Robust Bayesian methods for monitoring clinical trials

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 subjecti...

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Bibliographic Details
Published inStatistics in medicine Vol. 14; no. 12; p. 1379
Main Authors Greenhouse, J B, Wasserman, L
Format Journal Article
LanguageEnglish
Published England 30.06.1995
Subjects
Bayes Theorem
Clinical Trials as Topic - statistics & numerical data
Data Collection
Data Interpretation, Statistical
Extracorporeal Membrane Oxygenation - statistics & numerical data
Fluorouracil - administration & dosage
Fluorouracil - adverse effects
Granulocyte Colony-Stimulating Factor - administration & dosage
Humans
Infant, Newborn
Leucovorin - administration & dosage
Leucovorin - adverse effects
Leukopenia - chemically induced
Leukopenia - prevention & control
Likelihood Functions
Neoplasms - drug therapy
Persistent Fetal Circulation Syndrome - mortality
Persistent Fetal Circulation Syndrome - therapy
Stomatitis - chemically induced
Stomatitis - prevention & control
Survival Rate
Treatment Outcome
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Summary: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.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.4780141210