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Bayesian random effects meta‐analysis of trials with binary outcomes: methods for the absolute risk difference and relative risk scales by D. E. Warn, S. G. Thompson and D. J. Spiegelhalter, Statistics in Medicine 2002; 21 : 1601–1623
Author(s) -
O'Rourke Keith,
Altman Douglas G.
Publication year - 2005
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.2115
Subject(s) - bayesian probability , statistics , meta analysis , contrast (vision) , binary data , bayes factor , bayes' theorem , credibility , confidence interval , econometrics , marginal likelihood , mathematics , computer science , binary number , artificial intelligence , medicine , arithmetic , political science , law
In a recent Statistics in Medicine paper, Warn, Thompson and Spiegelhalter (WTS) made a comparison between the Bayesian approach to the meta‐analysis of binary outcomes and a popular Classical approach that uses summary (two‐stage) techniques. They included approximate summary (two‐stage) Bayesian techniques in their comparisons in an attempt undoubtedly to make the comparison less unfair. But, as this letter will argue, there are techniques from the Classical approach that are closer—those based directly on the likelihood—and they failed to make comparisons with these. Here the differences between Bayesian and Classical approaches in meta‐analysis applications reside solely in how the likelihood functions are converted into either credibility intervals or confidence intervals. Both summarize, contrast and combine data using likelihood functions. Conflating what Bayes actually offers to meta‐analysts—a means of converting likelihood functions to credibility intervals—with the use of likelihood functions themselves to summarize, contrast and combine studies is at best misleading. Copyright © 2005 John Wiley & Sons, Ltd.