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Bayesian estimation, simulation and uncertainty analysis: the cost‐effectiveness of ganciclovir prophylaxis in liver transplantation
Author(s) -
Vanness David J.,
Ray Kim W.
Publication year - 2002
Publication title -
health economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.55
H-Index - 109
eISSN - 1099-1050
pISSN - 1057-9230
DOI - 10.1002/hec.739
Subject(s) - markov chain monte carlo , parametric statistics , bayesian probability , econometrics , computer science , inference , statistics , mathematics , artificial intelligence
This paper demonstrates the usefulness of combining simulation with Bayesian estimation methods in analysis of cost‐effectiveness data collected alongside a clinical trial. Specifically, we use Markov Chain Monte Carlo (MCMC) to estimate a system of generalized linear models relating costs and outcomes to a disease process affected by treatment under alternative therapies. The MCMC draws are used as parameters in simulations which yield inference about the relative cost‐effectiveness of the novel therapy under a variety of scenarios. Total parametric uncertainty is assessed directly by examining the joint distribution of simulated average incremental cost and effectiveness. The approach allows flexibility in assessing treatment in various counterfactual premises and quantifies the global effect of parametric uncertainty on a decision‐maker's confidence in adopting one therapy over the other. Copyright © 2002 John Wiley & Sons, Ltd.