Exploring Heterogeneity in Randomized Trials Via Meta-Analysis

Abstract

Meta-analysis of clinical trials with heterogeneous results provides an opportunity to learn a great deal about variations in treatment effectiveness. Rather than computing a single summary estimate of a series of trials, it may be more informative to explore the effect that different study characteristics may make on treatment efficacy. Regression analysis offers a tool for these analyses. This paper outlines and applies hierarchical Bayesian models for this purpose, presenting two examples of meta-regression using summary data, in one of which results are compared with those from analysis of complete individual patient data. When covariates are not readily available, the event rate in the control group can become a surrogate covariate. An empirical study of 115 meta-analyses shows that this control rate is significantly correlated with the odds ratio about 15% of the time. This suggests that investigators should search for the causes of heterogeneity related to patient characteristics and treatment protocols to determine when treatment is most beneficial and that they should plan to study this heterogeneity in clinical trials.