Saman Muthukumarana

University of Manitoba, Department of Statistics

“Meta-analytic Methods using Dirichlet Process”

Date: Thursday, March 23, 2017

In many scientific disciplines, it is common to find large number of studies addressing the same research question of interest. Meta-analysis can be used for combining or contrasting the results from these multiple studies. We develop a Bayesian approach for meta-analysis using Dirichlet process. The key aspect of the Dirichlet process in meta-analysis is the ability to assess the evidence of statistical heterogeneity in the underlying effects across studies while relaxing the distributional assumptions. Assuming that the study effects are generated from a Dirichlet process, the study effects parameters have support on a discrete space and enable borrowing of information across studies while facilitating clustering among studies. We also extend the approach for binary data in the presence of excessive zeros and propose a modified unconditional odds ratio which accounts for excessive zeros. Results from the data analyses, simulation studies, and the log-pseudo marginal likelihood (LPML) model selection procedure indicate that the proposed models perform better than conventional alternative methods. Some extensions to network meta-analysis will also be discussed.

Graduations

Congratulations to our May, 2017 graduate: Dianwei Huang (M.Sc.).

Important Date

June 5 – June 9: Convocation

Upcoming Exams

STAT 1000 A01 Midterm
Wednesday, May 24 at 8:30 a.m.

STAT 1000 A02 Midterm
Wednesday, May 24 at 5:00 p.m.

Where are they now?

Chel Hee Lee, M.Sc. (2009)

Leo Odongo, Ph.D. (1993)