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Predicting an SF-6D preference-based score using MCS and PCS scores from the SF-12 or SF-36
1Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI 53726, USA. jehanmer@wisc.edu
Background:
The SF-6D preference-based scoring system was developed several years after the SF-12 and SF-36 instruments. A method to predict SF-6D scores from information in previous reports would facilitate backwards comparisons and the use of these reports in cost-effectiveness analyses.
Methods:
This report uses data from the 2001-2003 Medical Expenditures Panel Survey (MEPS), the Beaver Dam Health Outcomes Survey, and the National Health Measurement Study. SF-6D scores were modeled using age, sex, mental component summary (MCS) score, and physical component summary (PCS) score from the 2002 MEPS. The resulting SF-6D prediction equation was tested with the other datasets for groups of different sizes and groups stratified by age, MCS score, PCS score, sum of MCS and PCS scores, and SF-6D score.
Results:
The equation can be used to predict an average SF-6D score using average age, proportion female, average MCS score, and average PCS score. Mean differences between actual and predicted average SF-6D scores in out-of-sample tests was -0.001 (SF-12 version 1), -0.013 (SF-12 version 2), -0.007 (SF-36 version 1), and -0.010 (SF-36 version 2). Ninety-five percent credible intervals around these point estimates range from +/-0.045 for groups with 10 subjects to +/-0.008 for groups with more than 300 subjects. These results were consistent for a wide range of ages, MCS scores, PCS scores, sum of MCS and PCS scores, and SF-6D scores. SF-6D scores from the SF-36 and SF-12 from the same data set were found to be substantially different.
Conclusions:
Simple equation predicts an average SF-6D preference-based score from widely published information.
