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

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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.

Keywords: 2×2 table; EM algorithm; Funnel plots; Markov chain Monte Carlo methods; Meta-analysis; Metropolis–Hastings algorithm; Publication bias; Sensitivity analysis

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/1467-9868.00334

Publication date: May 1, 2002

bpl/rssb/2002/00000064/00000002/art00005
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