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

Journal Article (Review)

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.

Full Text

Duke Authors

Cited Authors

  • Parmigiani, G; Samsa, GP; Ancukiewicz, M; Lipscomb, J; Hasselblad, V; Matchar, DB

Published Date

  • October 1997

Published In

Volume / Issue

  • 17 / 4

Start / End Page

  • 390 - 401

PubMed ID

  • 9343797

Pubmed Central ID

  • 9343797

International Standard Serial Number (ISSN)

  • 0272-989X

Digital Object Identifier (DOI)

  • 10.1177/0272989X9701700404


  • eng

Conference Location

  • United States