M Henmi, JB Copas and S Eguchi
Confidence Intervals and P-valves for Meta Analysis with Publication Bias
Abstract: Summary: We study publication bias in meta analysis by supposing there is a population (y, σ) of studies which give treatment effect estimates y ~ N(θ, σ2). A selection function describes the probability that each study is selected for review. The overall estimate of θ depends on the studies selected, and hence on the (unknown) selection function. Our previous paper, Copas and Jackson (2004, A bound for publication bias based on the fraction of unpublished studies, Biometrics 60, 146-153), studied the maximum bias over all possible selection functions which satisfy the weak condition that large studies (small σ) are as likely, or more likely, to be selected than small studies (large σ). This led to a worstcase sensitivity analysis, controlling for the overall fraction of studies selected. However, no account was taken of the effect of selection on the uncertainty in estimation. This paper extends the previous work by finding corresponding confidence intervals and P-values, and hence a new sensitivity analysis for publication bias. Two examples are discussed.
Keywords: Publication bias; Selection model; Sensitivity analysis; Unpublished studies.