Seminar Announcement**

*Meta-Analysis Using Dirichlet Process*

*Ram Tiwari*

*Office of Biostatistics*

*Center for Drug Evaluation & Research *

*U.S. Food and Drug Administration*

*Engineering Building, Room 1602*

*Time: 11 am – 12 pm***

*Date: Friday, April 20, 2012 ***


A Bayesian approach for meta-analysis using the Dirichlet process is
presented. The key aspect of the Dirichlet process (DP) in meta-analysis is
the ability to capture the heterogeneity among studies while relaxing the
distributional assumptions. We assume that the study effects are generated
from a Dirichlet process. Under a DP model, the study effects parameters
have support on a discrete space and enable borrowing information from
study to study while facilitating clustering among studies. We evaluate the
performance of the DP approach through simulations and illustrate the
proposed method by applying it to three datasets; one large dataset on
Program for International Student Assessment (PISA) involving 30 countries,
a small dataset from published literature on the treatment of Alzheimer’s
disease and a two-arm clinical trial dataset on preventing mortality after
myocardial infarction. Results from the data analyses, simulation studies,
and log-pseudo marginal likelihood (LPML) model selection procedure
indicate that DP model perform better over conventional alternative methods.