title: Mapping Functions in Health-Related Quality of Life: Mapping from Two Cancer-Specific Health-Related Quality-of-Life Instruments to EQ-5D-3L.
abstract: Clinical trials in cancer frequently include cancer-specific measures of health but not preference-based measures such as the EQ-5D that are suitable for economic evaluation. Mapping functions have been developed to predict EQ-5D values from these measures, but there is considerable uncertainty about the most appropriate model to use, and many existing models are poor at predicting EQ-5D values. This study aims to investigate a range of potential models to develop mapping functions from 2 widely used cancer-specific measures (FACT-G and EORTC-QLQ-C30) and to identify the best model.
  
  
  
  Mapping models are fitted to predict EQ-5D-3L values using ordinary least squares (OLS), tobit, 2-part models, splining, and to EQ-5D item-level responses using response mapping from the FACT-G and QLQ-C30. A variety of model specifications are estimated. Model performance and predictive ability are compared. Analysis is based on 530 patients with various cancers for the FACT-G and 771 patients with multiple myeloma, breast cancer, and lung cancer for the QLQ-C30.
  
  
  
  For FACT-G, OLS models most accurately predict mean EQ-5D values with the best predicting model using FACT-G items with similar results using tobit. Response mapping has low predictive ability. In contrast, for the QLQ-C30, response mapping has the most accurate predictions using QLQ-C30 dimensions. The QLQ-C30 has better predicted EQ-5D values across the range of possible values; however, few respondents in the FACT-G data set have low EQ-5D values, which reduces the accuracy at the severe end.
  
  
  
  OLS and tobit mapping functions perform well for both instruments. Response mapping gives the best model predictions for QLQ-C30. The generalizability of the FACT-G mapping function is limited to populations in moderate to good health.
authors.affiliation: School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK (TAY, CM, DR, JEB)
authors.forename: Tracey A
authors.initials: TA
authors.lastname: Young
authors.affiliation: School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK (TAY, CM, DR, JEB)
authors.forename: Clara
authors.initials: C
authors.lastname: Mukuria
authors.affiliation: School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK (TAY, CM, DR, JEB)
authors.forename: Donna
authors.initials: D
authors.lastname: Rowen
authors.affiliation: School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK (TAY, CM, DR, JEB)
authors.forename: John E
authors.initials: JE
authors.lastname: Brazier
authors.affiliation: HERG, Brunel University, Uxbridge, Middlesex, UK (LL)
authors.forename: Louise
authors.initials: L
authors.lastname: Longworth
mesh_terms: D006801:Humans; D009369:Neoplasms / Q000503:physiopathology*; D011788:Quality of Life
keywords: EORTC-QLQ-C30; EQ-5D-3L; FACT-G; cancer; health-related quality of life; mapping functions