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Welcome to the Bayesian Statistical Science Section

The Bayesian Statistical Science Section of the ASA provides a forum for statisticians and people who have interest in the Bayesian paradigm. The broad objectives of the Section are: to encourage research on theory and methods of statistical inference and decisionmaking associated with Bayes' theorem and to encourage the application and proper use of Bayesian procedures in the behavioral, biological, managerial, engineering, environmental, legal, medical, pharmaceutical, physical, and social sciences.

Current Affairs 

Upcoming Web-Based Lectures

Title: Using SAS and LaTeX to Create Documents with Reproducible Results
Presenter: Tim Arnold, SAS Institute, Inc.
Date and Time: Thursday, June 4, 2015, 11:00 a.m. - 12:30 p.m. Eastern time
Sponsor: Section for Statistical Programmers and Analysts

Registration Deadline: Tuesday, June 2, at 12:00 p.m. Eastern time

This webinar will describe the StatRep system for reproducible research. The StatRep system uses SAS and the LaTeX typesetting system to create documents with reproducible results. It consists of a LaTeX package, a suite of SAS macros and a user guide. The LaTeX package provides two environments and two tags that work together to display your SAS code and results and to generate the SAS program that produces those results. The generated SAS program includes calls to the StatRep SAS macros that use the SAS Output Delivery System (ODS) document to capture the output as external files. With the StatRep system, you can share your LaTeX document with colleagues and be sure that your results are reproducible. 


Title: Propensity Score Methods for Estimating Causal Effects in Pharmaceutical Research
Presenter: Elizabeth Stuart, Johns Hopkins Bloomberg School of Public Health
Date and Time: Wednesday, June 24, 2015, 12:00 p.m. - 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Monday, June 22, at 12:00 p.m. Eastern time

Propensity scores are an increasingly common tool for estimating the effects of interventions in observational ("non-experimental") settings and for answering complex questions in randomized controlled trials. They can be of great use in pharmaceutical and health services research, for example helping assess broad population effects of drugs, devices, or biologics already on the market, especially investigating post-marketing safety outcomes, or for answering questions regarding the outcomes of long-term use using claims data. This webinar will discuss the importance of the careful design of observational studies, and the role of propensity scores in that design, with the main goal of providing practical guidance on the use of propensity scores to estimate causal effects. The webinar will briefly cover the primary ways of using propensity scores to adjust for confounders when estimating the effect of a particular "cause" or "intervention," including weighting, subclassification, and matching. Topics covered will include how to specify and estimate the propensity score model, selecting covariates to include in the model, diagnostics, and common challenges and solutions. Software for implementing analyses using propensity scores will also be briefly discussed. The webinar will also highlight recent advances in the propensity score literature, with a focus on topics particularly relevant for pharmaceutical contexts, including prognostic scores, covariate balancing propensity scores, methods for non-binary treatments (such as dosage levels of a drug or when comparing multiple drugs, devices, or biologics simultaneously), and approaches to be used when there are large numbers of covariates available (as in claims data).

*** More information and registration at the webinar page.

From the SBSS Mixer at JSM 2014



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