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BAYESIAN COST-EFFECTIVENESS ANALYSIS

An Example Using the GUSTO Trial

Published online by Cambridge University Press:  25 May 2001

Dennis G. Fryback ,
James O. Chinnis  and
Jacob W. Ulvila
Dennis G. Fryback
Affiliation:
University of Wisconsin-Madison
James O. Chinnis
Affiliation:
Decision Science Associates, Inc.
Jacob W. Ulvila
Affiliation:
Decision Science Associates, Inc.
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Abstract

A desirable element of cost-effectiveness analysis (CEA) modeling is a systematic way to relate uncertainty about input parameters to uncertainty in the computational results of the CEA model. Use of Bayesian statistical estimation and Monte Carlo simulation provides a natural way to compute a posterior probability distribution for each CEA result. We demonstrate this approach by reanalyzing a previously published CEA evaluating the incremental cost-effectiveness of tissue plasminogen activator compared to streptokinase for thrombolysis in acute myocardial infarction patients using data from the GUSTO trial and other auxiliary data sources. We illustrate Bayesian estimation for proportions, mean costs, and mean quality-of-life weights. The computations are performed using the Bayesian analysis software WinBUGS, distributed by the MRC Biostatistics Unit, Cambridge, England.

Keywords

Bayes theoremCost-effectiveness analysisSensitivity analysis
Type
Research Article
Information
International Journal of Technology Assessment in Health Care , Volume 17 , Issue 1 , January 2001 , pp. 83 - 97
Copyright
© 2001 Cambridge University Press

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