Premium
Bayesian estimation of cost‐effectiveness: an importance‐sampling approach
Author(s) -
Heitjan Daniel F.,
Li Huiling
Publication year - 2004
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.825
Subject(s) - bayes' theorem , posterior probability , bayesian probability , statistics , sampling (signal processing) , computer science , sample size determination , econometrics , mathematics , filter (signal processing) , computer vision
We describe a method for estimating the cost‐effectiveness of a new treatment compared to a standard, using data from a comparative clinical trial. We quantify the clinical effectiveness as a binary variable indicating success or failure. The underlying statistical model assumes that costs are uncensored and follow separate gamma distributions in each of the groups defined by the four possible combinations of treatment arm and effectiveness outcome. The method is subjectivist, in that it represents prior uncertainty about model parameters with a probability distribution, which we update via Bayes's theorem to produce a posterior distribution. We approximate the posterior by importance sampling, a straightforward simulation method. We illustrate the method with an analysis of cost (derived from resource usage data) and effectiveness (measured by one‐year survival) in a clinical trial in heart disease. The example demonstrates that the method is practical and provides for a flexible data analysis. Copyright © 2003 John Wiley & Sons, Ltd.