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 FACT-melanoma quality-of-life scores to EQ-5D health utility weights.
OBJECTIVES We sought to develop a mapping function from functional assessment of cancer therapy-melanoma ( FACT-M ) quality of life scores to the EuroQol-5D ( EQ-5D ) utility scores . METHODS FACT-M and EQ-5D scores were collected during a prospect i ve study of melanoma-related quality of life at a tertiary cancer care center in the United States . The study sample was divided into development and validation data sets with equal distributions by cancer stage and treatment status . Censored Least Absolute Deviation ( CLAD ) and Ordinary Least Squares ( OLS ) regression analyses were performed using the developmental data set to derive mapping functions , and model performance was examined through comparisons of residuals and measures of fit in the validation data set . Exploratory analyses examined the predictive ability of clinical factors and individual subscales . RESULTS Of 273 patients , 75 were undergoing treatment with 198 in follow-up surveillance . Relatively even distributions were observed by melanoma stage : I/II ( n = 102 ) , III ( n = 100 ) , and IV ( n = 71 ) . OLS regression result ed in a mapping function of EQ-5D = 0.0037*FACT-M+0.2238 with an R(2 ) 0.499 . CLAD regression result ed in a mapping function of EQ-5D = 0.0042*FACT-M+0.1648 with pseudo R(2 ) 0.328 . When applied to the validation data set , correlations between observed and predicted values result ed in identical coefficients ( r = 0.824 , P < 0.001 ) . Though the mapping functions were similar , residuals were smaller at the 20th , 40th , and 60th percentiles using the OLS model . The CLAD derived mapping function result ed in smaller residuals only for patients whose EQ-5D = 1 . CONCLUSIONS The OLS mapping function demonstrated better predictive ability and will facilitate the derivation of utilities when direct population preference measures are not available
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 .