In this webinar, we will be discussing the latest developments in the Bayesian capabilities within SAS, that are relevant to the biopharmaceutical industry. A focal developmental area is in the new BGLIMM procedure, a high-performance, sampling-based procedure that provides full Bayesian inference for generalized linear mixed models (GLMM). The procedure uses syntax similar to that of the popular MIXED and GLIMMIX procedures, handles multilevel nested and noIn addition, we will also present software advancement in power prior analyses, a historical borrowing method that is playing an increasingly prominent role in many disciplines. We have developed new functionalities in PROC BGLIMM that enable you to fit the power prior to many models with the simplest setup. This webinar will offer a tutorial, that covers topics in partial borrowing, and choosing of the power weight parameter a0, in single and multiple historical data sets settings. If time permits, we will discuss some recent addition to the general-purpose simulation MCMC procedure, specifically in the PK modelling area.n-nested random effects and fits models to longitudinal data with repeated measurements (balanced or unbalanced). We will illustrate its usage through a number of pharma-related data analysis examples and case studies.
Key Learnings
Computational know-how in modeling hierarchical data using PROC BGLIMM
Efficient and effective ways to analyze data using historical information
Fang Chen manages the development of statistical software for SAS/STAT®, SAS/QC®, and analytical components that drive SAS® Visual Statistics software. Also among his responsibilities are the development of Bayesian analysis software and the MCMC procedure. Before joining SAS, he received his Ph.D. in statistics from Carnegie Mellon University.