MWSUG 1997 Papers - Index

63 papers. Source: MWSUG (Midwest SAS Users Group) conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Pages
1	%COMPARWS: Compare with Summary: A Macro Using PROC COMPARE to W rite a File of Di f ferences to Edit and Use for Updates	Ronald J. Fehd	Data review can be viewed as a two step process: 1. Compare: review differences between two data sets. 2. Exception Report: review data consistency with range and logic checks. A data set may updated with a file of the form: if 10 = 1 then do; <Variable name> = <value>; * end; This paper reviews the `PROC COMPARE` output data set an~ the manipulation of that data set which is necessary to write a report to a file which contains update statements in an easily editable form. SUmmary information is written at the end of the update file	macro facility, proc compare	4
2	A Bag of FSEDIT Tricks	Derek P. Morgan, Michael A. Province	It is always easier to build an application for yourself than to build one for other people to use, just because you know how everything is supposed to work. This paper details four solutions to problems that have been presented to us by our users. They are: how to annotate a specific item in a data set; improving data quality at entry; maintaining an intuitive interface when there is a category called ·other (specify)", and finally, how to handle phone numbers for painless entry and storage. For data entry applications, FSEDIT provides a good user interface. With reasonable care and knowledge, an FSEDIT screen will yield well-organized, reasonably clean data for analysis	character variable, numeric variable, proc format, proc step	5
3	A Macro for Efficient and Flexible Data Smoothing	John Cucka	This paper provides the user with a simple and flexible	macro loop, %if-%then-%else, lag function, macro variable, macro definition, missing value, proc sort, proc step, by group processing, regression analysis	7
4	A Macro Tool for Quickly Producing a Handy Documented Listing of SAS Data Sets for Use as a Reference While Writing Programs to Analyze the Same	Hugh Geary	When beginning a new project I often have a number of SAS data sets with a total of, perhaps, 200 or more variables to analyze. Appreciable time can be saved when developing programs for processing these data if listings are made so that the data can be scanned quickly. This paper describes a macro, named PRNT, which can automatically generate informative and compact listings by supplying, in the simpler cases, little more than the name of the data set. Listings are done by `PROC PRINT` and are made compact by splitting up long character strings into smaller strings so that they do not cause page-wrap. 'One method is to use a mUlti-page format splitting up long character variables over two or more pages each so that the first one fills the page to the right margin and the second begins at the left margin of the next page	proc step, proc print, dataset, macro parameter, proc contents, macro variable, macro definition	8
5	A SAS Macro System to Automate the Decoding and Validation of Data	Carl R. Haske, Kayhan Najmabadi	This paper presents an application that was implemented to solve a problem in transferring data between SAS and Microsoft Access·. The macros and AF application that were developed have gained application beyond their original intent. When data are transferred between SAS and other plat forms, a problem ar1ses if the data variables have associ ated formats. Typically, when oose is used, the trans lated data contain codad values rather than the formatted values of data. One component of this system is to auto mate the dacoding process by using the formatted values of the data. There is also a problem of validating the data transfer. When critical data are transformed to a different file for mat, one must be able to guarantee the accuracy of the data. This vandation process typically involves some type of comparison of the translated data to the original data	dictionary tables, macro variable, proc step, data cleaning, macro definition, raw data, proc sql, %if-%then-%else	5
6	A simple macro to identify samples for reanalysis	Robert H. Gallavan, James M. McKim	It is a common practice to peri'onn two or more determinations of each sample endpoint during an analytical procedure to protect against measurement error. When cost or time constraints prohibit multiple measumnents, a check for internal consistency can partially protect against measurement error. Autolllllling the evaluation process is partiCDIarly important in a regulated environment where decisions to reanalyze individual samples must be unbiased and the decision process must be documented. In the demonstration case, stained nuclei ti'om liver sections were counted using a blinded plan of analysis. The data were then partially unmasked to allow sorting into undisclosed, randomly ordered tteatment groups. A macro ordered the observations in each group by absolUle distance from the group median and sequentially tested the extreme values against the remaining observations in that group	sample, confidence interval, standard deviation, outlier	3
7	An Introduction to Survival Analysis	Linda M. Quinn	There are several procedures in SAS@> that perform various aspects of survival analysis, LIFETEST, LlFEREG, and PHREG. This talk will introduce the topic of survival analysis including definitions of survival distributions and censoring. The LlFETEST and PHREG procedures require similar structures for your dataset prior to analysis. Finally, a brief introduction and comparison of the three procedures will be shown. `PROC PHREG` will be discussed in the most detail. This talk is suitable to users will a basic or intermediate understanding of survival analysis who want to be able to use the SAS system to perform analyses.	survival analysis, proc step, proc lifereg, relative risk, null hypothesis, proc phreg, proc calis, hazard function, binary variable, left censoring, time variable, regression analysis, sample, response variable, right censoring, probability distribution	12
8	Analysis of Covariance: A Useful Tool For Investigating Molecular Interaction	John A. Wass		covariance, regression analysis, ancova, estimate, proc genmod	3
9	Automating Data Transfer from SAS to Microsoft Access	Kayhan Najmabadi, Carl R. Haske	Transferring data between the $AS System and other database platforms is commonplace in the management of clinical data. Some features built into Windows allow the SAS System to communicate and share data with other Windows applications. These tools include OLE Automa tion, Dynamic Data Exchange (DOE), and Open Database Connectivity (ODBC). By taking advantage of these tools, the programmer can develop reusable systems to render the transfer of data between platforms. A problem arises with how to handle formatted variables. When SAS writes the values of variables, it uses the format associated with a variable to print the values. However, when $AS data are transferred to a derived database in another platform, the decoded values of data are often lost The resulting exported data contain cryptic codes that are not meaningful to the end user who receives the derived database	data cleaning, clinical data	3
10	Bits to Bytes --More Efficient Memory Utilization	Brian L. Bournival	The quantity of data stored and processed by most companies has increased dramatically in the last few years, leading to a demand for more efficient memory utilization. When variables are outputted to an external file using a SAS format, the system allocates at least one byte of memory for each data point. This paper describes a method that can be used to store Boolean variables in single bits. SAS already provides an informat to read the bit-sized variables	flag variable, missing value	3
11	Building a Better Dataset Descriptor	Mike D. Tangedal	`PROC CONTENTS` is a useful tool in describing the structure of your dataset However, it does not provide any infonnation as to the data values within. I have developed a macro which will list counts of missing observations, counts = 0, min and max, and a unique value list for the variables in the dataset, depending on formats set up by the user. This will be listed in a report similar to the standard look ofPROC CONTENTS. This is accomplished through judicious use ofPROC MEANS and `PROC FREQ`. These `PROC`:edures are used but once, thus keeping the increase in processing time over using just `PROC` CONTENfS to a minimum. A complete description of the macro code is included	proc step, proc contents, proc freq, variable list, flat file, proc means, extract, if-then-else, lookup, cpu time, txt, proc sort	6
12	CATALOGER: An Applications 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
13	Chart Smart: Design Graphs to Inform and Influence	LeRoy Bessler		bar chart, horizontal bar chart	10
14	Color Smart: Design Application for Effective Visual Communciation	LeRoy Bessler			2
15	Customizing Toolbars	Deb Cassidy	Version 6.11 TS Level 0040 of SAS software has a new toolbar for FSP products. While the new icons are an enhancement for many applications, they weren't for one of the applications I support. This paper will give a brief overview of how to customize toolbars for an application		4
16	Double Data Entry in FSEDIT Using Point-of-Entry Verification	Derek P. Morgan, Michael A. Province	Double data entry is a proven, heavily used tool for data quality control. Although there are many functions avail able in `SAS/AR`:, SASlFSP and methods for FRAME entries to reduce entry errors, double entry adds to the overall quality control of the data. This paper describes a	error message, character variable	6
17	Efficency Ideas for Large Files	J. Meimei Ma, Andrew H. Karp		proc step, dataset, numeric variable, proc sql	9
18	Empowering SAS Users through a Webpage on Your Intranet	John M. LaBore	SAS users in any organization can benefit from a webpage on their Intranet. This paper describes web browsing basics and assists the user in constructing a basic webpage. Internet websites with useful tutorials and tools will be listed for the budding webpage developer. Potential webpage developers will be provided a framework for SAS-related webpage content and given the unifonn resource locators (URLs) for a number of recommended SAS related sites which can be "linked" into their webpages; plus, the presentation will include an on line demonstration of an actual Intranet webpage for SAS users	txt, pharmaceutical industry	9
19	Enhancing the Output from PROC LOGISTIC	Bharat K. Thakkar, Kwan Hur, Charles A. Oprian, William G. Henderson, Sharon Urbanski	A SAS code for enhancing the output from `PROC LOGISTIC` with the stepwise option was developed. Among the enhancements provided are: the suppression of SAS output from several pages to one table, the incremental change in the c-index statistic, the -2 LOG likelihood statistic (-2 log L statistic), and an incremental test for the significant change by using the -21og L statistic. In addition, the code provides the parameter estimate, the standard error, the Wald chi square, the p-value of the chi-square, the standardized estimate, and the odds ratio at each step of the stepwise logistic regression output	logistic distribution, predictor variable, proc step, sas output, likelihood function, response variable, regression analysis, proc logistic, proc calis, odds ratio	6
20	Examining Cointegration Among Time-Series Variables Using Customized SAS Macro: Residual-Based Methods	Jun Zuo	Co integration has become a new domain or "emerging technology" in the area of time-series analysis since the late 1980s. However, the concepts and approaches related to cointegration have not been incorporated with either SASlSTA r or SASfETS, except stationarity tests, which are included in `PROC ARIMA` in the recent release 6.12 ofSASfETS.In this paper, based on discussion about concepts and approaches of cointegration, a customized SAS macro is developed to examine the presence of cointegration among multiple time-series variables. A major SAS statistical procedure used in the macro is `PROC REG` in SASISTAT, so that people may not need SASfETS software to conduct the examination of cointegration for time-series data. Two residual based tests (Dickey-Puller and Augmented Dickey-Fuller tests) for cointegzation are embodied in the macro	time series analysis, health insurance, regression analysis, macro definition, null hypothesis, correlation, predictor variable, premium, unit root, proc step	8
21	Faster SAS Jobs and Fewer Passes Via Data Step Views	Steven J. First	`DATA STEP` views have been around for a few years, but very few jobs take advantage of them. There are many situations where `DATA STEP` views require less passes,less intermediate work space, less CPU, and less clock time than traditional DATA steps do. The improvement in certain cases can be Significant, and in fact, using views may be the only way to get some jobs to run at all. This paper will examine the pros and cons of using `DATA STEP` views and benchmark several different scenarios taken from actual production applications	proc step, data step, dataset, sas log, elapsed time, flat file	7
22	Graphing: Taking the Mystery Out of Subscripts and Superscripts (The Why Not's and How To's Are Presented)	Lori D. Griffin	This paper covers several different areas of a graph where subscripting and superscripting may occur. For example, when graphing, have you ever had a title that needed a superscripted character or a footnote that needed a subscripted character added? Maybe you need to add a note statement that has a character that is superscripted. There are other areas of a graph, like axis values and axis labels that might also contain a subscripted or superscripted character. The intent of this paper is to illustrate the differences in your code that exist when adding subscripts or superscripts to titles, footnotes, notes, axis labels and axis values. Some of these labeling areas of the graph require alternative graphing techniques in order to accomplish the task of subscripting or superscripting	title statement, sample	7
23	Handling the Year 2000 and Other Timely Issues	David E. Beam, Rosalind Gusinow	As we approach the year 2000, smart IS professionals are planning for a successful start of the next century. The SAS System has some of the most powerful date handUng capablities of any software system, and can offer valuable tools to prepare your computer files for the end of the 90's. One of the areas in which SAS Software excels is the powerful capabilities to do sequential date processing. This paper wDI present a brief OveMew of how SAS handles dates, and some examples of conveJSions required to read in a variety of dates. Examples of several unique date calculation problems wiD also be presented, as wen as a cflScussion of the year 2000 issues	sysdate9, date variable, macro variable, sample, datepart	8
24	How to Use the SAS/AF FRAME OrgChart Object	Thomas A. Miron		dataset, proc sql	10
25	Implementing a SAS Data Warehouse Considering Star, Fact Constellation, and Snow Flake Schemata	Marie Lupertin	Data warehousing, data mart, and data mining nomencla ture have lacked definitive explanations of what applies when. The key analysis question is what approach is to be taken when considering business inte1Iigence needs and the data warehouse Ii1ethod. We should not be "dazzled" by the approach most popular today. This work explores a number of architectures for the data warehouse structure. These, more often than not, are driven by the :functionality of the software being used. Thus, a general discussion ofwbat works with SAS will unfold. An implementation of a data mart for an outpatient clinical information system will be presented. The star schema, fact constellation, and snowflake methods will be contrasted. One will conclude that with any emerging teclmology, the distinctions between. these approaches are often bhnTed or overlapping	fact table, dimension table, data mart, star schema, data warehouse, proc step, snowflake schema, proc freq	8
26	Industry Directions and Their Impact on SAS Programming	Sally A. Goostrey	There are many issues being faced today by individuals in business responsible for setting infonnation ttclmology directions and strategies. Business is changing so rapidly that there is rarely sufficient time for fully customized application development SAS Institute Inc. bas increasingly incorporated computer science concepts into their product line, opening up new opportunities for increased functionality with the SAS System. Many people have used SAS software for years but have found themselves falling behind its current capabilities. Computer science graduates frequently ask questions regarding the future of becoming a "SAS" Programmer, especially those who wish to become heavily involved in QUI development and object oriented programming. QUI and RAn approaches to development tend to be contradictory to structured analysis, design and progl3l1lllling		4
27	Interfacing Microsoft Access and SAS Using OLE and ODBC	Kayhan Najmabadi, Carl R. Haske, Raymond Xu	As an OLE Automation controller, Microsoft Access can manipulate any object in another application's type library. SAS 6.12 can function as an OLE Automation server, and we have built an application that uses SAS in this way. This third and final paper of the present group brings out this brand-new possibility offered by SAS 6.12 and shows what advantage can be taken of new horizons in the enhanced SASSystem	data cleaning, dataset	5
28	Linear Regression Modeling using the SAS System: A Real-World Example Comparing the Factors Influencing Charity Care and Bad Debt in Wisconsin Hospitals	Lynette A. Gerstner		proc step, response variable, regression analysis, proc reg, predictor variable, proc corr, outlier, p-value, correlation, proc freq, standard deviation	7
29	Macro Facility Enhancements for Release 6.09E and Release 6.11	Jeffrey A. Polzin, Susan M. O'Connor	This paper summarizes the changes and enhancements which have been added to the macro facility of theSASC System In Release 6.OSE and In Release 6.11. These macro enhancements are elq)erimental. Examples of the enhanCements will be given in the paper. AdcIi1ionally, sample macro examples v.iII be made avaiIaIlIe for access using $AS Ins1iIute's InIemet gateway	macro variable, macro function, macro put, call symput, automatic macro variable, macro definition, macro facility, missing value, run, sas log, macro debugging, sas function, translate, sample, proc step, sas base	11
30	Map Smart: Design and Build Effective InfoGeographics Using PROC GMAP and Software Intelligence	LeRoy Bessler		proc step, proc gmap, if-then-else	10
31	Measuring Regressor Information Content In the Presence of Collinearity	Joseph Retzer, Kurt Pflughoeft	Design matrix collinearity is an all too common occurrence in least squares analyses. A measure of regressor variable information content suggested by Theil and Chung (which represents an extension of a concept originally introduced by Kruskal) is shown to, in part, mitigate this problem. Kruskal's method measures regressor variable importance by averaging partial correlations over all model orderings. A limiting aspect of Kruskal's weights is that they are non-additive. That is to say the sum of the weights is not intrinsica.lly meaningful. Theil and Chung suggest alternatively examining information theoretic measures which in fact are additive. The information measures are estimated using the weil know Hald Data set (which in turn contains strong collinearities). The authors take advantage of a number of uniquely powerful components of the SAS system. We begin by describing Kruskal's measurement of importance as it represents the underlying methodology employed	regression analysis, correlation, partial correlation, proc reg, predictor variable, proc step, sample, response variable	7
32	MidWest SAS User Group, Inc. - FAQ Session	Linda M. Quinn	This will be a moderated panel		5
33	Multi-Platform Code and Data Management in SAS	Paul D. McDonald	This paper will cover basic uses of the `SAS/CONNECT` with some use of `SAS/ACCESS` as well. Items discussed will include collecting data from multiple Microsoft `EXCEL` spreadsheets, getting informational data from a mainframe, and reporting the data to a SAS report. The paper uses a simple but enjoyable example of a weekly status report system to show the ideas presented. It is written for advanced SAS programmers with experience in multiple operating systems	excel, proc step, macro variable	7
34	Multidimensional Data Visualization Tools	Sanjay N. Matange, Jim Beamon, Cynthia L. Huffman		data warehouse, predictor variable	6
35	New Evolving Roles in Research for Nurses Using SAS Development of an End-User Support System and Coordination of a Research Team	Betty J. Anderson		data science, dataset	10
36	Phonetic Matching of Character Data	S. David Riba	There are many occasions when character values must be compared on non-precise. or "fuzzy", criteria For example, how do you find all the .vaJues in your data for a city such as Raleigh, NC. when the values could be spelled (or mis-speUed) as: Raleigh. Raliegh, Ralehg, or even Roleg Traditionally, SAS programming looks at the TEXT of the character values and attempts to defme all of the possible text variations. There is another way. Most people, when they do not know how to spell something, will phonetlcize It and spell it based on how·it sounds. You can compare text the same way, based on how the text value SOUNDS - phonetically. There are several tools in Base SAS5 software to compare character data based on phonetic values. This paper will review these SAS tools, and present several techniques for comparing and summarizing d. based on phonetic. instead of text, values. These techniques can be useful for all levels of SAS programming expertise	where statement, proc step, proc sql	4
37	PROC TRANSPOSE	Steven L. Wonder	Ftequently the need arises in SAS programming for intercbmging columns and rows ofdau sets. A classic example of this occurs during the `PROC`:essing of normalized data. Modem relational databases store dau by primary and foreign keys in a tabular format. However, the preseotation and `PROC`:essing of sud! dau often requires re-organization of the dau where the keys become colwnns and the dau itself are tabulaled within the columns. WbUe `PROC TABULATE` can yield sudl a presentation of Dollll3lized data. it is DOt able to output a dau set for funher analysis. This paper presents a method of processing nonnalized dau that achieves the same	proc step, proc transpose, proc sql	3
38	Reliability Data Analysis in the SAS System	Gordon Johnston		confidence interval, regression analysis, right censoring, proc calis, interval censoring, weibull distribution, data science, predictor variable, likelihood function, maximum likelihood estimation, dataset, sample, censoring, estimate, logistic distribution, proc step, binomial distribution	10
39	SAS Application Development Using Windows RAD Software for Front End	Zhuan (John) Xu	This paper presents a method for using SAS as a data management system and analytic engine, with a front end implemented with a Windows 951NT development tool such as Borland Delphi or Microsoft Visual Basic. Two approaches are introduced for connecting the SAS engine and the front end. One uses the FTP protocol to implement a client/server application, where the server can be any computer with an FTP server and SAS installed, such as a UNIX workstation. The other uses OLE Automation to control SAS on Windows 951NT. This presentation discusseS how to develop such applications. One client/server application using F1P is presented as an example. In addition, a Windows application which uses SAS through OLE automation is provided free to the J!Ublic as a front end to SAS	dataset, macro definition, txt, excel, proc step	8
40	SAS as a Compute Server for Java	Andrew A. Norton		proc sql, output delivery system	5
41	SAS/AF, EXTENDED TABLES, FSEDIT & SCL coding put together for easy location, input or additional information.	Tom O'Brien	Time is money in many types of businesses but it has a large impact in the Airline Industry, when an aircraft: is on the ground and waiting for a part. SAS!AF, Extended Tables and SCL coding have been a good solution to this problem at Northwest Airlines. SAS!AF is easy to use by all levels of our employees, from Clerks to Mechanics, Managers to Directors. Extended Tables gives us a fact view of things we need, then connected to an FSEDIT we can enter, update or delete information. Minimum entry is the key to a successful system, therefore we have added SCL coding to bring in additional information fields. This paper will show what has been developed to bring all of these SAS tools together		9
42	Saving Money on Direct Mail Campaigns with SAS/AF	Roderick Lashley, Kris Kellogg		dataset, proc step	3
43	Self-Modifying SAS Programs: A DATA Step Interface	S. David Riba	There are many situations where it is useful to haVe a SAge program modify itself during execution. Some examples include: conditiOnally execute a macro based on the data in a SAS dataset conditiOnally execute a procedure when there is no data in a SAS dataset ** _ generate in-line SAS program code based on the "COAf:ents in a SAS dataset " * write Sal Statements for the SQL Pass-Through Facility "on-the-fIy" This paper explores a little-known routine in the SAS System that allows a SAS program to write SAS code "on-the-fIy" during execution. Since CALL EXECUTE is a `DATA STEP` CALL routine, the full capabilities of the `DATA STEP` can be applied to conditionally generate SAS program statements while the program is executing. If you have ever written program code to a temporary file and then %INClUDEd it back into your program, you need to take a closer Jook at CALL EXECUTE. The paper wDl discuss the uses of CALL EXECUTE with specific examples based on real world situations	call execute, dataset, proc step, proc sql, macro facility, proc print, sample, macro variable, proc sort, excel, if-then-else, macro definition	8
44	Setting Dates In a Production Job	Deb Cassidy	A set of production jobs produced several reports. The original code required a programmer to edit several jobs each month to specify the correct fiscal years and month for the report. Editing this monthly job required considerable time and was very prone to errors. There were numerous occurrences where the value of a variable or the variable name had to represent a specific fiscal year or a month. For example, some reports needed values for a variable to be 'FY96' or FY95'. Others needed variables named Jul95 and Ju196. Titles were required that read "FY 96 vs. 95" or "Summary for March 96". This paper shows how to use date functions and macros to set all these values without editing a single job	if-then-else, macro variable, calendar year, data cleaning	4
45	Setting Up a Client Server Environment for End Users Using SAS/Connect	Howard Levine		dataset, batch job, traffic data, proc step	10
46	Share a SAS Dataset without SAS/SHARE	Philip James Busby		dataset, batch job	4
47	Statistical Enhancements in Release 6.12 of the SAS System	Maura E. Stokes	Release 6.12 of the SAS System brings several key enhancements to SASISTAT software. Generalized Estimating Equations (GEE) methodology, a popu lar technique for the modeling of correlated response data, has been incorporated into the GENMOD proce dure. Another key update is the computation of exact p-values for a number of statistical tests provided by the `FREQ` procedure. In addition, this paper provides an overview of enhancements to the MUL TTEST, MIXED, GLM, and NPAR1WAY procedures. GEE Methods in the `GENMOD` Procedure GEEs were introduced by Uang and Zeger (1986) as a method of dealing with correlated data when, except for the correlation among responses, the data can be modeled as a generalized Dnear model. . Correlated data can arise from situations such as * longitudinal studies, in which multiple measure ments are taken on the same subject at dif ferent points in time. A clinical trial for a new treatment of psoriasis may involve repeated measurements of symptom severity		7
48	Structured Design and CASE Tool Use in SAS Macro Systems	Thomas H. Burger, Philip M. Pochon	The SAS" macro facility provides powerful tools for building analysis and reporting systems. SAS" macros pennit construction of generalized modular c:omponents which can be reliably tested and integreted as a system. However, sound design principals may be overlooked during the translalion of requirements 1O code specification. CASE tools provide a uscfuI means of designing system components and Rlating them 1O function. We describe a general approach using pseudo-S1rIICtuIed design and CASE teclmology for the COIISlI1Iction of SAS" macro systems	macro variable, macro facility, proc sql	4
49	Summarize Your Data With SAS: PROC MEANS and PROC UNIVARIATE	Nestor Rohowsky		proc step, proc means, proc univariate, box plot	9
50	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 mj1!ennium. A successful strategy is needed to ensure cost-effective, timely, and lasting solutions	sysdate9, date variable, proc step	6
51	Table Look-up using Indexes, Arrays, Formats, and SQL without using Matched Merge Data Steps	Sandra Lynn Aker		proc step, merge statement, proc format, options statement, put statement	7
52	Transporting a Relational Database: The Q-System for Pharmaceutical Safty Summarization, Graphs, and Analysis	Mary Cowmeadow	Transposing relational data to one record per subject can solve all those "many to one" problems that make analyzing and summarizing this type of data so difficult. The Q System transposes phannaceutical safety data, i.e. adverse event (AE) data, from a relational database structure to a "data mart" type of structure with one record per patient. Since AE data are generally summarized and analyzed at the patient level, the data should be structured in this same way. Data can be entered directly into appropriate statistical or graphical procedures with meaningful results. Transposing a relational database is a straight forward process. The major challenge is sorting, organizing, renaming and relabeling the hundreds, sometimes thousands, of AEs collected during a clinical trial. But once this was done, writing programs for standard summary and analysis is greaily simplified	adverse events, excel, relational database, proc transpose, proc step, structured data, adverse event severity, macro variable, proc freq, macro definition	5
53	Using KEY= to Perform Table Look-up	Sandra Lynn Aker	This paper demonstrates the use of indexes to perform table look-up, a technique whereby values stored. in a master file can be compared to values stored in a table. This technique does not use the traditional match-merge, but instead is contrasted against it for advantages and disadvantages, most importantly for efficiencies. There are other alternatives to matching values without using the `MERGE` statement, and for an overview, the author recommends the ~ training publication referenced at the end. This technique for comparing values is the standard, and one that is very simple, and yet very powerful. Data is read from a data set, or from an external flat file into a data set, and from any number of the same, sorted by any number of variables, and merged by those same variables	automatic variable, proc step	4
54	Using SAS for a Bootstrap Analysis of Repeated Measures Ectoparasiticide Data	Domon Disch		sample, data step, anova, output delivery system	5
55	Using SAS for Variance Estimation with Complex Sample Surveys	Patricia A. Berglund		sample, proc calis, regression analysis, random sampling, confidence interval	9
56	Using SAS to Create and Run SAS	Joseph M. Filip		where statement, data step	2
57	Using SAS/STAT to Analyze Correlated Data	Marlene Goormastic		proc step, proc genmod, covariance, sas output, response variable, repeated measures, anova, proc mixed, binomial distribution	7
58	When %DO I Use Macros and When %DO I Not	Allan M. Kominek	The author has been using macros for over 15 years and at one point was somewhat obsessed with them. The depth of this obsession came to light when he attempted to name his children %Katie and&Courtney. After many hours of soul searching and the one incident with the . `%LET` statement, he is feeling much better now. This presentation will deal with the appropriateness of using macros. When does the power and efficiency of the macro language outweigh the complexity that can be added to program code? The decision to use macro code is strictly up to the programmer. The goal of the presentation is to provide infonnation for making that decision	proc step, macro loop, macro definition, macro variable, dataset	4
59	When Graphing, Anything is Possible: Once You Know How to ANNOTATE	Lori D. Griffin	Have you ever created a graph, especially one involving annotate, tested it to your screen and then printed it but didn't get on paper what you saw on your screen? The following paper should help you understand as well as fix problems like this. The intent of this paper is to help clear up some of those foggy areas surrounding, mainly, the ANNOTATE facility with consideration of other related graphing problems. Annotating can be FUN once you have a fundamental understanding of the basics needed to annotate. This paper will help to develop a good foundation from which to work. Some of the topics covered will be HPOS and VPOS, XSYS and YSYS, understanding the device you use, using the POSITION variable for text placement, annotate macros, and much more. All the programming code used to create the examples will be provided during the poster presentation	length statement, proc step	6
60	Word-Wrapping Text Output in SAS Reports	S. David Riba	How do you get your SAS output thatnonnally 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 clatastep 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. INTRODUcnON There are many occasions where it would be useful to take long text strings and split them into smaller substrings, with the division occuring at word breaks	if-then-else, conditional do loop	2
61	Working with SAS Date and Time Functions	Andrew H. Karp		sysdate9, datetime variable, date variable, datepart, time variable, numeric variable, dataset, raw data, proc freq, proc step, mdy, datetime, time, if-then-else, subsetting if, time series analysis	7
62	XRPT: macros %ID and %SHOW for writing Exception Reports: perform range and logic checks on a data set: write file of exceptions to edit and use for updates	Ronald J. Fehd	Data review can be viewed as a two step process: 1. Compare: review differences 'between twO data sets. 2. Exception Report: review data consistency with range and logic checks. A data set may be updated with a file of the form: if 10 = 1 then do; <variable name> - <VakJe>; * end; This paper reviews common problems in writing exception reports for range and logic checking; the product is a file which contains update commands in an easily editable form. Summary information is written at the end of the update file	macro variable, character variable, proc step, numeric variable	4
63	Your Friend the Colon: Some Efficiency Techniques for Comparing and Selecting Character Data	S. David Riba	S. David Riba, JADE Tech, Inc., Clearwater, FL	colon modifier, substr function	2

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