Confidence intervals for random effects meta-analysis and robustness to publication bias

Abstract

The DerSimonian-Laird confidence interval for the average treatment effect in meta-analysis is widely used in practice when there is heterogeneity between studies. However, it is well known that its coverage probability (the probability that the interval actually includes the true value) can be substantially below the target level of 95 per cent. It can also be very sensitive to publication bias. In this paper, we propose a new confidence interval that has better coverage than the DerSimonian-Laird method, and that is less sensitive to publication bias. The key idea is to note that fixed effects estimates are less sensitive to such biases than random effects estimates, since they put relatively more weight on the larger studies and relatively less weight on the smaller studies. Whereas the DerSimonian-Laird interval is centred on a random effects estimate, we centre our confidence interval on a fixed effects estimate, but allow for heterogeneity by including an assessment of the extra uncertainty induced by the random effects setting. Properties of the resulting confidence interval are studied by simulation and compared with other random effects confidence intervals that have been proposed in the literature. An example is briefly discussed.