc freq | 11 | |
| 82 | Propagating Observations in a Batch Environment | Michael J. Marzano | The SAS DATA STEP allows you to perform some powerful functions with data sets including combining, concatenating, interleaving, and match-merging. But the one feature it does not allow for is "propagating observations", or expanding the observations in a data set by some given hierarchy. This feature can be especially useful in applications that require the creation of transactions that will subsequently update a database |
sas base, macro definition | 2 | |
| 83 | Providing Users with Access to the SAS Data Warehouse: A Discussion of Three Methods Employed and Supported | Cynthia A. Stetz | A Data Warehouse is stored in SAS datasets on a server running a UNIX operating system. The user community is heterogeneous, containing power SAS System users, casual SAS System users, and non-SAS users. Supporting them all at their own 'comfort level' requires the deployment of different access methods. Fortunately, with the SAS system, we have a variety of tools available for data access. This paper will discuss three access methods we have in use and are actively supporting, the pros and cons of each, and how best you can help your users choose the best method for their needs (which may mean choosing one for them!) | data warehouse, sample, dataset, proc step | 6 | |
| 84 | Rankings and Multi-Criteria Evaluation of Alternatives: Usage of Objective Data, Subjective Measurements and Expert Judgement | Pavel Brusilovskiy | There are many problems that require full utilization of all available and useful information in order to achieve a comprehensive and systemic evaluation of alternatives. These sources are objective data, subjective measure- ments (i.e. survey), and expert judgment. Each type of information is reflected in the ordinal scale as an indi- vidual ranking (or rating) of the alternatives. Two different cases are considered: a) all individual rankings are treated equally important; b) each individual ranking has its own ranked weights. In the latter case the weights reflect in corresponding ordinal scale of a decision maker's preferences (confidence, reliability and/or importance) of the different types of information ob- tained. The paper is dedicated to better management decisions through the aggregation of different types of information | correlation, interval variable, proc step, proc rank | 7 | |
| 85 | Retrievals from DB2 BLOB (Binary Large Objects) Data Warehouses Using SAS Software | Tracy L. Lord | This paper describes a method of retrieving data from a DB2 BLOB (Binary Large Object) data warehouse using SAS as the data analysis tool. Our current Data Warehouse architecture involves storing summary data in traditional DB2 relational databases and storing raw chip data in a DB2 BLOB data type. With this BLOB data type many opportunities have opened up for our team to experiment with various methods of retrieving data. This paper details the choices we made and the technical reasons why | proc sql, relational database, structured data, dataset | 5 | |
| 86 | Retrieving Weather Data from the Internet and Analyzing Using the SAS System | Richard D. Langston | This poster attempts to describe two distinct processes: 1) obtaining weather data from Internet sites and 2) analyzing the data using the SAS System. There are two different Internet sites that are described: 1) the National Climatic Data Center OASIS site, and 2) the hurricane data repository at Purdue University | proc step, proc append | 5 | |
| 87 | Sample SAS Program for Reporting (from Multiple Records) | Hla Win | This paper is intended to create a summary frequency table for the data set that contains information of different procedures performed by each family practice resident of Mid-Hudson Family Health Institute. It is considered that due to the repetitive data entry (scanned punch-CARDS), simple PROC FREQ could not be used. To modify the data set, the author developed a SAS program that includes 2 main strategies namely, 'expanding single observation into multiple observations by using an array and DO loop' a n d 'combining variables.' This program can be used in any educational program to track down frequency and types of training already received. It is envisaged that the users could make use of this program in a similar situation with repetitive data entry |
proc freq, sample | 2 | |
| 88 | SAS Data Views: A Virtual View of Data | John C. Boling | The concept of a SAS data set has been extended or broadened in Version 6 of the SAS System. Two SAS file structures now function as SAS data sets: SAS data files and SAS data views. Both file structures can be processed as SAS data sets in DATA steps or procedure steps. This paper introduces the concept of a SAS data view, presents its advantages and disadvantages, discusses the three types of SAS data views, and compares its structure with that of a SAS data file. Defining a SAS Data View The SAS data file is already familiar to existing SAS users because it represents what, historically, has been called a SAS data set. For example, 1982 Version SAS data sets and Version 5 SAS data sets are technically referred to now as SAS data files. This is essentially a change in terminology. The new file structure and definition in Version 6 is the SAS data view. Unlike SAS data files, SAS data views do not contain actual data | dataset, proc sql, proc step, proc print, create view, order by, proc contents | 5 | |
| 89 | SAS Macro Solutions: An Application to Get You Going | Ralph W. Leighton | calendar year, macro variable, macro loop, proc step, variable, macro definition, macro facility, %if-%then-%else, proc print, call symput, do loop, proc format, proc transpose, casualty insurance, claims development, format statement, raw data, macro function | 10 | ||
| 90 | SAS Program Efficiency for Beginners | Bruce F. Gilsen | dataset, where statement, if-then-else, proc step, subsetting if, conditional do loop, input statement, character variable, proc append, set statement, numeric variable, flat file, sas function | 9 | ||
| 91 | SAS Programming Career Choices in the Health Care Industry | Bryan K. Beverly | As the traditional health care access, delivery and finance paradigms evolve, the opportunities for SAS programmers to address the technical needs of the health care industry are expanding. Above and beyond prototypical positions such as research assistants and clinical research programmers, new employment opportunities for programmers are emerging as health care providers, payers and patients concern themselves with the business of health. The purpose of this poster is to present an overview of the SAS programming careers in the health care industry. This information should be helpful to those persons with good or growing skills that need ideas on where they should target their career development activities | sas programmer, sas programming | 3 | |
| 92 | SAS Software as a Compute Server for Java | Andy Norton | proc sql, input statement | 7 | ||
| 93 | SAS Versatility: Using SAS Software to Analyze and Report Students' Evaluations of Faculty | Gail Wisan | An antiquated COBOL program was used to process data from students' evaluations of their instructors. When the evaluation form was updated, the COBOL program also required modification. Rather than updating an old COBOL program, a new evaluation and reporting program was developed. SAS was used not only to statistically analyze the evaluation data but also to create the multiple required reports which were substantially different from standard SAS output. The analysis portion of the SAS faculty evaluation program utilized PROC SORT, PROC MEANS, PROC FREQ, and the BY statement. Moreover, since derived items were important evaluation tools, the MEAN function was also utilized. The report portion of the SAS faculty evaluation program required mimicking the existing COBOL reports. The highest priorities assigned for the report were: 1.) saving space/paper; 2.) duplicating the appearance of the old COBOL reports that administrators and faculty were accustomed to viewing in prior reports |
proc step, proc freq, proc print, put statement, proc sort, standard deviation, numeric functions, sas output, by group processing, data science, macro definition | 10 | |
| 94 | SAS/ACCESS Software: PROC ACCESS a Quick Start Guide | Eric E. Engle | SAS/ACCESS Software provides an interface between the SAS System and external data files from other database management systems, including PC file formats such as DBF, DIF, WK4, and XLS. To an apprehensive SAS programmer the process can appear confusing, the syntax a little non-standard -- even the documentation looks different than the Base SAS and SAS Procedures guides. This discussion will provide a gentle |
proc step, dataset, variable, data wrangling | 4 | |
| 95 | SAS/GRAPH Software: Doing More with Less ANNOTATE | Irene Mendelson | Annotation, an advanced technique to embellish basic graphs in SAS/GRAPH, is at times an arcane process. It involves first of all recognizing what features in the graphic desired can be produced with the basic tools and then deciding how to translate the additional lines, text, or shapes desired into SAS instructions in the annotation dataset. In the last few years, improvements in SAS/GRAPH have provided alternatives to annotation in certain cases. This paper will touch on these improvements and outline some rules of thumb that the author has found useful in distinguishing when to use annotation and when a simpler technique will do |
bar chart, regression analysis, proc step | 6 | |
| 96 | Saving Trees and Time - A SAS Macro for Testing and Reporting the Association Between Multiple Candidate Variables and a Single Binary Variable | Christianna S. Williams | In beginning the analysis of a large data set for research, it is often of interest to screen large numbers of variables for their statistical association with a dichotomous outcome (or treatment) variable. But who wants to sift through pages and pages of output from the FREQ procedure? Or risk the transcription errors inherent in manually transferring the data to a more concise and readable format? This paper will present a set of useful and flexible |
confidence interval, macro variable, relative risk, sample, odds ratio, macro definition, proc freq, proc step, binary variable | 9 | |
| 97 | Search Your LOG Files for Problems the Easy Way: Use LOGCHK.SAS | Michael A. Walega | macro loop, %if-%then-%else, macro definition | 2 | ||
| 98 | SELECT ITEMS FROM PROC.SQL WHERE ITEMS > BASICS | Alan Dickson, Ray Pass |
proc sql, proc step, dataset, order by, group by, select, by group processing, where statement, create table, proc print, proc sort, raw data | 10 | ||
| 99 | Simplifying Complex Character Comparisons by Using the IN Operator and the Colon (:) Operator Modifier | Paul H. Grant Jr. | Complex character comparisons - comparisons of character values in a data set against a number of character constants which vary in length - are easier specified than coded. For example, how would you select the records of customers whose last names begin with 'Mc' or 'Mac'? How would you select the records of customers who live in ZIP codes beginning with '010' through '0131'? This paper will show you how to use the IN operator and the colon ( :) operator modifier to code these comparisons simply and clearly. A Sample Comparison To illustrate this discussion, let's use a more realistic version of the ZIP code comparison. Suppose we have to select the records for all the customers located in ZIP codes which begin with: '010' '011' '012' '0131' '0133' '0134' '0136' '0138' The difficulty is that we want to select some records based on the first three bytes of the ZIP code, others based on the first four bytes | character constant, length statement | 2 | |
| 100 | Small Chat Between Jim and Bill Under Windows Using DDE | Han-li Lee | This paper shows you a simple way to read and write between SAS data files and non SAS based files under the PC Windows environment using Dynamic Data Exchange (DDE) method. It is based on a hands-on mini-workshop that the author prepared for teaching Company's SAS users. It uses a simple and direct approach by which users can easily export a SAS data file into an EXCEL spreadsheet and visa versa. The variables that contain embedded blanks will be discussed in detail. The use of the wizard in SAS new version 6.12 will be illustrated and other PC files like lotus and dBase will also be depicted. This paper will benefit those users who are not a bit and byte's person but need to get jobs done. The examples used have been tested on SAS versions 6.08, 6.11 and 6.12 |
excel, dataset, input statement, sas output | 7 | |
| 101 | Split Street Address Information Using Do Loop and Array Processing | Winston Lai | Many data entry systems for business applications allow the user to enter street address information using any convenient format. General address variables such as "LINE1" and "LINE2" are very common. Although this method simplifies data entry, it produces street address data that are difficult to use for matching, tracking, and solicitation projects. This paper demonstrates how to use DO loop and ARRAY processing in Base SAS Software to split a free format street address into basic components of street name, building number, street direction and apartment number. The same logic can be used to split a person's name or city-state-zip combination into components | do loop, array | 2 | |
| 102 | SQL Case Expression: An Alternative to DATA Step IF/THEN Statements | John Q. Zhang | This paper will show that case-expression utilized in PROC SQL can be as efficient and flexible as the logical expression IF/THEN statement. In addition to the advantage of clear, brief code, PROC SQL can not only perform procedural steps but data steps as well. This paper will show how powerful a tool PROC SQL can be |
proc sql, proc step, data step, macro definition | 7 | |
| 103 | SQL Step by Step: An Advanced Tutorial For Business Users | Judy Loren, Gregory S. Barnes Nelson |
Some excellent papers have been presented at SUGI and NESUG giving reasons to use SQL (Hermansen), overviews of SQL (Winn), selected features of SQL (Pass & Dickson, Peters), or behind the scene details of joins (Kent). This paper aims to solidify your understanding of the power of SQL by presenting an integrated series of examples that progress from the straightforward to the intricate. The paper will focus on coding but will pay some attention to performance issues and use of SQL via pass-thru to other databases. Scope of this Paper The purpose of this paper is to introduce advanced users of the SAS System to some of the more complicated tasks that can be accomplished using SQL. This is not intended to provide the reader with a blueprint of all the functionality found in SQL, but rather a focused exercise into the complexity of the language. Each of the examples is presented in order of common business questions that might have been asked | 10 | ||
| 104 | Summarizing Data to Assess Patient Compliance With a Protocol | Sarah McLaughlin | data step, proc print | 3 | ||
| 105 | Summarizing Impossibly Large SAS Data Sets for the Data Warehouse Server Using Horizontal Summarization | Michael A. Raithel | Data warehouse applications thrive on pre-summarized data. When a warehouse's data originates on a mainfram e computer, it makes sense to take advantage of th e mainframe's power to summarize the data before porting it to the warehouse server. The SAS System is the perfect tool to use to summarize data bound for a data warehouse . However, summarizing very large SAS data sets with many CLASS variables can be problematic even on a mainframe computer. This paper introduces the concept of Horizonta l Summarization. Horizontal Summarization is a methodology that can be employed to summarize very large SAS data sets by every combination of a specific set of CLASS variables. Horizontal Summarization is designed to work with th e mainframe operating system and avoid common out-of - memory and work-space-shortage abends. Horizonta l Summarization was specifically developed and employed as the front-end summarization tool for a data warehous e _TYPE_ Description application |
dataset, class variable, proc means, do loop, proc step, batch job | 5 | |
| 106 | Survival Analysis: Non-Traditional Applications for Non-Statisticians | Margaret Ann Goetz | Survival analysis (also known as failure time analysis) is a way of examining time to a particular event in a set of data. Originally developed to study time until death, survival analysis is being used with increasing frequency by research professionals across many disciplines. For many public health professionals and others, however, survival analysis remains a daunting and unfamiliar series of procedures. As a result, the potentially beneficial and flexible application of survival analytic techniques is often overlooked. This paper asks the question: could survival analysis be used to examine my data? The goal of this paper is to assist the non-statistician or public health researcher in considering survival analysis as an alternative technique using SAS . As an example, the paper explores a non- traditional application of survival data--a study of drinking during pregnancy--where the event of interest is not death | survival analysis, proc step, proc phreg, censoring, randomization, clinical trial, hazard function, response variable, treatment effect, logistic distribution, regression analysis | 5 | |
| 107 | Survival Strategies and Solutions for the New Millennium - Year 2000 | Kirk Paul Lafler, Sunil Kumar Gupta, Charles E. Shipp |
As the turn of the century approaches, critical challenges face organizations world-wide. Will mission-critical computer applications be equipped to handle the numerous calculations and comparisons required by businesses to successfully operate? And how can organizations smoothly transition into the next century without impacting current operations and staffing levels? If estimates are accurate, as much as 90 percent of all business applications will be impacted in some way by the coming millennium. A successful strategy is needed to ensure cost-effective, timely, and lasting solutions | sysdate9, date variable, proc step | 6 | |
| 108 | Tables and Views and Forms: Oh My! | Alan C. Wilson | New in Release 6.12, the VIEWTABLE window provides a table-like interface for viewing and editing | dataset, where statement, proc step | 9 | |
| 109 | Teaching an Old Dog New Tricks: Collaboration Techniques for Legacy Data | Thalene T. Mallus | The National Institutes of Health (NIH) is the Federal Government's primary facility for biomedical investigation. As with any large agency, the NIH has available to its investigators multiple resources for data management and analysis. Although many investigators now maintain and analyze their data using desktop software, many large legacy data sets are still actively analyzed on mainframe systems such as MVS. The prospect of sharing or accessing legacy data cross- platform can often seem daunting to investigators faced with this challenge. This paper reviews some existing | proc step, proc contents, dataset, format statement | 10 | |
| 110 | Techniques for Managing Large Data Sets: Compression, Indexing and Summarization | Lisa Ann Horwitz | Storage space and accessing time are always serious considerations when working with large SAS data sets. Two features available in the SAS System, compression and indexing, provide you with ways of decreasing the amount of room needed to store these data sets and decreasing the time in which observations are retrieved for processing. A third technique, summarization, can also be used to manage large data sets. This tutorial focuses on the "hows, whens, and wherefores": it offers several methods for decreasing data set size and processing time, considers when such techniques are particularly useful, and looks at the tradeoffs for the different strategies. Know Your Data Two techniques for managing large data sets, compression and indexing, were introduced in the 6.06 version of the SAS System. A third technique, summarization, has always been a mainstay of the SAS System | proc step, index, proc contents, cpu time, dataset, by group processing, proc sort | 8 | |
| 111 | The 'SKIP' Statement | Paul H. Grant Jr. | macro definition, proc step | 2 | ||
| 112 | The INPUT Statement: Where It's @ | Ronald P. Cody | input statement, infile statement, proc print, sas log, data step, proc step, column input, trailing @, dataset, error message, missing value, numeric variable | 10 | ||
| 113 | The Power of the SAS Date Functions: Creating a Utilization History for Residents of Nursing Homes | Sandra T. Rothwell | The 1985 National Nursing Home Survey and Followup collected data on a national probability sample of 11,181 persons living in nursing homes in 1984 and 1985. For each person, data included up to 103 admission- discharge date pairs describing stays in nursing homes and hospitals. Dates were not collected in chronological order and were collected from several sources. Thousands of dates were incomplete and hundreds of reported stays conflicted with one another. The first challenge in organizing this data is making it sensible: imputing missing portions of dates, correcting conflicts, and maintaining markers for stays with too little information for imputation. The second challenge is presenting the data so they can be easily used for varied analyses and still account for variations in nursing home discharge policies. The task would be almost intolerable without the powerful, easy to use SAS date functions | sample, lag function | 7 | |
| 114 | The Six Ampersand Solution | John R. Gerlach | Assume you want to reinstate macro variables denoting criteria for an analysis that you performed months ago. Using Dictionary tables, made available through the SQL procedure, it is possible to collect macro variables defined for that analysis and to store them in a permanent SAS data set containing the name of each macro variable along with its respective value. This paper explains how to preserve and, at a later time, reinstate the macro variables. As an added feature, the SAS solution lists the macro variables that have been reinstated, which requires a six ampersand solution. The Problem Consider an application used for the health industry that performs various analyses based on a priori criteria. For example, an analysis might represent patients diagnosed with Essential Hypertension from 1995 through 1996 and you are interested in specific kinds of drug therapy each patient received during a six month study period from the onset of the diagnosis |
macro variable, dictionary tables, call symput, put statement, macro put, macro loop, do loop, sas log, proc sql | 2 | |
| 115 | The Version 7 Output Delivery System | David W. Kelley | This paper discusses advances made in the output generated by all SAS Software procedures. New features provided by the Output Delivery System [ODS] include HTML generation, output datasets from any printed output and a persistable output document that can be re-organized and re-played long after the PROC that created it has completed. ODS also produces all legacy line-mode printing. The talk establishes the base line ODS features to be delivered in Version 7, as well as a roadmap to future enhancements. History SAS Output has been criticized for its Green-Bar characteristics. [Green-Bar being a generic term for "traditional" computer paper mounted in a chain printer - perforations along the sides, 66 lines deep, 132 characters wide, horizontal green stripes to aid readability… The stuff legends are made of. ] Until Version 7, SAS procedures that produce printed output (i.e. destined for the SAS Listing File) have had this "page of paper" as their model when generating output |
output delivery system | 10 | |
| 116 | The What, When, Why, and How of PROC FORMAT | Allison Levine | This paper unfolds the mystery of the FORMAT Procedure. It is geared towards those people who have not used PROC FORMAT or want to learn more about using PROC FORMAT. The four questions of What, When, Why and How to use PROC FORMAT will be covered. Returning more than one value from PROC FORMAT will also be covered. Some of the situations covered include using PROC FORMAT with PROC FREQ, for Data Validation, and for table look-ups. What is PROC FORMAT? SAS provides a vast array of Formats and Informats for users to use. These supplied Formats and Informats are very useful. However, there are times when you need to define, group or represent your data in a manner unique to your assignment. PROC FORMAT can be used to create your own Formats and Informats. What is the difference between Formats and Informats? Good question. Informats are applied to variables as they are being read into SAS. Informats are also used with the Input function |
proc format, proc step, character variable, put statement, data cleaning, input statement | 4 | |
| 117 | There Is No APPEND Option on PROC FORMAT. What Can You Do? | Lori Goldman | You've inherited maintenance of a permanently stored SAS format. This format has over 100 entries. Your task is to add a few new entries and change an entry which already exists in the stored format. This presentation will demonstrate a technique for accomplishing these tasks in the absence of an APPEND option for PROC FORMAT |
proc format, proc step, dataset | 2 | |
| 118 | Time Series Magic: Smoothing, Interpolating, Expanding and Collapsing Time Series Data With PROC EXPAND | Andrew H. Karp | proc step, time series analysis, proc freq, missing value, dataset, moving average, sysdate9, sample, data step | 5 | ||
| 119 | Tips and Techniques for Processing Complex Databases: An Example With Health Care Claims Files | Linda Nelsen | Health care claims databases are used for a variety of analyses in health services research. These databases are designed to capture data relevant to processing reimbursement, not for the analysis of specific outcomes of the health care. Creating a useful analytic file with 1 record per subject presents many challenges. Claims databases have millions of records. There are usually multiple data file structures (e.g., inpatient or pharmaceutical claims), and within each file structure there are a varying number of records per patient (a patient can have 0, 1 or 100 pharmaceutical claims). This paper discusses a structured approach to processing claims databases and the development of a SAS dataset relevant for statistical analysis. It includes techniques for large files including documentation, understanding the content of the data, and minimization of the amount of data included in the analytic file | dataset, proc step, insurance claims, proc format, proc append, proc transpose | 8 | |
| 120 | Tips for Producing Customized Graphs with SAS/GRAPH Software | Perry Watts | proc format, proc step | 9 | ||
| 121 | Too Many Notes! (Fine Tuning Data Displays in SAS/AF FRAME) | Sharilyn Fabian-Ross | In the movie Amadeus, there is a scene where the audience complains that Mozart's newest composition has too many notes. They feel that his style of composition served to confuse rather than soothe the listener. (History, of course, has proven them quite wrong.) In our development environment, we have been alluding to this scene by using the phrase "too many notes" as a shorthand way of referring to a graph displaying too much information. SAS/AF provides the means to address this problem. Concentrating on the SAS Graphics and SAS/GRAPH Output widgets, examples will be provided showing how to alter the properties of widgets, build a drill down interface and control the amount of data displayed. Combining these solutions in an SAS/AF application will provide the user a dynamic way of manipulating the appearance of graphs |
bar chart, proc gchart, group by, proc step | 9 | |
| 122 | U-Analyst: A Market Assessment System for Energy Companies | Alexander Lonshteyn | sas base, proc sql, dataset | 8 | ||
| 123 | Using %WINDOW to Obtain User Criteria for Reports | Michael A. Mace | Using %WINDOW provides an elegant means by which to gather user criteria for customized or ad hoc reports. Several examples will be used to demonstrate gathering single or multiple choices per criterium. In addition, the use of %WINDOW will be compared to using %PUT and %INPUT |
macro put, macro variable, %if-%then-%else, sas base, macro definition, proc freq | 5 | |
| 124 | Using a Data Dictionary to Drive an Automated Tabulations System | Cynthia Grant | The Census Bureau creates many data products for public use consisting of hundreds of tabulations from Census and other survey data sets. Custom programming of hundreds of tables can consume enormous resources. Further, the specification and verification of tables often delays the delivery of products because there is no standard process for defining tables. The Census Bureau has just begun a test phase of a new survey called the American Community Survey (ACS). The ACS-TABS group was assigned the task of producing the table products for ACS. More than 200 different tables were to be produced for 5 different levels of geography. After discussions with Bureau data users and data producers, we decided to build an automated system that both would standardize and accelerate the process of producing tables. This system allows survey analysts to specify completely and generate tables using an interface built in SAS/AF Frame and base SAS |
data dictionary, format statement, dataset, proc sql | 8 | |
| 125 | Using Base SAS and SAS/GRAPH Software to Predict and Geographically Display Regional Estimates of Persons without Health Insurance | Matthew Reilly | With the demise of health care reform at the federal level, states have taken up the task of expanding health insurance, especially to children. Accordingly, states are left with the task of estimating this population at a sub-state level, in order to predict and analyze provider capacity and consumer demand at city, county and regional levels. The Current Population Survey (CPS) is the most widely-quoted source of information on estimates of the number of persons without health insurance. However, the Bureau of Labor Statistics does not capture these data at a county level of analysis, thereby making it difficult to estimate county markets. This paper describes an example of an application using SAS to solve this problem. It estimates regional demand for children's health insurance, using PROC REG and using PROC GMAP, maps or geographically displays these regional markets |
if-then-else, health insurance, proc step, anova, sample, response variable, proc anova, regression analysis, proc reg, proc sort, proc freq | 8 | |
| 126 | Using Compute Blocks to Enhance PROC REPORT Output | Laurie Buchanan | Compute blocks are a powerful feature of PROC REPORT, allowing for the addition of programming statements to the REPORT procedure. This paper will demonstrate the use of compute blocks to conditionally assign formats to rows of the report and to produce subtotals only when a grouping contains more than one row |
proc report, proc step, character variable | 3 | |
| 127 | Using Dynamic Data Exchange Within SAS Software to Directly Access Data From Microsoft Windows Applications | Ellen L. Asam | In order to support the statistical analysis of clinical data, we utilized the DDE facility in SAS software to retrieve clinical study data from a Microsoft Access database. Although the DDE facility is documented, it took quite a bit of experimentation to successfully execute. Our programming environment included Windows for Workgroups 3.11, SAS 6.10, and Microsoft Access 2.0. Our Microsoft Access database was available to many users on a local area network and DDE allowed us to access the most current data. In this paper, we use our programs to provide examples to review the DDE syntax using the FILENAME statement and explain how the DDE triplet maps to our Microsoft Access database. Our example reads in entire Access tables, but we describe how to set limits. Our examples use SAS macro code so accessing current data from the database could be easily repeated in any calling program. Our paper highlights the fine points and pitfalls of this useful technique |
dataset, proc sql, excel | 2 | |
| 128 | Using HTML Format to Add Depth of Information Presented by SAS Output | James Sun | Internet and intranet have quickly become important vehicles for information delivery. The versatile HTML format is one of the reasons behind the widespread popularity of web technology. While traditional SAS output procedures are still powerful in presenting complex information, their limitations and disadvantages are easy to see in this fast growing information age. In this paper, authors discuss the merits of using HTML format to produce or re-produce SAS output. The authors explain different ways to create SAS output in HTML format. Furthermore, this paper illustrates an | sas output, sample, footnote statement, proc print, proc step | 5 | |
| 129 | Using Logistic Regression to Test for Interaction in the Presence of Zero Cells | Lisa Suchower | Logistic regression models are often used for the analysis of dichotomous response variables. These models are very useful since covariates can be included in the model to account for variability and to determine the effect of covariates on the response variable. The model can also test for interactions between different covariates or explanatory variables. However, problems can occur when testing for interactions if there are frequencies of zero in contingency tables. There are several ways to handle the problem of "zero cells." If the zero cells are sampling and not structural zeros, a small value can be added to each zero cell. A sensitivity analysis should be performed to determine the smallest such number to add to the zero cells. Other strategies for handling zero cells include collapsing the categories of the covariates in a sensible way, eliminating a category completely, or modeling ordinal variables as if they are continuous | logistic distribution, logistic regression, predictor variable, regression analysis, likelihood function, main effect, response variable, proc freq, interaction effect, degrees of freedom, sample, maximum likelihood estimation, sensitivity analysis, binary variable, proc logistic, estimate, proc genmod, proc step, binomial distribution | 10 | |
| 130 | Using Macros to Input and Process Data from 3.5" Diskettes | Charles A. DePascale | We routinely use SAS to score, analyze, and produce a variety of reports for multiple-choice tests. Generally, responses arrive on scannable answer documents which are quickly processed to produce an input data file. A new client has initiated a computer-based testing program as part of its nationwide training program for sales representatives. Student responses now are delivered to us on individual diskettes, each containing two files: ID - containing the sales rep identification number RESPONSE - containing the responses to test items. Creating an input data file has now changed from an easy scanning job to the processing of up to 600 3.5/G05 diskettes for each testing session. Processing each diskette includes the following steps: 1. Reading the two files and converting them to a usable format. 2. Merging them to create a single file 3. Comparing the ID to a master file to check its validity 4. Creating an individual backup file for each diskette 5 | macro variable, batch processing | 2 | |
| 131 | Using PERL To Prepare Data Collected from the Internet for SAS Input | Robert S. Workman | This presentation will discuss the use of the Perl programming language to convert data collected for the Internet into SAS readable format. The demonstration project has been created that uses data from an Echocardiography Study. The data conversion process includes consistency checks and real time feedback to the respondent when inconsistent data entry is detected. Internet form data types that will be discussed include text boxes, radio buttons, and checkboxes. The URL for the form is: http://www.scsu-cs.ctstateu.edu/~workman/ec.htm | sample, sas output | 6 | |
| 132 | Using PROC SQL to Create Ad Hoc Reports | Anne Marie Smith | The purpose of this paper is to demonstrate the powerful features of PROC SQL in the creation of ad hoc reports for clinical trial research. The reports created are for the display, summation and statistical analysis of clinical trial research data. The SAS programs discussed are flexible and summarize Phase I to Phase III clinical trial data; the adverse events, study medications and potentially clinically significant lab test values. In the SAS programs, the use of PROC SQL affords for shorter, easier programs with short execution times, while making SAS program code efficient, flexible and maintainable |
proc sql, proc step, clinical trial, adverse events, create view, clinical data | 7 | |
| 133 | Using the New System Functions in the Data Step for 6.12 and 6.09E | Jeffrey A. Polzin | This paper summarizes the new system functions added to the SAS System for Release 6.09E and 6.11/6.12 and how they can be used in SAS DATA STEP programming |
11 | ||
| 134 | Using Unnamed Pipes to Simplify Access to External Files | John A. Wenston | Unnamed pipes allow the SAS user to run a program or execute an operating system command outside of the SAS system and redirect the output back into SAS. This enables the programmer to get data directly from an external program without having to create an intermediate data set or enter information manually. This paper presents an example that uses unnamed pipes to redirect the output from the DIR command. The examples presented have been used with SAS 6.11 for both Windows 95 and OS/2 | macro definition, macro variable | 2 | |
| 135 | Utilizing Conditional Logic To Improve Efficiency | Karla Atwell | Whether the task is statistical analysis, reporting or somewhere in-between, conditional logic allows you to select only what is needed during each phase of data processing. This tutorial expands upon the standard definition of conditional logic processing through coding examples and in-depth explanations via the DATA STEP and applicable procedures. SAS system options and Benchmarking to quantify efficiency are also discussed. Statements discussed include, but are not limited to, add, drop, subsetting-if, if- then/else, if-then/delete, if-then/output, boolean variable references, and PROC SORT options to remove duplicates. Compatible with all operating systems |
dataset, if-then-else, proc sort, options statement, proc step, where statement, delete | 4 | |
| 136 | Web-based SAS Output Viewer System | Shi-Tao Yeh | sas output, macro variable, macro definition, proc sgplot, macro loop, proc step, %if-%then-%else | 5 | ||
| 137 | Word-Wrapping Text Output in SAS Reports | S. David Riba | How do you get your SAS output that normally prints text values in a very long line to wrap in smaller lines at word breaks just like a word processor typically does? The problem with printing long text values in SAS is that either a) your output gets truncated when the next field writes over it at a predefined column or b) you can split the values at a certain line length which may, or may not, be at a break between words This datastep routine parses long text values into smaller output lines based on the output line length that has been defined. Instead of merely splitting the line, it defines the closest gap between words and breaks at that point | conditional do loop, index | 2 |
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