Bayesian random-effects meta-analysis of aggregate data on clinical events

Abstract

To investigate intervention effects on rare events, meta-analysis techniques are commonly applied in order to assess the accumulated evidence. When it comes to adverse effects in clinical trials, these are often most adequately handled using survival methods. A common-effect model that is able to process data in commonly quoted formats in terms of hazard ratios has been proposed for this purpose. In order to accommodate potential heterogeneity between studies, we have extended the model by Holzhauer to a random-effects approach. The Bayesian model is described in detail, and applications to realistic data sets are discussed along with sensitivity analyses and Monte Carlo simulations to support the conclusions.

23 pages, 8 figures

References in corpus (10)

• A weakly informative default prior distribution for logistic and other regression models
• Rank-normalization, folding, and localization: An improved $\hat{R}$ for assessing convergence of MCMC
• Bayesian random-effects meta-analysis using the bayesmeta R package
• Meta-analysis of few small studies in orphan diseases
• On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis
• Likelihood-based meta-analysis with few studies: Empirical and simulation studies
• Using the bayesmeta R package for Bayesian random-effects meta-regression
• Detecting and diagnosing prior and likelihood sensitivity with power-scaling
• Summarizing empirical information on between-study heterogeneity for Bayesian random-effects meta-analysis
• Bounds for the weight of external data in shrinkage estimation