MWSUG 2004 Papers - Index

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

#	Title ×	Authors ×	Abstract ×	Keywords ×	Pages
1	"To err is human, to forgive divine."	James T. Kardasis		input statement, proc step, sas log, error message	4
2	%Fun &With %SYSFUNC	Derek P. Morgan	The %SYSFUNC macro has allowed access to the SAS component language inside of traditional `DATA STEP` programming. We have used this to great advantage during version 6 to version 8 migration and in our work with our external collaborators. We've used %SYSFUNC to simplify renaming variables, changing case of variable names, and to simulate the IMPORT wizard as a part of a larger table creation task. The first two will be covered in this paper	macro variable, macro function, character variable, numeric variable, macro loop, macro definition, %if-%then-%else	4
3	A System of SAS Macros for Producing Statistical Reports	Greg A. Grandits, Ken Svendsen	Monitoring clinical trials requires periodic generation of statistical reports for Data and Safety Monitoring Board (DSMB) reviews and other purposes. These reports display comparisons between treatment groups and consist of summary statistics and significance levels comparing the groups. The summary statistics can be simple descriptive summaries (counts, means, SD's, etc.) or summaries from more complicated analyses (e.g. hazard ratios and confidence intervals from proportional hazards regression) and often include a combination of both. Existing SAS reporting procedures do not provide a straightforward method of generating such reports, in part because these reports require the collation of information from several procedures. However, with procedure output datasets, the output delivery system (`ODS`), and the data-step, SAS has the tools necessary to produce such reports	dataset, hazard ratio, proc means, regression analysis, proc phreg, clinical trial, ods html, variable list	8
4	A System to Calculate Market Risk	George Rezek	`SAS/IML` is used with Base SAS and Oracle to produce a system to calculate Value-at-Risk (VaR) with the flexibility to reflect changes in the database, in the calculation, and reporting. VaR is calculated using Monte Carlo simulation. The overall process is covered and aspects of the calculation are highlighted. Finally, the impact of the results on the Enterprise are outlined	interest rate, confidence interval, standard deviation, covariance, interest rate risk, correlation, proc transpose, proc step, monte carlo	11
5	Accessing Oracle Tables using Base SAS/SAS Access	David Thul		proc sql, proc step, sql pass-through, implicit sql pass-through, sas function, create table	3
6	Analysis of Complex Sample Survey Data	Patricia A. Berglund	The paper presents the defining characteristics of complex sample surveys and demonstrates the use of `PROC SURVEYMEANS`, `PROC SURVEYFREQ`, `PROC SURVEYREG`, `PROC SURVEYLOGISTIC` and SAS macro coding to correctly analyze these data. Means, Frequency Tables, Linear regression, and Logistic regression are programmed and run assuming a simple random sample and a complex sample design. The analytic techniques presented can be used on any operating system and are intended for an intermediate level audience. Keywords: complex sample survey data; `PROC SURVEYFREQ`, `PROC SURVEYREG`, `PROC SURVEYMEANS`, `PROC SURVEYLOGISTIC`, macro language; adjusted variance estimates; simple random samples	sample, proc step, proc calis, regression analysis, random sampling, logistic distribution, logistic regression, proc surveyreg, proc surveyfreq, proc surveylogistic, proc surveymeans, response variable, proc reg, odds ratio, macro definition, proc freq, proc logistic, macro facility	7
7	Building a Data-Driven Computer Assisted Interview Using SAS/AF	Derek P. Morgan	Creating a respondent-usable survey using the SAS System involves a large amount of developer time using traditional SAS System methods of data capture with an end-user interface, since it is directly linked to the size of the survey. In such an environment, each survey has to be custom-built and coded. This method was developed to bypass that and other obstacles related to end-user data capture. This method uses SAS datasets to implement program control, as well as provide questions and responses. A fixed series of `SAS/AF` frames interact with the user, yielding a consistent user interface. The frame code does not change between surveys-the datasets do. However, if the user needs to create a custom frame to handle the administration of a particular question, it is easily integrated into the existing method. This method was initially developed in order to administer the diet history questionnaire (DHQ)	where statement, data governance	18
8	Calculation and Assessment of Inferential Test Statistics for Meta Analysis of Cross-Over Clinical Trials	Hurley	Meta-analysis is the process of combining two or more similar data sets in order to make inference on the combined data set. In the case of clinical trials, this methodology is often used to increase the amount of data available and thereby increase the power to detect a difference between treatment groups. Meta-analysis is generally used to combine data sets from studies of similar design with similar end points. Traditionally, meta-analysis is used to combine data from two or more parallel designed data sets. However, it is often desirable to combine two or more crossover data sets or a number of crossover and parallel data sets with each other. Methodologies to do this, as defined by Curtin F, Elbourne D, and Altman DG in 2002, will be reviewed. A SAS macro written to simulate two cross-over trials and assess the results with respect to power and type I error rate of the test statistics defined by Curtin, et.al. will then be described	type i error, significance level, treatment effect, null hypothesis, clinical trial, macro variable, data step, normal distribution, macro definition	8
9	Conditional LINEs in PROC REPORT	Derek P. Morgan	Due to the way that `PROC REPORT` works, you cannot conditionally execute LINE statement within a compute block. If it is important enough in a production environment, you can get around this, as long as the report does not use a stored report definition. While you may not be able to execute LINE statements conditionally, you can conditionally construct the `PROC REPORT`, and choose which LINE statements are in the `PROC REPORT` itself. This will show you how in both an application context using SAS component language, and insight on how to do this by using a `DATA STEP`	proc report, proc step, macro variable, title statement	5
10	Contrasting Programming Techniques for Summarizing Voluminous SAS Output Using the SAS ODS	Stuart D. Long, Lawrence Park	SAS `ODS` provides programmers with the ability to extract selected information from a procedure and store it in datasets. Such datasets can then be combined to summarize the results from numerous procedures. The SAS Macro facility can be used to execute and extract information from repetitively called SAS procedures. "Macro Variable Arrays" can simplify the extraction of information from SAS procedures when identical or similar code is executed repeatedly on a multitude of datasets and/or variables. This paper describes Macro Variable Arrays and contrasts these with other programming methods to obtain summary datasets from `ODS` output. Although `PROC FREQ` is used in this paper, these techniques can serve as a blueprint for extracting and summarizing output from other SAS procedures. This is an advanced tutorial intended for SAS programmers knowledgeable with the SAS Macro facility	macro variable, proc freq, proc step, call execute, do loop, confidence interval, data step, macro loop, macro definition, macro call, out=, sas output, macro facility, conditional do loop, odds ratio	17
11	Creating Variable Numbers of Output Files Based on Data Set Values	Rob Boostrom	This paper will describe a technique, using macro variables, for dividing a large set of data into a variable number of smaller files and saving each file with a name, and in a directory, based on values within the data. In other words, if there is a data set that can have sales from 25 offices on one run and 782 offices on the next, the program uses macro variables and do loops to create the number of output records equivalent to the number of unique offices that are within the source data set. Tools utilized include CALL SYMPUT, macros with do loops and SYSTASK	macro variable, data step, call symput, proc step	4
12	Creepy Characters	Elizabeth Axelrod	SAS formats are extremely useful for categorizing and grouping ranges of values, but there are situations that can yield unexpected results. This paper illustrates several very creepy results, and cautions the user when identifying character ranges	proc format, char., proc step	3
13	CRM: Making it Simple for the Banking Industry	Aslam Chaudhry	Executing Customer Relationship Management (CRM) for the financial and banking industry involves many issues, including the use of unique processes and solutions. To be successful with CRM, financial and banking organizations must define and develop a business strategy as well as a supporting infrastructure for that strategy. SAS Banking Intelligence Solutions can be used to complete these tasks smoothly and efficiently - even by the non-technical banking business user community. With SAS Banking Intelligence Solutions, banking and financial industry business users can quickly learn how to segment, cross-sell, up-sell, and retain customers. Additionally, the users can monitor customer life-cycle trends to aid customer portfolio management with a "customer equity" assets management focus	customer retention, financial services, subscriber analytics, proc freq	11
14	Data Parameters for Interval Reporting	Nina L. Werner	Once you realize how easy it is to let SAS get your interval dates for you, you will never be entering date parameters again. The intended audience is Base SAS users of all experience levels on any platform	macro variable, call symput	3
15	Data Quality and the Big 'D'	Elizabeth Axelrod	With the passage of time, it becomes more and more difficult to recall or recover the process, data, or results of a project. With the new Federal Data Quality Act, it is even more critical that the entire data process be repeatable. This paper presents an overview of the whys and hows of documenting an entire data process - the many steps involved in creating deliverables for government research projects. Examples of how to accomplish this are provided	sas log, raw data	4
16	Doing the Cool and Unusual with PROC FORMAT	Marty Hultgren	As an instructor at SAS I am privileged to work with students of all levels, from beginner to advanced, and one of the joys of my job is sharing little golden SAS nuggets with students - cool and useful things to do that can help back at work. The nuggets I present in this paper all concern the use of `PROC FORMAT`. I am assuming the reader has had some experience with `PROC FORMAT` and the VALUE statement, but I will spend a little time reviewing basic `PROC FORMAT` syntax, before demonstrating some other very useful techniques	proc format, proc step, format statement, proc tabulate, options statement, work library	8
17	Don't Resort to Re-Sort	Elizabeth Axelrod	You have a huge dataset, happily sorted by one or more keys. But you need to modify your key fields on a relatively small number of records, which will result in an un-sorted file. This paper presents a quick way to re-sort your file ... without re- sorting your file!	data step, set statement	3
18	Efficiency Techniques for Beginning PROC SQL Users	Kirk Paul Lafler	`PROC SQL` provides `SAS/G115` users with a powerful programming language that can rival `DATA STEP` coding techniques either in terms of the processing time or the human time involved to write the code. Attendees of this hands-on workshop will learn how to use `PROC SQL` statements, options, and clauses to conserve CPU, I/O, and memory resources while accomplishing tasks involving processing, ordering, grouping, and summarizing data. Numerous hands-on exercises will reinforce the `PROC SQL` coding techniques that efficiently utilize available resources. No previous experience with `PROC SQL` is required to attend this workshop	proc sql, proc step, cpu time, where statement, create table, order by, select, dictionary tables, proc sort	8
19	Evaluating Continuous Variable Transformations in Logistic Regression	Maggie (Yu) Zhang, Shaolin Chen, Suzanne C. Rain	In modeling using logistic regression, the appropriate transformations on continuous variables are necessary to optimize the model predictiveness. The purpose of this paper is to demonstrate a method to automatically generate univariate plots for each input variable so that users can visually detect appropriate transformations. The application of this program is demonstrated using real examples from Keybank	logistic regression, regression analysis, logistic distribution, interval variable, data transformation, response variable	12
20	Exceptional Exception Reports	Gary M. McQuown		proc step, data validation rules, macro variable, proc format, proc freq, if-then-else, where statement, data cleaning, data step	10
21	Explore, Analyze, and Summerize Your Data with SAS Software:Selecting the Most Appropriate Power Tool from a Rich Portfolio	LeRoy Bessler		proc step, proc sql, proc cluster, numeric variable, proc corr, proc print, proc tree, proc freq, proc means	20
22	Generate PDF Reports on the Web	Desheng (Ben) Xu	PDF file gets more and more popular because of its portability on different platforms and unique features on information presentation. This paper discusses how to produce PDF report from the web application through combination of Output Delivery System (`ODS`) and Application Dispatcher. JavaScript language is involved to approach more desirable result. This paper is appropriate for intermediate programmers who know `SAS/INTRNET` as well as JavaScript language	ods pdf, output delivery system, proc step	7
23	Generating a Detailed Table of Contents for Web-Served Output	Derek P. Morgan, Steve Hoffner	`ODS HTML` is a powerful tool that can automatically generate a hyper-linked table of contents for output produced by the SAS System. What happens when this excellent functionality doesn't quite go far enough? If you have a multi-user application, you need some way to distinguish between output for user A and user B, even if they have used the same program to produce different results. What happens if the output is directed to different physical web locations? If some of the output is generated outside of the SAS System (for example, with a shell command), `ODS` won't even know that it exists	macro variable, ods html, ods pdf, proc step	7
24	Getting Started with SAS ODS the Easy Way: Custom-Present Tables and Graphs Via the Web without Mastering PROC TEMPLATE	LeRoy Bessler		proc print, proc step, output delivery system, proc template, ods listing	20
25	How to Get SAS Data into PowerPoint with SAS V9	David J. Bosak	The purpose of this paper is to describe three techniques of creating PowerPoint presentations from SAS. Two of these techniques are independent of SAS version. One of the methods is dependant on new features in SAS9. The paper will describe these techniques, and comment on the relative merits of each technique	excel, sas base, xls, proc step, dataset, proc sql, proc template	10
26	Let SASHELP help you - Easy Ways SAS Can Help you with Your Dynamic Programming and Validation	Stacey D. Phillips, Gary Klein	SASHELP tables are data views, contained in the SASHELP library, that automatically collect and store information about the current SAS session on a constant basis. These views are a wealth of information about your session and can be accessed via standard `DATA STEP` procedures to improve and automate your code. Through the combined use of SASHELP views and macros, programming can be easily transported between different programs and projects as your code becomes more dynamic - that is, less data-driven. This paper will explore some simple techniques using the information contained in the SASHELP library that can be utilized in general programming and the validation process to automate your programming procedures as well as make them consistent across programs and projects	sashelp, proc step, dictionary tables, proc sql, data cleaning, data step, macro variable	7
27	Pipeline Parallelism Performance Practicalities	M. Michelle Buchecker	Pipeline Parallelism is a great new feature of SAS 9. This presentation will describe what pipeline parallelism is and how it differs from traditional SAS operations. The purpose of pipeline parallelism is to increase your speed of your program and decrease your I/O operations. This paper will take you behind the scenes to show you what's going on and focus on what NOT to do that could actually cause your program to behave improperly. THE "GOOD OLD DAYS" - SEQUENTIAL EXECUTION AND INDEPENDENT PARALLELISM	proc step, proc sort, mp connect, proc means, sas 9, proc print, elapsed time, dataset	6
28	Presenting the Binary Logit Model Effectively using SAS	Hun Myoung Park	The binary logit/probit regression models can be more difficult to interpret the results than linear regression models. The difficulty is due largely to their nonlinear nature. Just reporting parameter estimators is not helpful for understanding the impacts of independent variables. Like STATA and SPSS, however, the SAS procedures such as LOGISTIC, PROBIT, and GENMOD focus on producing parameter estimators and goodness-of-fit measures. This paper illustrates how to effectively present binary logit/probit models using the several methods extensively discussed by J. Scott Long (1997). A `SAS/IML` program, similar to Long's SPost module, was written to conduct the necessary follow-up analyses of binary logit/probit models	predictor variable, logistic distribution, response variable, logistic regression, proc logistic, odds ratio, standard deviation, likelihood function, goodness of fit, probability distribution, regression analysis, hypothesis testing, sample, binary variable, covariance, interval variable, maximum likelihood estimation, sample size, proc reg	11
29	Producing HTML Reports with ODS	James T. Kardasis	The user will learn how to produce detail and summary reports as well as frequency tables with SAS procedures. These reports will be enhanced with labels, formats, titles, and footnotes. This paper will introduce the user to producing HTML reports with `ODS`	proc tabulate, proc step, class variable, proc freq	8
30	Reporting SAS 9 License Usage Through ITRM	David J. Bosak	The purpose of this paper is to describe a way of quantifying and reporting SAS product usage precisely. The paper will outline a method of capturing SAS session usage using the RTRACE configuration option. The data from RTRACE will be mapped to SAS products, and reports will be generated from SAS IT Resource Management (ITRM). The result will be a system for monitoring and analyzing SAS license usage from ITRM	exception handling, sas library, sas base, proc step	10
31	Revelation and Persuasion: Solutions for a Finite Work Day in an Era of Information and Image Overload	LeRoy Bessler	This tutorial covers simple and widely applicable ways to present data for quick understanding, using procedures in Base SAS and `SAS/GRAPH`. You are provided design principles, design examples, and working code. Included are some coding techniques that enable you to get results that the software options do not naturally support. The design of outputs is intended to be communication-effective, and to focus viewer attention on the message, not the medium. The code can be adapted to your own data without much work. The design principles are software-independent. This tutorial assumes no prior knowledge of `SAS/GRAPH`. It cuts through all the documentation and bypasses the problem of "Options OverChoice" to empower you to make elegant and effective pie charts, bar charts, and trend charts-to get them right from the start. For tabular reporting, the tutorial focuses on a few key design principles and construction methods	bar chart, proc step, macro loop, footnote statement, proc print, output delivery system, proc template, proc report, %if-%then-%else, macro definition, sample, class variable	31
32	SAS Code for Estimating the Confidence Interval for a Standardized Effect Size	S. Jonathan Mends-cole	The inferential confidence interval provides an alternative to the significance test. The range of confidence limits indicates the veracity of the null hypothesis (Hoenig & Heisey, 2001). The width of the interval indicates the precision of estimating the population parameter (Hoenig & Heisey, 2001). Effect sizes aid decisions regarding the effectiveness of an experimental treatment (Kirk, 1996). The interval inversion principle brings together the explanatory capacity of the confidence Interval and the effect size (Steiger and Fouladi, 1997). The observed test statistic is used to find confidence limits about the noncentrality parameter. The SAS (2001) function TNONCT provides the limits. The equation for the noncentrality parameter is modified to solve for the effect size limits	effect size, confidence interval, sample, sample size, standard deviation	6
33	SAS Enterprise Guide 3.0 for SAS Programmers - A Roadmap to Combining the Best of Both Worlds	Stephen McDaniel, Annette McDaniel, Gregory A. Smith	SAS Enterprise Guide is a powerful Windows client application that provides both a guided mechanism to utilize the power of SAS and a intuitive SAS code development environment. Results can be shared throughout the organization either via the SAS Stored Process Server, SAS IntrNet, the SAS Information Delivery Portal, email, or sending the results to other third party applications such as Microsoft Word or `EXCEL`. Business analysts, statisticians, and SAS programmers can all use Enterprise Guide to leverage the power of SAS on many platforms enabling them to access data simply and quickly and perform basic reporting and utilize complex analytics such as forecasting and linear modeling. This paper will cover the pertinent areas of SAS Enterprise Guide relevant to developing and managing SAS programs efficiently and effectively	sas enterprise guide, clinical research	3
34	SAS Macros to Implement Survival Analyses for Oncology Clinical Trials	Adeline Yeo	The primary objective in Oncology clinical trials is to determine the effect of drug treatment on the survival experience of patients. Survival analyses are often implemented in SAS for this purpose. However, analyses can be similar across studies, resulting in SAS programs being repetitive. The motivation is to implement common survival analyses as a SAS macro, and to have the macro validated for multiple uses. In this SAS macro, we have incorporated two procedures for survival analyses; `PROC LIFETEST` and `PROC PHREG`. The `LIFETEST` procedure output descriptive statistics, survival rates, the number of patient at risk, and p-values of log-rank, Wilcoxon and trend tests. The `PHREG` procedure provides hazard ratios and the confidence intervals of the ratios. This SAS macro is suitable for regulatory reporting and can be used in the clinical phases of drug development	hazard ratio, macro variable, proc step, macro definition, confidence interval, proc phreg, survival analysis, proc means, sample, ordinal variable, time variable, wilcoxon test	5
35	SAS Solutions for Fatal Pitfalls Within the Consumer Expenditure Survey	Jon G. Fields	The data within the Consumer Expenditure Survey (CES), collected by the United States Department of Labor, Bureau of Statistics, and the Census Bureau, provides a plethora of valuable data for researchers. Each year of CES data consists of many individual data sets with differing structures. While thousands of researchers use the CES data for various analyses, many are unaware that the structures within the data sets present serious pitfalls that, if left uncontrolled, produce seriously flawed statistical output that lead to erroneous conclusions and misleading publications. This paper discusses the problems created by the existence of multiple observations for individual households within some of the CES data sets and illustrates the ramifications of those multiple observations. It also discusses the problems created by the absence of data for individual households in some of the data sets that must be merged with other CES data sets that do contain data for those households	proc sort, proc step, proc means	5
36	Skinning the Cat This Way and That: Using ODS to Create Word Documents That Work for You	Elizabeth Axelrod, David Shamlin	By supporting formats like Rich Text Format (RTF) and HyperText Markup Language (HTML), the SAS Output Delivery System (`ODS`) makes turning SAS output into Word documents easy. Tabular output from SAS procedures can be sent directly to a file that Word can read. However, the resulting Word document might fall short of your expected results. For example, we needed to create a Word document that would serve as a user guide (or codebook), describing over 400 variables in a survey study. The body of the document needed to include detailed information for each variable. The Table of Contents needed to list the page numbers for the sections including the detailed information for each variable. As a result, we experienced several formatting challenges. Long values in section titles did not wrap correctly, and there was too much white space between sections, to name a few	sas output, title statement, ods html, output delivery system, proc step, ods rtf, label statement, proc print, sas batch, macro variable, proc freq, proc template, proc sql	10
37	Subset Raw Data and Zip Them Up	Desheng (Ben) Xu	The special variable _INFILE_ can be used to subset the raw input data. The PUT _INFILE_ statement writes the entire input record to the output destination. The %DO loop in macro is used to create dozens of external file references. Finally, use `DATA STEP` statements to generate a batch program that can be invoked in WinZip command line to zip up every resulting subset file. This paper is appropriate for programmers with intermediate skills and related macro background	macro variable, dataset, infile statement, data step, file statement, if-then-else, macro loop	3
38	The Randomization Test Procedure: Testing for a Non-Zero Quadratic Effect	Ling Huang, Paul W. Johnson	A randomization test is performed for data from the ultraviolet light spectrum of 2,5-dimethyl-2,4-hexadiene (in methanol). This compound is a useful chemical intermediate for agricultural products. A quadratic model is fit to the data. Absorbance (the dependent variable) is a measure of the extent to which a compound absorbs radiation of a particular wavelength (the independent variable). The optimum value of absorbance is obtained and a two-tailed test for a non-zero quadratic effect is conducted. The SAS macro is available for download from http://pages.prodigy.net/johnsonp12/rtest.html. The macro requires base SAS (SAS, 1990), `SAS/GRAPH` (1991) and `SAS/STAT` (1993) software to run	randomization, permutation test, significance level, regression analysis, outlier	8
39	Unleashing the Power of SAS Functions to Manipulate and Manage Data	Ben Cochran	Oftentimes, SAS users receive data that is alleged to be cleaned, scrubbed, and ready for an application or placement into a Data Warehouse. Even though the data may be clean, many times further manipulation is needed. This paper examines ways of manipulating data through the use of functions in the SAS System. This paper is intended for the beginning and intermediate level SAS user. Functions and arrays are a part of the base SAS System; therefore, no other SAS product will be examined in this paper	character variable, character functions, sas function, dataset, input statement, numeric functions, ranuni	5
40	Using Different Methods for Accessing Non-SAS Data to Build and Load that Data Warehouse	Ben Cochran	Often SAS users need to access data from non-SAS sources. This is especially true when constructing a SAS data warehouse from other vendors' databases. While this task is not too difficult, sometimes unforeseen challenges can arise, especially when dealing with date values. This tutorial initially takes a look at several methods for accessing different kinds of data to do the initial load of the data warehouse. Then attention is given to various ways of doing incremental updates and how to overcome some potential problems. This paper follows the tasks that were involved in a specific retail application and how a certain organization faced and overcame the challenges that accompany building and updating a data warehouse. To accomplish its objectives, this paper is divided into seven sections. The first section looks at the environment and issues surrounding the initial load of the warehouse	date variable, libname statement, proc sql, proc step, data warehouse, macro variable, dataset, create table	9
41	Using Excel's Active-X Controls as a GUI for SAS Solicit File Creation	Eric A. Reece		excel, macro variable, proc step	4
42	Using Meta and Project Data for Data Driven Programming	Brian K. Varney	Creating dynamic, data driven programs is a very powerful tool. Often we can use the metadata or the project data itself to help write our programs. This paper will introduce some of the concepts related to writing SAS programs which will generate pieces of SAS code in a data driven manner. Parts of the SAS language used in this discussion include the following: 1. macro call routines (c all execute, call symput)	macro variable, proc sql, proc step, call execute, dictionary tables, call symput, macro loop, data step, label statement	9
43	Using SAS at SAS: The timing of SAS Tech Support	Annette M. Sanders, Craig DeVault	Over the last decade, the amount of data that has been collected and stored has increased dramatically. Statistical methods have been, and continue to be, refined and perfected to yield valuable answers to business problems and goals using standard qualitative and quantitative data analysis methods. However, an overwhelming portion of all data collected is actually unstructured text. The evaluation of textual data is a relatively new focus area of statistics. Classical methods that generate answers to business problems can be enhanced effectively by coordinating the analysis of textual data with SAS Enterprise Miner and SAS Text Miner. The Technical Support Division at SAS has not escaped this deluge of data. Although we collect data for every question or problem that we receive in order to manage the delivery of answers and solutions, we have never used the data as a tool for discovering underlying behavior or patterns of software and support issues -- until now	data mining, proc freq, proc hpsplit, dimensionality reduction, regression analysis, dataset, chi-square test, scree plot, txt, proc corresp, outlier	22
44	Using SAS Enterprise Guide to Automate and Customize Statistical Analyses for Pharmaceutical Data	Alex Dmitrienko, Cindy Lee	With increasing demand to analyze massive clinical and non-clinical data, pharmaceutical companies are looking for tools to set up systems for automating and customizing statistical analysis and reporting. The systems reduce statisticians' time spent in programming and expedite the design, conduct and analysis of pharmaceutical studies. SAS Enterprise Guide provides a powerful graphical user interface that facilitates common tasks such as data access, analysis and reporting in a Windows environment. This presentation demonstrates the benefits of using SAS Enterprise Guide as a tool for creating analysis and reporting modules in pharmaceutical applications. As an illustration, the presentation describes custom Enterprise Guide tasks for designing clinical trials and summarizing clinical trial data	sas enterprise guide, clinical trial, macro definition, group sequential design, clinical data, proc means, character variable, numeric variable, standard deviation, data science, treatment effect, sample, data cleaning	6
45	Using SAS Special Missing Values to Enhance Display and Analysis of Survey Research Data	Francine Cannarozzi		missing value, proc freq, sample, proc step	5
46	Using SAS/Access to save time and automate non SAS processing steps	Timothy J. Stires	This paper will discuss benefits for using SAS Access to streamline processes and procedures for marketing rental list selection. Rental lists are selected from customer marketing databases managed on behalf of retailers, who realize the value of their marketing databases as a revenue source. Using SAS Access, marketers can provide list rental as a service while maintaining compliance with opt-out requests and local and regional privacy legislation. Our list rental process was typically composed of five discrete steps, four of which occur outside of SAS. Using `SAS/ACCESS`, we can automate and manage those steps from within SAS and save time primarily spent in having a programmer perform these steps manually	raw data, proc sql	4
47	Using SAS/ACCESS-OLE DB for Pharmacoeconomic Analysis	Brandon Zagorski	Pharmacoeconomic analyses are frequently replicated over time and may require data from dynamic and disparate file formats; and without an integrating software the automation of such analyses will be limited. `SAS/ACCESS`-OLE DB permits the data integration from diverse file formats, such as, relational databases, flat files, and spreadsheets, without sacrificing performance. Using the `SAS/ACCESS`-OLE DB `LIBNAME` statement, data can be accessed in an automated fashion. Using the `%PUT` &sqlxmsg statement, the `LIBNAME` statement can be built and stored so that all subsequent connections will be automated	libname statement, relational database, txt, proc sql	3
48	What to Expect When you Break into Web Development	Chuck Kincaid	Are you a SAS programmer who has wanted to do more web programming, but weren't sure the best way to go about it? Are you confused by all of the things you hear about web programming? This presentation will review the many ways that SAS can be used to take information to the web from the simplest to the most advanced. The goal is to provide programmers with information to help them make career decisions	business intelligence, sas base	6

Back to Conf go 2005 go 2003

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

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

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