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 .
The potential for a generally applicable mapping model between QLQ-C30 and SF-6D in patients with different cancers: a comparison of regression-based methods
Purpose To establish and compare generalized or “ global ” mapping relationships between QLQ-C30 and SF-6D , applicable across different cancer types . Methods Patients ( N = 671 ) with breast , myeloma , colorectal , lymphoma , bone marrow , prostate , lung and gastroenteric cancer were r and omly split into estimation ( 75 % ) and validation ( 25 % ) data sets . SF-6D was estimated from QLQ-C30 scores via ordinary least squares , generalized linear models and median ( least-absolute deviations ) regression approaches , and with Bayesian additive regression kernels . Predictive ability was assessed with root mean square error , mean absolute error and proportions of predictions with absolute errors > 0.05 and > 0.1 , whereas explanatory power with adjusted R2 or equivalent fit measures . Two external sample s ( breast and colorectal cancer ) were used to further test the models . Results The QLQ-C30 's global health item , the physical , emotional and social functioning scales , and the fatigue , pain and diarrhea symptom scales were significant predictors ( p < 0.05 or better ) in all models . Negligible deviations in models ’ performance were observed . All models overpredicted utilities for patients in worst health and underpredicted them for those in better health ( p < 0.01 or better ) . Regarding external validation , performance was better in the colorectal cancer than in the breast cancer sample . Conclusions This study has provided evidence to support the use of “ global ” mapping models to predict SF-6D utilities from QLQ-C30 in patients with different cancers . Testing with diverse patient sample s is required to confirm the generalizability ( or not ) of mapping models across cancer conditions
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 .