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 of the DLQI scores to EQ-5D utility values using ordinal logistic regression
Purpose The Dermatology  Life Quality  Index ( DLQI ) and the European Quality of Life-5 Dimension ( EQ-5D ) are separate measures that may be used to gather health-related quality of life ( HRQoL ) information from patients . The EQ-5D is a generic measure from which health utility estimates can be derived , whereas the DLQI is a specialty-specific measure to assess HRQoL. To reduce the burden of multiple measures being administered and to enable a more disease-specific calculation of health utility estimates , we explored an established mathematical technique known as ordinal logistic regression ( OLR ) to develop an appropriate model to map DLQI data to EQ-5D-based health utility estimates . Methods Retrospective data from 4010 patients were r and omly divided five times into two groups for the derivation and testing of the mapping model . Split-half cross-validation was utilized result ing in a total of ten ordinal logistic regression models for each of the five EQ-5D dimensions against age , sex , and all ten items of the DLQI . Using Monte Carlo simulation , predicted health utility estimates were derived and compared against those observed . This method was repeated for both OLR and a previously tested mapping methodology based on linear regression . Results The model was shown to be highly predictive and its repeated fitting demonstrated a stable model using OLR as well as linear regression . The mean differences between OLR-predicted health utility estimates and observed health utility estimates ranged from 0.0024 to 0.0239 across the ten modeling exercises , with an average overall difference of 0.0120 ( a 1.6 % underestimate , not of clinical importance ) . Conclusions This modeling framework developed in this study will enable research ers to calculate EQ-5D health utility estimates from a specialty-specific study population , reducing patient and economic burden
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 .