Thursday, November 3rd, 2016
12:30 PM (Registration)
1:30 – 4:30 PM (Meeting)
309 Lakeside Drive
Foster City, CA 94404
Thomas Leung (415) 956-3611
Jesse A. Canchola, Roche Molecular Diagnostics
PrecMod: An Automated Precision SAS® Macro for Random Effects Models
Typical random effects linear model estimation involves fitting a model with main effects that may or may not include nesting with other study factors. Part of the challenge comes when having to calculate the confidence intervals for the variance components using the correct effective degrees of freedom (Satterthwaite, 1946) and then iterating the macro over different grouping levels (if they exist).
The current precision macro, PrecMod, surmounts these challenges and provides a clear and concise path towards efficient and timely calculations ready for reporting.
About the Speaker:
Jesse Canchola is a native of California and has been using SAS for about 30 years. Jesse is a statistician by training from UC Berkeley, UC Davis and UCLA. He worked for 14 years in the Dept. of Medicine at UCSF before moving to the medical device industry at Siemens Healthcare Diagnostics in Berkeley for about 6 years, then he went on to Tethys Bioscience, a small startup in Emeryville, California for two years before landing at Roche Molecular Systems in Pleasanton, California.
Jesse was also adjunct faculty in the Department of Statistics at the California State University, East Bay in Hayward, California for 12 years from 1997 to 2008, teaching both undergraduate and graduate courses in statistics as well as being the SAS instructor for the Department. Over the years, he has presented at statistics and SAS conferences and written substantially in peer reviewed journals and in proceedings of statistics (including The Joint Statistical Meetings) and SAS conferences (including The SAS Global Forum/SUGI, PharmaSUG and WUSS).
Greg Steffens, Gilead Sciences
Third-Party Data Transfer and Metaprogramming
In my previous BASAS presentation, I introduced the concepts of “reusable code” and “metaprogramming.” There were several questions asking for an example and how to justify metaprogramming to the boss. In this presentation I will respond to the request for an example, using third-party data transfer process. I will also talk about the challenges in justifying the development of reusable code.
About the Speaker:
Greg Steffens has been using SAS for programming and applications development since 1981, primarily in the pharmaceutical and health insurance industries. He has held job positions ranging from lead technical to director-level management in seven pharmaceutical companies. He is currently Associate Director of Data Standard at Gilead Sciences. Greg’s experience includes the design and development of metadata and software to automate data definition, data transformation, data validation and FDA submissions.
Vicky Dingler, RTI International
Using SAS to compare text strings
SAS can be used to compare text strings of varying lengths to ensure data quality and eliminate redundancy. Very simple data steps are powerful tools for maximizing accuracy.
About the Speaker:
Victoria Dingler is a senior data analyst at RTI International (www.rti.org). She provides programming and database support for several nation-wide administrations of secondary and postsecondary surveys. In addition to programming, she is responsible for maintaining the survey data library, is a product tester for the in-house data and metadata tracking tools, training analysts and programmers on data techniques, administering the office data security program, and managing several programming staff.