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.
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.