Mapping MOS Sleep Scale scores to SF-6D utility index.
Author(s): Yang M, Dubois D, Kosinski M, Sun X, Gajria K
Affiliation(s): QualityMetric Incorporated, 640 George Washington Highway, Lincoln, RI 02865, USA.
Publication date & source: 2007-09, Curr Med Res Opin., 23(9):2269-82.
Publication type: Research Support, Non-U.S. Gov't
OBJECTIVE: Deriving preference scores for the Medical Outcomes Study (MOS) Sleep Scale would enable its use in cost-utility analyses. The objective of this study was to map scores of the MOS Sleep Scale to a preference-based health-state utility index (SF-6D) scored from the SF-36 Health Survey (SF-36). RESEARCH DESIGN AND METHODS: Three datasets were used: (1) the MOS study, a 4-year observational study of chronically ill patients, (2) a 7-week open-label, non-comparative clinical trial of an osmotic controlled-release oral delivery system (OROS) hydromorphone in the treatment of chronic low back pain (CLBP), and (3) a 6-week open-label randomized controlled trial of OROS hydromorphone in the treatment of pain associated with chronic osteoarthritis (OA). Various models were tested, where SF-6D was regressed onto the Sleep Problem Index-II (SLP9) in 1000 random half (developmental) samples of the MOS (n = 1413). The best fitting model was applied to the other 1000 random half (cross-validation) samples of the MOS (n = 1412), and to the two trial samples (n = 199 in the CLBP trial; n = 124 in the OA trial). RESULTS: The best fitting model in the MOS samples included a quadratic term for the SLP9 which explained 34% of the variance in SF-6D in the developmental samples. Errors in prediction were greatest at higher SLP9 scores. Addition of demographic and clinical variables to the model explained minimal incremental amounts of variance (< 5%) in SF-6D scores. These results were replicated in the cross-validation MOS samples. In both developmental and cross-validation MOS samples, mean predicted and observed SF-6D scores were nearly identical. When the mapping algorithm developed in the MOS was applied to the CLBP sample, mean predicted SF-6D scores were 0.09 points higher than observed SF-6D scores at both baseline and final visits, while changes in predicted and observed SF-6D scores were identical. CONCLUSION: Results indicate that it is possible to map MOS SLP9 to SF-6D yielding useable preference-based scores essential for cost-utility analyses. A limitation concerns the interpretation of SF-6D scores estimated from SLP9 scores above 60, where the prediction errors increased considerably.