Bayesian methods for evidence synthesis in cost-effectiveness analysis
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Recently, health systems internationally have begun to use cost-effectiveness research as formal inputs into decisions about which interventions and programmes should be funded from collective resources. This process has raised some important methodological questions for this area of research. This paper considers one set of issues related to the synthesis of effectiveness evidence for use in decision-analytic cost-effectiveness (CE) models, namely the need for the synthesis of all sources of available evidence, although these may not ‘fit neatly’ into a CE model.
Commonly encountered problems include the absence of head-to-head trial evidence comparing all options under comparison, the presence of multiple endpoints from trials and different follow-up periods. Full evidence synthesis for CE analysis also needs to consider treatment effects between patient subpopulations and the use of nonrandomised evidence.
Bayesian statistical methods represent a valuable set of analytical tools to utilise indirect evidence and can make a powerful contribution to the decision-analytic approach to CE analysis. This paper provides a worked example and a general overview of these methods with particular emphasis on their use in economic evaluation.
KeywordsMarkov Chain Monte Carlo Pelvic Inflammatory Disease Evidence Synthesis Relative Treatment Effect Mixed Treatment Comparison
Tony Ades and Mark Sculpher receive funding from the UK Medical Research Council as part of the Health Services Research Collaboration. Mark Sculpher is also funded through a Public Health Career Scientist Award from the UK NHS Research and Development Programme.
The authors have no conflicts of interest.
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