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Bayesian inference for randomized clinical trials with treatment failures
Author(s) -
Shaffer Michele L.,
Chinchilli Ver M.
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1726
Subject(s) - counterfactual thinking , bayesian probability , randomization , randomized controlled trial , clinical trial , computer science , inference , missing data , set (abstract data type) , treatment and control groups , bayesian inference , medicine , statistics , psychology , artificial intelligence , machine learning , mathematics , surgery , social psychology , pathology , programming language
Abstract During the course of a clinical trial, subjects may experience treatment failure. For ethical reasons, it is necessary to administer emergency or rescue medications for such subjects. However, the rescue medications may bias the set of response measurements. This bias is of particular concern if a subject has been randomized to the control group, and the rescue medications improve the subject's condition. The standard approach to analysing data from a clinical trial is to perform an intent‐to‐treat (ITT) analysis, wherein the data are analysed according to treatment randomization. Supplementary analyses may be performed in addition to the ITT analysis to account for the effect of treatment failures and rescue medications. A Bayesian, counterfactual approach, which uses the data augmentation (DA) algorithm, is proposed for supplemental analysis. A simulation study is conducted to compare the operating characteristics of this procedure with a likelihood‐based, counterfactual approach based on the EM algorithm. An example from the Asthma Clinical Research Network (ACRN) is used to illustrate the Bayesian procedure. Copyright © 2004 John Wiley & Sons, Ltd.