Methods for Using Aggregate Historical Control Data in Meta-Analyses of Clinical Trials With Time-to-Event Endpoints

Abstract

This article deals with comparing a test with a control therapy using meta-analyses of data from randomized controlled trials with a time-to-event endpoint. Such analyses can often benefit from prior information about the distribution of control group outcomes. One possible source of this information is the published aggregate data about control groups of historical trials from the medical literature. We review methods for making posterior inference about exponentially distributed event times more robust to prior-data conflicts by discounting the prior information based on the extent of observed prior-data conflict. We use simulations to compare analyses without prior information with the meta-analytic combined, meta-analytic predictive and robust meta-analytic predictive approaches, as well as Bayesian model averaging using shrinkage priors. Bayesian model averaging via shrinkage priors with well-chosen hyperpriors performed best in terms of credible interval coverage and mean-squared error across scenarios. For the robust meta-analytic predictive approach, there was little benefit in increasing the weight of the informative mixture components beyond 0.2–0.5. This was the case even when little prior-data conflict was expected, except with very sparse data or substantial between-trial heterogeneity in control group hazard rates. Supplementary materials for this article are available online.

KEYWORD:

• Bayesian model averaging
• Rare events
• Robust meta-analytic predictive prior
• Safety meta-analysis
• Shrinkage priors

Acknowledgments

The author is indebted to Rainer Schwabe and Ekkehard Glimm for discussions and advice on his research, as well as Heinz Schmidli and two anonymous reviewers for comments on this article.