Bayesian approaches for comparative effectiveness research

Abstract

A hallmark of comparative effectiveness research is the analysis of all the available evidence from different studies addressing a given question of medical risk versus benefit. The Bayesian statistical approach is ideally suited for such investigations because it is inherently synthetic and because it is philosophically uninhibited regarding the ability to analyze all the available evidence. To consider a variety of comparative effectiveness research settings and show how the Bayesian approach applies. The Bayesian approach is described as it has been applied to the comparative analysis of implantable cardioverter defibrillators and mammographic screening, in the Cancer Intervention and Surveillance Modeling Network, in comparisons of patient outcomes data from different sources, and in designing adaptive clinical trials to support the development of 'personalized medicine.' Bayesian methods allow for continued learning as data accrue and for cumulating meta-analyses and the comparison of heterogeneous studies. Bayesian methods enable predictive probability distributions of the results of future studies. Bayesian posterior distributions are subject to potential bias - in the selection of 'available' evidence and in the choice of a likelihood model. Sensitivity analyses help to control this bias. The Bayesian approach has much to offer comparative effectiveness research. It provides a mechanism for synthesizing various sources of information and for updating knowledge in an online fashion as evidence accumulates.