Publication bias and meta-analysis for 2×2 tables: an average Markov chain Monte Carlo EM algorithm

Authors


Address for correspondence : John Copas, Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK.
E-mail: jbc@stats.warwick.ac.uk

Abstract

Summary. A major difficulty in meta-analysis is publication bias. Studies with positive outcomes are more likely to be published than studies reporting negative or inconclusive results. Correcting for this bias is not possible without making untestable assumptions. In this paper, a sensitivity analysis is discussed for the meta-analysis of 2×2 tables using exact conditional distributions. A Markov chain Monte Carlo EM algorithm is used to calculate maximum likelihood estimates. A rule for increasing the accuracy of estimation and automating the choice of the number of iterations is suggested.

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