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

Med Decis Making. Oct-Dec 1997;17(4):390-401. doi: 10.1177/0272989X9701700404.

Abstract

A framework for quantifying uncertainty about costs, effectiveness measures, and marginal cost-effectiveness ratios in complex decision models is presented. This type of application requires special techniques because of the multiple sources of information and the model-based combination of data. The authors discuss two alternative approaches, one based on Bayesian inference and the other on resampling. While computationally intensive, these are flexible in handling complex distributional assumptions and a variety of outcome measures of interest. These concepts are illustrated using a simplified model. Then the extension to a complex decision model using the stroke-prevention policy model is described.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.
  • Review

MeSH terms

  • Bayes Theorem
  • Cerebrovascular Disorders / economics
  • Cerebrovascular Disorders / prevention & control
  • Computer Simulation
  • Cost-Benefit Analysis
  • Decision Support Techniques*
  • Health Care Costs / statistics & numerical data*
  • Humans
  • Models, Statistical
  • Quality-Adjusted Life Years