Saman Muthukumarana

Date: | Thursday, March 23, 2017 |
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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.

Important Date

October 5 – October 6: Fall Term break (No classes)

Upcoming Seminars

PIMS lecture:
**Robert Serfling**:
“Depth Functions in Multivariate & Other Data Settings: Concepts, Perspectives, Challenges”
—
Thursday, September 28 at 4 p.m.,
Robert Schultz Theatre.

Statistics seminar:
**Zeinab Mashreghi**:
“Resampling procedures in the presence of missing data applied to high entropy sampling designs”
—
Thursday, October 12 at 2:45 p.m.,
301 Biological Sciences.

Statistics seminar:
**Francis Zwiers** —
Thursday, October 19 at 2:45 p.m.,
301 Biological Sciences.

PIMS lecture:
**Melania Alvarez** —
Thursday, October 26 at 4 p.m.,
Robert Schultz Theatre.

Where are they now?

Asma Alavi, M.Sc (1996)

Chel Hee Lee, M.Sc. (2009)