A0511
Title: Use of a nonparametric Bayesian method to model health state preferences: An application to Lebanese SF-6D valuations
Authors: Samer Kharroubi - American University of Beirut (Lebanon) [presenting]
Abstract: This paper reports on the findings from applying a new approach to modelling health state valuation data. The approach applies a nonparametric model to estimate SF-6D health state utility values using Bayesian methods. The data set is the Lebanon SF-6D valuation study where a sample of 249 states defined by the SF-6D was valued by a representative sample of 577 members of the Lebanese general population using standard gamble. The paper presents the results from applying the nonparametric model and comparing it to the original model estimated using a conventional parametric random effects model. The covariates effect on health state valuations was also reported. The two models are compared theoretically and in terms of empirical performance. The nonparametric Bayesian model is argued to be theoretically more flexible and produces better utility predictions from the SF-6D than previously used classical parametric model. In addition, the Bayesian model is more appropriate to account the covariates effect. Further research is encouraged.
