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Assessing uncertainty in cost-effectiveness analyses: application to a complex decision model

G Parmigiani1, G P Samsa, M Ancukiewicz

  • 1Institute of Statistics and Decision Sciences, Duke University, Durham, NC 27705, USA.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|October 31, 1997
PubMed
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This study presents methods for quantifying uncertainty in complex health economic models. It details Bayesian inference and resampling techniques for cost-effectiveness analysis, aiding policy decisions.

Area of Science:

  • Health economics
  • Decision analysis
  • Biostatistics

Background:

  • Complex decision models are crucial for health policy but quantifying uncertainty is challenging.
  • Multiple data sources and model-based data integration require specialized uncertainty quantification techniques.

Purpose of the Study:

  • To present a framework for quantifying uncertainty in costs, effectiveness, and cost-effectiveness ratios within complex decision models.
  • To explore and compare Bayesian inference and resampling as approaches for uncertainty quantification.

Main Methods:

  • Discusses two primary methods: Bayesian inference and resampling (e.g., bootstrapping).
  • Illustrates concepts with a simplified model.
  • Applies the framework to a complex stroke-prevention policy model.

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Main Results:

  • Both Bayesian inference and resampling offer flexible approaches to handle complex distributional assumptions and various outcome measures.
  • These methods, though computationally intensive, provide robust uncertainty quantification for decision models.

Conclusions:

  • The presented framework and methods are applicable to complex health economic evaluations.
  • Effective uncertainty quantification is essential for reliable decision-making in health policy using complex models.