title: Bayesian meta-analyses for comparative effectiveness and informing coverage decisions.
abstract: BACKGROUND
  Evidence-based medicine is increasingly expected in health care decision-making. The Centers for Medicare and Medicaid have initiated efforts to understand the applicability of Bayesian techniques for synthesizing evidence. As a case study, a Bayesian analysis of clinical trials of implantable cardioverter defibrillators was undertaken using patient-level data not typically available for analysis.
  
  
  OBJECTIVE
  Conduct Bayesian meta-analyses of the defibrillator trials using published results to demonstrate a Bayesian approach useful to policy makers. DATA SOURCES, STUDY SELECTION, DATA EXTRACTION: We reconsidered trials in a 2007 systematic review by Ezekowitz et al (Ann Intern Med. 2007;147:251-262) and extracted information from the original published articles. Employing a Bayesian hierarchical approach, we developed a base model and 2 variants, and modeled hazard ratios separately within each year of follow-up. We considered sequential meta-analyses over time and found the predictive distribution of the results of the next trial, given its sample size.
  
  
  RESULTS
  For the most robust of 3 models, the probability that the mean defibrillator effect (in the population of trials) is beneficial is greater than 0.999. In that model, about 5% of trials in the population of trials would have a detrimental effect. Despite the moderate amount of heterogeneity across the trials, there was stability of conclusions after the first 3 of the 12 total trials had been conducted. This stability enabled reasonable predictions for the results of future trials.
  
  
  CONCLUSIONS
  Inability to assess treatment effects within subsets of patients.
  
  
  CONCLUSIONS
  Bayesian meta-analyses based on literature surveys can effectively inform coverage decisions. Bayesian modeling for endpoints such as mortality can elucidate treatment effects over time. The Bayesian approach used in a sequential manner over time can predict results and help assess the utility of future clinical trials.
authors.affiliation: Berry Consultants, College Station, TX 77845, USA. scott@berryconsultants.com
authors.forename: Scott M
authors.initials: SM
authors.lastname: Berry
authors.forename: K Jack
authors.initials: KJ
authors.lastname: Ishak
authors.forename: Bryan R
authors.initials: BR
authors.lastname: Luce
authors.forename: Donald A
authors.initials: DA
authors.lastname: Berry
mesh_terms: D001499:Bayes Theorem; D014487:Centers for Medicare and Medicaid Services, U.S. / Q000458:organization & administration*; D002986:Clinical Trials as Topic; D057186:Comparative Effectiveness Research / Q000379:methods*; D017147:Defibrillators, Implantable; D006801:Humans; D019458:Insurance Coverage / Q000458:organization & administration*; D015201:Meta-Analysis as Topic; D014481:United States