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.
Not quite "PROC AUTOBUCKET"
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
www.sas.com/industry/pharma/drug_dev.html
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