Assessing the Sensitivity of Meta-analysis to Selection Bias: A Multiple Imputation Approach

Evidence synthesis, both qualitatively and quantitatively through meta-analysis, is central to the development of evidence-based medicine. Unfortunately, meta-analysis is often complicated by the suspicion that the available studies represent a biased subset of the evidence, possibly due to publication bias or other systematically different effects in small studies. A number of statistical methods have been proposed to address this, among which the trim-and-fill method and the Copas selection model are two of the most widely discussed. However, both methods have drawbacks: the trim-and-fill method is based on strong assumptions about the symmetry of the funnel plot; the Copas selection model is less accessible to systematic reviewers, and sometimes encounters estimation problems. In this article, we adopt a logistic selection model, and show how treatment effects can be rapidly estimated via multiple imputation. Specifically, we impute studies under a missing at random assumption, and then reweight to obtain estimates under nonrandom selection. Our proposal is computationally straightforward. It allows users to increase selection while monitoring the extent of remaining funnel plot asymmetry, and also visualize the results using the funnel plot. We illustrate our approach using a small meta-analysis of benign prostatic hyperplasia.