Predicting preference-based SF-6D index scores from the SF-8 health survey

Abstract

Purpose: To develop and test functions for predicting the preference-based SF-6D index scores from the SF-8 health survey. Methods: This study was a secondary analysis of data collected in a population health survey in which respondents (n = 7,529) completed both the SF-36 and the SF-8 questionnaires. We examined seven ordinary least-square estimators for their performance in predicting SF-6D scores from the SF-8 at both the individual and the group levels. Results: In general, all functions performed similarly well in predicting SF-6D scores, and the predictions at the group level were better than predictions at the individual level. At the individual level, 42.5-51.5 % of prediction errors were smaller than the minimally important difference (MID) of the SF-6D scores, depending on the function specifications, while almost all prediction errors of the tested functions were smaller than the MID of SF-6D at the group level. At both individual and group levels, the tested functions predicted lower than actual scores at the higher end of the SF-6D scale. Conclusions: Our study developed functions to generate preference-based SF-6D index scores from the SF-8 health survey, the first of its kind. Further research is needed to evaluate the performance and validity of the prediction functions. © 2012 Springer Science+Business Media Dordrecht.