title: Bayesian estimation of cost-effectiveness ratios from clinical trials.
abstract: Estimation of the incremental cost-effectiveness ratio (ICER) is difficult for several reasons: treatments that decrease both cost and effectiveness and treatments that increase both cost and effectiveness can yield identical values of the ICER; the ICER is a discontinuous function of the mean difference in effectiveness; and the standard estimate of the ICER is a ratio. To address these difficulties, we have developed a Bayesian methodology that involves computing posterior probabilities for the four quadrants and separate interval estimates of ICER for the quadrants of interest. We compute these quantities by simulating draws from the posterior distribution of the cost and effectiveness parameters and tabulating the appropriate posterior probabilities and quantiles. We demonstrate the method by re-analysing three previously published clinical trials.
authors.affiliation: Division of Biostatistics, International Center for Health Outcomes and Innovation Research, Columbia University, New York, NY 10032, USA. dfh5@columbia.edu
authors.forename: D F
authors.initials: DF
authors.lastname: Heitjan
authors.forename: A J
authors.initials: AJ
authors.lastname: Moskowitz
authors.forename: W
authors.initials: W
authors.lastname: Whang
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000191:economics / Q000706:statistics & numerical data*; D003362:Cost-Benefit Analysis / Q000379:methods*; D004341:Drug Evaluation / Q000191:economics; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006302:Health Services Research / Q000191:economics / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D017059:Models, Econometric; D026712:Value of Life