Evolution of statistical models for meta-analysis and implications for best practice

Abstract

Purpose of review Meta-analyses are a common and important component of clinical practice guidelines. Concomitantly, there has been a tremendous increase over the past three decades in the number of published meta-analyses. An important factor in the quality of the results from a meta-analysis rests on selecting the most appropriate pooling model. In this brief review, the evolution of the numerous different pooling models that extend beyond the traditional fixed effect, fixed effects, and random effects models is described, with a focus on estimating between-study variance, that is, heterogeneity. The most recent evidence, including alternative models, is also described and recommendations for model selection and reporting provided. Recent findings In the absence of checking for between-study normality, appropriately conducted simulation studies have found that the IVhet model, a quasi-likelihood approach, may be the best model for pooling results in an aggregate data meta-analysis. Summary The IVhet model is recommended for pooling results for an aggregate data meta-analysis. If there is insistence on a random effects model, the restricted maximum likelihood method along with the Knapp-Hartung adjustment is recommended. A need exists for a large, collaborative, appropriately conducted simulation study that examines which pooling models are best based on the scenario presented.