Random effects survival models gave a better understanding of heterogeneity in individual patient data meta-analyses

Abstract

Background and objective: Individual patient data meta-analysis consists in combining data from all available trials dealing with a therapeutic problem in order to increase the power of statistical analyses. A key issue when analyzing these pooled data sets is intertrial heterogeneity. In survival data, heterogeneity manifests itself either by differing treatment effects between the included trials or by a baseline hazard that differs between studies. One way to investigate and accommodate this heterogeneity is to use models that include random effects.

Methods: We apply this class of models to the Meta-Analysis of Chemotherapy in Head and Neck Cancers, in which strong heterogeneity is exhibited. This meta-analysis pooled 63 trials involving 10,741 patients.

Results: We show that such modeling permits a better understanding of heterogeneity in the MACH-NC data, both from a frequentist and from a Bayesian point of view. In particular, the modeling suggests the presence of two outlying sets of trials whose baseline risk could explain the apparent efficacy or inefficacy of some treatment protocols.

Conclusion: We conclude that this family of random-effects models is a useful tool for exploring heterogeneity in meta-analyses of time-to-event data, and that its features can be applied to a very wide range of studies.