Objective: The objective is to derive an algorithm to predict a cohort preference-based short form-6D (short form-6D) score using the eight mean health dimension scores from the short form-36 (SF-36) when patient level data are not available.
Methods: Health-related quality of life data (N = 6890) covering a wide range of health conditions was used to explore the relationship between the SF-6D and the eight health dimension scores. Models obtained using ordinary least square regressions were compared for goodness of fit and predictive abilities on both within-sample subgroups and out-of-sample published data sets.
Results: The models explained more than 83% of the variance in the individual SF-6D scores with a mean absolute error of 0.040. When using mean health dimension scores from within-sample subgroups and out-of-sample published data sets, the majority of predicted scores were well within the minimal important difference (0.041) for the SF-6D.
Conclusions: This article presents a mechanism to estimate a mean cohort preference-based SF-6D score using the eight mean health dimension scores of the SF-36. Using published summary statistics, the out-of-sample validation demonstrates that the algorithms can be used to inform both clinical and economic research. Further research is required in different health conditions.