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Robust Bayesian Methods for Monitoring Clinical Trials
Joel B. Greenhouse
and
Larry Wasserman
Abstract:
Bayesian methods for the analysis of clinical trials data have received
increasing attention recently as they offer an approach for dealing
with difficult problems that arise in practice. A major criticism of
the Bayesian approach, however, has focused on the need to specify a
single, often subjective, prior distribution for the parameters of
interest. In an attempt to address this criticism, we describe
methods for assessing the robustness of the posterior distribution to
the specification of the prior. The robust Bayesian approach to data
analysis replaces the prior distribution with a class of prior
distributions and investigates how the inferences might change as the
prior varies over this class. The purpose of this paper is to
illustrate the application of robust Bayesian methods to the analysis
of clinical trials data. Using two examples of clinical trials taken
from the literature, we illustrate how to use these methods to help a
data monitoring committee decide whether or not to stop a trial
early.
Key words: Clinical trials; Data monitoring committee;
-contaminated class; Prior distribution; Robust Bayesian
inference; Sensitivity analysis.
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