Another Successful BASAS meeting on June 7th, 2010!
We had a successful BASAS Meeting on June 7th at Genentech, Inc. in South San Francisco. We had about 100 attendees meeting and sharing valuable experience together.
We have 3 wonderful presentations:
Migrating to SAS 9.2: Implications for Biotech
Sarmad Pirzada
SAS 9.2 brings new challenges and technologies for programmers, statisticians and SAS admins in IT. For SAS programmers and statisticians, implications exist for macros developed for survival analysis and other STAT procedures in versions of SAS prior to 9.2. Examples discussed include PHREG and LIFETEST. Some solutions are offered with code, including a short hands-on session for migrating to ODS Graphics, SGPLOT, SGRENDER, and GTL to avoid future problems. Plus, hidden tricks and techniques to jump-start your SAS session. For IT, implications for setinits and 64-bit processing are briefly discussed.
About the speaker:
Sarmad Pirzada, is an avid SAS consultant and researcher working in the Bay Area and the Northwest for the last 10 years. Recent clients include, Fred Hutch/Cancer Research and Biostat, Wells Fargo, AAA, Visa, Community Hospital of Los Gatos and many others. He received his graduate education at University of Washington in Seattle and has been a prior employee of SAS Institute.
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Optimally Scheduling Resource Constraint Project Using SAS/ORŪ
Jeff Cai, Amgen Inc., Thousand Oaks, CA
This paper shares with SAS users an approach to effectively distribute programming resources in a clinical study project or among several projects by using procedures and macros in SAS/ORŪ. Considering some activities in a project may have precedence and time constraints, and each programmer may also have different time constraints, such as different start date and quit date on a project, personal vacation, etc., one effective method is to use constraint programming procedure CLP to optimally schedule these activities for project management. By incorporating a specific calendar, user can estimate the project target date with the constraint resource, or forecast the required programming resource with the constraint target date. Plot procedures NETDRAW and GANTT for visualizing activity data are described in this paper as well.
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%rtf2data : A utility macro to convert RTF Table to SASŪ dataset
Neeral Beladia
Often in the pharmaceutical industry, we face the challenge of reverse engineering, where we have archived summary Tables or Listings in the form of RTF Table, however the programs that led to their generation are untraceable. In scenarios like these or when there is a need to perform extended statistical analyses on the RTF Table data or when it is critical to ensure a match between multiple versions of RTF Table, it would be nice if we would be able to convert the RTF Table into a SASŪ dataset. This paper focuses on presenting a utility macro(%rtf2data) that converts most commonly used RTF Table templates to SASŪ dataset. The macro executes in a 2-phase process. In the first phase, the macro reads in a user-defined meta-data that defines the structure of the RTF file and also defines the attributes of variables in the output SASŪ dataset. In the second phase, the macro reads in the RTF Table file, parsing through the underlying RTF scripts that build the RTF Table, identifying the free text data and
generates the SASŪ dataset using the attributes defined by the meta-data read in the first phase. The macro can also be used to convert RTF Table that has logical boundaries as in the case of Baseline Characteristics Tables wherein each category defines start of a new logical section though having the same underlying structure; or physical boundaries where unequal structured RTF sections are stacked up to form a RTF Table into separate SASŪ datasets.
About the speaker:
Neeral Beladia is currently working at MaxisIT Inc. as a Statistical Programmer/Analyst providing SAS programming consulting services for Roche Molecular Systems at Pleasanton, CA. He has provided statistical programming support in the areas of clinical trials, pre-clinical research and epidemiology. He has a Masters degree in Computer Science and his research interests are machine learning and data mining.
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