Bayesian modeling of health state preferences: can existing preference data be used to generate better estimates?
64th ISI World Statistics Congress - Ottawa, Canada
Format: CPS Abstract
Session: CPS 23 - Bayesian statistics
Monday 17 July 4 p.m. - 5:25 p.m. (Canada/Eastern)
Background: Valuations of preference-based measure such as EQ-5D or SF6D have been conducted in different countries because social and cultural differences are likely to lead to systematically different valuations. However, there is a scope to make use of the results in one country as informative priors to help with the analysis of a study in another, for this to enable better estimation to be obtained in the new country than analyzing its data separately.
Methods: Data from two SF-6D valuation studies were analyzed where, using similar standard gamble protocols, values for 249 common health states were devised from representative samples of the Lebanon and UK general adult populations, respectively. A nonparametric Bayesian model was applied to estimate a Lebanon value set, where the UK results were used as informative priors. Generated estimates were compared to a Lebanon value set estimated using Lebanon values alone using different prediction criterion, including predicted versus actual mean health states valuations, mean predicted error, root mean square error and out of sample leave one out prediction.
Results: The novel method of modelling utility functions permitted the UK valuations to contribute significant prior information to the Lebanon analysis. The results suggest that using Lebanon data alongside the existing UK data produces Lebanon utility estimates better than would have been possible using the Lebanon study data by itself.
Conclusion: The promising results suggest that the existing preference data could be combined with data from a valuation study in a new country to generate preference weights, thus making own country value sets more achievable for low–middle income countries. Further research and application to other countries and preference-based measures are encouraged.