title: Estimating the Bayesian loss function. A conjoint analysis approach.
abstract: Current health economic literature does not provide clear guidelines on how uncertainty around cost-effectiveness estimates should be incorporated into economic decision models. Bayesian analysis is a promising alternative to classical statistics for incorporating uncertainty in economic analysis. Estimating a loss function that relates outcomes to societal welfare is a key component of Bayesian decision analysis. Health economists commonly compute the loss function based on the quality-adjusted life-years associated with each outcome. However, if welfare economics is adopted as the theoretical foundation of the analysis, a loss function based in cost-benefit analysis (CBA) may be more appropriate. CBA has not found wide use in health economics due to practical issues associated with estimating such a loss function. In this paper, we present a method based in conjoint analysis for estimating the CBA loss function that can be applied in practice. We illustrate the use of the methodology using data from a pilot study.
authors.forename: M V
authors.initials: MV
authors.lastname: Bala
authors.forename: J
authors.initials: J
authors.lastname: Mauskopf
mesh_terms: D000208:Acute Disease; D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D003258:Consumer Behavior; D003657:Decision Making; D015276:Health Care Rationing / Q000191:economics*; D006801:Humans; D017059:Models, Econometric; D009203:Myocardial Infarction / Q000188:drug therapy / Q000191:economics; D010865:Pilot Projects; D019057:Quality-Adjusted Life Years