Conclusions The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions
Background Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values ( HSUVs ) from data obtained from another health-related quality of life ( HRQoL ) measure .
There have been several method ological developments in this area since a previous review up to 2007 .
Mapping MOS Sleep Scale scores to SF‑6D utility index
ABSTRACT 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 data sets 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 r and omized 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 r and om half ( developmental ) sample s of the MOS ( n = 1413 ) . The best fitting model was applied to the other 1000 r and om half ( cross-validation ) sample s of the MOS ( n = 1412 ) , and to the two trial sample s ( n = 199 in the CLBP trial ; n = 124 in the OA trial ) . Results : The best fitting model in the MOS sample s included a quadratic term for the SLP9 which explained 34 % of the variance in SF‑6D in the developmental sample s. 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 sample s. In both developmental and cross-validation MOS sample s , 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
Conclusions The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions
Background Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values ( HSUVs ) from data obtained from another health-related quality of life ( HRQoL ) measure .
There have been several method ological developments in this area since a previous review up to 2007 .