Background: . When data on preferences are not available, analysts rely on condition-specific or generic measures of health status like the SF-12 for predicting or mapping preferences. Such prediction is challenging because of the characteristics of preference data, which are bounded, have multiple modes, and have a large proportion of observations clustered at values of 1.
Methods: . We developed a finite mixture model for cross-sectional data that maps the SF-12 to the EQ-5D-3L preference index. Our model characterizes the observed EQ-5D-3L index as a mixture of 3 distributions: a degenerate distribution with mass at values indicating perfect health and 2 censored (Tobit) normal distributions. Using estimation and validation samples derived from the Medical Expenditure Panel Survey 2000 dataset, we compared the prediction performance of these mixture models to that of 2 previously proposed methods: ordinary least squares regression (OLS) and two-part models.
Results: . Finite mixture models in which predictions are based on classification outperform two-part models and OLS regression based on mean absolute error, with substantial improvement for samples with fewer respondents in good health. The potential for misclassification is reflected on larger root mean square errors. Moreover, mixture models underperform around the center of the observed distribution.
Conclusions: . Finite mixtures offer a flexible modeling approach that can take into account idiosyncratic characteristics of the distribution of preferences. The use of mixture models allows researchers to obtain estimates of health utilities when only summary scores from the SF-12 and a limited number of demographic characteristics are available. Mixture models are particularly useful when the target sample does not have a large proportion of individuals in good health.
Keywords: EQ-5D; SF-12; Tobit; health-related quality of life; mapping; mixture models; prediction.
© The Author(s) 2015.