Document Detail


Bayesian meta-analyses for comparative effectiveness and informing coverage decisions.
MedLine Citation:
PMID:  20473185     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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. PURPOSE: 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. DATA SYNTHESIS: 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. LIMITATIONS: 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:
Scott M Berry; K Jack Ishak; Bryan R Luce; Donald A Berry
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Medical care     Volume:  48     ISSN:  1537-1948     ISO Abbreviation:  Med Care     Publication Date:  2010 Jun 
Date Detail:
Created Date:  2010-05-20     Completed Date:  2010-06-18     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0230027     Medline TA:  Med Care     Country:  United States    
Other Details:
Languages:  eng     Pagination:  S137-44     Citation Subset:  IM    
Affiliation:
Berry Consultants, College Station, TX 77845, USA. scott@berryconsultants.com
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MeSH Terms
Descriptor/Qualifier:
Bayes Theorem*
Centers for Medicare and Medicaid Services (U.S.) / organization & administration*
Clinical Trials as Topic
Comparative Effectiveness Research / methods*
Defibrillators, Implantable
Humans
Insurance Coverage / organization & administration*
Meta-Analysis as Topic*
United States

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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