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Archive of Past Presentations

Late Autumn meeting - Tuesday, December 9, 2003, 9am - SAS Bedminster, NJ

Using SAS Drug Development as a Report Management Application
Barry R. Cohen, Planning Data Systems, Inc.

Many statisticians and statistical programmers in the pharmaceutical industry will first come to know SAS Drug Development as a product that addresses their regulatory - compliance issues (auditing, versioning, and security) as they develop their on-going analysis programs, data, and documents for NDA filings. However, the product provides a full, flexible processing environment that can be used in other ways. In this paper, I examine standard features of SAS Drug Development that allow it to serve as a Web-enabled report management application for a library of SAS-based report programs. Such an application could cover typical functions such as: report program loading; report parameter solicitation; report program selection and execution; and report output file viewing.

Barry Cohen is a systems development consultant and President of Planning Data Systems, Inc, with over 20 years experience, much involving SAS Software.

Mr. Cohen has provided services to a variety of industries, including a focus in the pharmaceutical industry. He is a co-founder and President of PhilaSUG, the Philadelphia SAS Users Group. Mr. Cohen is an accomplished author and invited speaker at SAS and other conferences, and occasionally chairs SAS user group conference sections.
His most recent experiences have involved design of a SAS Program Development Environment, performance testing of SAS-based client/server configurations for analytic processing, and efficiency tools for statistical program development in clinical trials.


Bob Bertolatus, SAS Certified Advanced Programmer, Somerset, NJ

Data occurs and is naturally viewed as stacks or columns of values of varying height for individual units-of-interest. Usually the stacks are flattened to a single row to create a homogenous observation for each unit. To create these rows, the stacked values are distributed across each row into buckets. In SAS, a data step can be crafted to perform this reshape of the data, utilizing conditional statements to distribute the values. When the bucket count becomes large (tens or greater), the data step approach grows in complexity and becomes less flexible. This paper will show how to replace the conditional data step with a combination of PROC FORMAT, PROC SUMMARY, and PROC TRANSPOSE to almost make the bucketing process table driven. As such, this method can be readily scaled to hundreds or thousands of buckets (columns).

Bob is a computer consultant, providing data processing solutions using SAS to the telecommunications, insurance, and pharmaceutical industries in central New Jersey for nearly twenty years. He also is an adjunct instructor at Raritan Valley Community College in North Branch, NJ, teaching courses in Computer Literacy, advanced Unix and SAS. He is a MetroStars (soccer) fan because somebody has to be and enjoys traveling either with his family or by motorcycle touring.



Fall meeting - Friday, October 3, 2003, 9am, Rutgers Labor Education Center, New Brunswick, NJ

"LAG with a WHERE" and other DATA Step Stories
Neil Howard, Manager of Statistical Programming, Ingenix Pharmaceutical Services, Basking Ridge, NJ

ETL Studio on V9.1
Gary Mehler, SAS, Cary, NC


Spring meeting - Friday, June 6, 2003, 9am, Rutgers Labor Education Center, New Brunswick, NJ

Introduction to Mapping with SAS/GRAPH
Mike Zdeb

You can create maps with SAS by using PROC GMAP, one of the procedures available within SAS/GRAPH. Like other SAS procedures, PROC GMAP can be used on a number of levels. At a beginning level, you can produce a number of different types of maps using very little SAS code and no procedure options. At a more advanced level, you can create maps with labeled areas and hyperlinks to other information (examples).

This paper examines the two extremes of SAS/GRAPH mapping. First, the basics are explained, giving you enough information to allow you to create several different types of maps with SAS/GRAPH. Then, some advanced features (including hyperlinks, map popups, and animation) are shown, concentrating on creating maps for access on the web. A New Jersey example can be found here.

Mike Zdeb is an assistant professor in the Department of Epidemiology at the U@Albany School of Public Health. He teaches both introductory and advanced SAS courses, concentrating on data organzation and data management. Mike has been a SAS user since 1986 and recently completed "Maps Made Easy Using SAS", a book published by SAS as part of the books-by-users program.


SyncSort – Making SAS applications run faster
Suzanne Malzacher

Do you want better performance from your long running SAS jobs? Is there any application/process that takes hours to run? Is there any SAS application that processes millions of records? See how you can improve SAS application performance through SyncSort. SyncSort processes data and records much faster than typical applications. By implementing SyncSort to replace various SAS processing, SAS application elapsed time decreases dramatically. The key functions that SyncSort accelerates include:

* Aggregation – Summarize data for equally keyed records
* Cleansing – Correct or eliminate invalid data
* Computation - Perform arithmetic operations on data to create computed values
* Copy – Copy data
* Data creation - Create data fields based on conditions in the source data
* Extraction – Select records or fields from various data sources
* Filtering – include/omit to group records for different processing
* Integration - Combine data from multiple sources
* Join - Join data from different sources based on a common key
* Merge – Merge pre-sequenced data from different sources
* Ordering - Sort data according to key fields
* Pattern matching – frequently used for conditional processing of web log data
* Partitioning – Prepare data for parallel database loads
* Segmentation – Route source data to different targets
* Transformation - Reformat, convert, rearrange data fields
* Validation – Verify the correctness of the data

The presentation will review 3 case studies where SyncSort made a significant difference in SAS performance. Learn about the role of SyncSort in developing and improving performance of SAS applications. See a demonstration of SyncSort's GUI - an easy to use front end to simplify maintenance and modification of applications and decrease application development time.

Suzanne Malzacher is a Software Engineer at Syncsort Incorporated, focusing on creating SyncSort applications in UNIX, NT and VMS environments. With over 13 years experience at the company, Suzanne has extensive experience in writing programs in C, C++, COBOL and Perl. In addition, she is responsible for debugging customer applications, creating shell scripts, and ensuring source integrity. Suzanne has also built numerous SyncSort applications for various customers to reduce their elapsed processing time in such areas as data warehousing, data mining, data marts, CRM, ERP, DSS, BI, Oracle Financials, SAS applications and legacy migration. She earned her MS in Computer Science from Iona College.


Late Winter meeting - Tuesday, 18Mar2003, 9am, Bedminster, NJ

Data about Data: An Introduction to Dictionary Tables
Frank DiIorio

Dictionary tables have been part of the SAS System since Version 6.07. Since their introduction, they have become an increasingly popular part of the SAS programming tool box. They, and their associated views in the SASHELP library, are meta-data, or "data about data." They contain information about SAS datasets, catalogs, external files, and system options. And, best of all, the SAS programmer does not need to create them. They are created and maintained automatically by the SAS System.

This paper addresses a specific application of dictionary tables - using them with the SQL macro interface. The paper briefly reviews the different tables, then shows them at work using a series of Real World examples. To emphasize just how very cool and effective they are, we do some side-by-side comparisons of code NOT utilizing the tables and code that does. The reader should come away from this paper with: an understanding of the tables' structure, an appreciation of their power, and a grasp of how to use the techniques we demonstrate in the paper.

Frank DiIorio is author of "SAS Applications Programming: A Gentle Introduction" and (with Ken Hardy) "Quick Start to Data Analysis with SAS," Both titles are part of SAS Institute's Books by Users series and have sold over 25,000 copies. Frank has been active in the SouthEast SAS Users Group (SESUG) since its inception, co-chairing the 1994 and 1996 conferences. He has, much to his astonishment after doing the math, over a quarter century experience with SAS software. His new book, "The Elements of SAS Programming Style," (working title) will be published "real soon now." When not writing *about* SAS, Frank writes *in* SAS, primarily data management and reporting applications in the pharmaceutical industry. A native New Yorker, he has lived in Chapel Hill, North Carolina since 1974 and sort of buys into its claim of being the "Southern Part of Heaven."


Summarization with Proc Means
Ron Cody, UMDNJ

Several SAS procedures produce summary output data sets. PROC MEANS (aka PROC SUMMARY) and PROC FREQ come to mind. This talk will discuss how to produce and use these summary data sets. Some of the version 8 additions to these procedures will be discussed as well. For example, PROC MEANS now includes a CHARTYPE option and a TYPES statement as well as an AUTONAME output option (which automatically provides names to the various output statistics). You may actually discover the mystery of the underscore variables _TYPE_ and _FREQ_. So, come and learn some new neat stuff!

Dr. Ron Cody is a Professor in the department of Environmental and Community Medicine at the Robert Wood Johnson Medical School, Piscataway, New Jersey. He has been a SAS user for more than 20 years and is the author of "Applied Statistics and the SAS® Programming Language" (fourth edition), published by Prentice Hall. He has also authored or co-authored several books for the SAS Institute as part of their Books by Users (BBU) series. Ron has presented invited papers for numerous local, regional, and national SAS conferences.


SAS Drug Development
Terry Druckman / SAS NJ office

The SAS Drug Development platform provides a centralized repository of data and associated documents related to a client-defined domain, such as compound or therapeutic area, and a Web based framework through which this information can be managed and accessed. The platform includes a knowledge management infrastructure, framework to support collaboration, data warehousing technologies and analysis and reporting. Through validation, audit trails, security, etc., SDD can facilitate the obligation to readily support government regulations such as 21 CFR Part 11 compliance. The following link provides further information about the solution