Fall Seminar Series - December 2, 2004
University of Minnesota
School of Statistics
College of Liberal Arts
A
Bayesian Semi-Parametric Model for Random Effects Meta-Analysis
Hani Doss
Department of Statistics
Ohio State University
Thursday, December 2, 2004
3:30 PM, 115
Ford Hall
Minneapolis, East Bank Campus
Social at 3:00 PM, 300 Ford Hall
Abstract
In
meta-analysis there is an increasing trend to explicitly
acknowledge the presence of study variability through random effects
models. That is, one assumes that for each study, there is a
study-specific effect and one is observing an estimate of this
latent variable. In a random effects model, one assumes that these
study-specific effects come from some distribution, and one can
estimate the parameters of this distribution, as well as the
study-specific effects themselves. This distribution is most often
modelled through a parametric family, usually a family of normal
distributions. The advantage of using a normal distribution is that
the mean parameter plays an important role, and much of the focus is
on determining whether or not this mean is 0. For example, it may
be easier to justify funding further studies if it is determined
that this mean is not 0. Typically, this normality assumption is
made for the sake of convenience, rather than from some theoretical
justification, and may not actually hold. We present a Bayesian
model in which the distribution of the study-specific effects is
modelled through a certain class of nonparametric priors. These
priors can be designed to concentrate most of their mass around the
family of normal distributions, but still allow for any other
distribution. The priors involve a univariate parameter that plays
the role of the mean parameter in the normal model. This work arose
from a problem in cardiology, which I will discuss and use to
illustrate the methodology.
This is joint work with Deborah Burr.