Modeling SF-6D Health Utilities: Is Bayesian Approach Appropriate?

Background: Valuation studies of preference-based health measures like SF6D have been conducted in many countries. However, the cost conducting such countries with small populations or low- and middle-income (LMICs) can be prohibitive. There is potential to use results from readily available countries’ valuations produce better valuation estimates. Methods: Data Lebanon UK SF-6D value sets were analyzed, where values for 49 249 states extracted samples populations, respectively, using standard gamble techniques. A nonparametric Bayesian model was used estimate a set data as informative priors. The resulting estimates then compared obtained by itself via various prediction criterions. Results: findings permit evidence contribute prior information analysis producing more precise than analyzing only under all criterions used. Conclusions: positive suggest that existing merged another country sets, thereby making own attainable LMICs.