Modeling a preference-based index for two condition-specific measures (asthma and overactive bladder) using a nonparametric Bayesian method.

BACKGROUND Conventionally, parametric models were used for health state valuation data. Recently, researchers started to explore the use of nonparametric Bayesian methods in this area. OBJECTIVES We present a nonparametric Bayesian model to estimate a preference-based index for two condition-specific five-dimensional health state classifications, one for asthma (five-dimensional Asthma Quality of Life Utility Index) and the other for overactive bladder (five-dimensional Overactive Bladder Quality of Life-Utility Index). METHODS Samples of 307 and 311 members of the UK general population valued 99 health states selected from a total of 3125 health states defined by each of the measures using the time trade-off technique. The article presents the results of the nonparametric model and compares it with the original model estimated using a conventional parametric random-effects model. The different methods are compared theoretically and in terms of empirical performance across the two data sets. It also reports the effect of respondent characteristics on health state valuations. RESULTS The nonparametric models were found to be better at predicting health state values within the estimation sample than without in terms of root mean square error and the patterns of standardized residuals. Some respondent characteristics were found to explain variation in health state values, but these did not have a significant effect on the health states values when estimates were adjusted for sample differences with the general population. CONCLUSIONS The nonparametric Bayesian models are theoretically more appropriate than previously used parametric models and provide better utility estimates from the two condition-specific measures. Furthermore, the model is more flexible in estimating the effect of covariates.