title: Comparing the mapping between EQ-5D-5L, EQ-5D-3L and the EORTC-QLQ-C30 in non-small cell lung cancer patients.
abstract: BACKGROUND
  Several mapping algorithms have been published with the EORTC-QLQ-C30 for estimating EQ-5D-3L utilities. However, none are available with EQ-5D-5L. Moreover, a comparison between mapping algorithms in the same set of patients has not been performed for these two instruments simultaneously. In this prospective data set of 100 non-small cell lung cancer (NSCLC) patients, we investigate three mapping algorithms using the EQ-5D-3L and EQ-5D-5L and compare their performance.
  
  
  METHODS
  A prospective non-interventional cohort of 100 NSCLC patients were followed up for 12 months. EQ-5D-3L, EQ-5D-5L and EORTC-QLQ-C30 were assessed monthly. EQ-5D-5L was completed at least 1 week after EQ-5D-3L. A random effects linear regression model, a beta-binomial (BB) and a Limited Variable Dependent Mixture (LVDM) model were used to determine a mapping algorithm between EQ-5D-3L, EQ-5D-5L and QLQ-C30. Simulation and cross validation and other statistical measures were used to compare the performances of the algorithms.
  
  
  RESULTS
  Mapping from the EQ-5D-5L was better: lower AIC, RMSE, MAE and higher R(2) were reported with the EQ-5D-5L than with EQ-5D-3L regardless of the functional form of the algorithm. The BB model proved to be more useful for both instruments: for the EQ-5D-5L, AIC was -485, R(2) of 75 %, MAE of 0.075 and RMSE was 0.092. This was -385, 69 %, 0.099 and 0.113 for EQ-5D-3L respectively. The mean observed vs. predicted utilities were 0.572 vs. 0.577 and 0.515 vs. 0.523 for EQ-5D-5L and EQ-5D-3L respectively, for OLS; for BB, these were 0.572 vs. 0.575 and 0.515 vs. 0.518 respectively and for LVDMM 0.532 vs 0.515 and 0.569 vs 0.572 respectively. Less over-prediction at poorer health states was observed with EQ-5D-5L.
  
  
  CONCLUSIONS
  The BB mapping algorithm is confirmed to offer a better fit for both EQ-5D-3L and EQ-5D-5L. The results confirm previous and more recent results on the use of BB type modelling approaches for mapping. It is recommended that in studies where EQ-5D utilities have not been collected, an EQ-5D-5L mapping algorithm is used.
authors.affiliation: Clinical Trials Unit & Department of Health Economics, University of Surrey, Guilford, UK. I.Khan@surrey.ac.uk.|Department of Applied Health Research, University College London, London, UK. I.Khan@surrey.ac.uk.
authors.forename: Iftekhar
authors.initials: I
authors.lastname: Khan
authors.affiliation: Department of Applied Health Research, University College London, London, UK.
authors.forename: Steve
authors.initials: S
authors.lastname: Morris
authors.affiliation: Department of Applied Health Research, University College London, London, UK.
authors.forename: Nora
authors.initials: N
authors.lastname: Pashayan
authors.affiliation: Liverpool Heart and Chest Hospital, Liverpool, UK.
authors.forename: Bashir
authors.initials: B
authors.lastname: Matata
authors.affiliation: University of Birmingham, Cancer School, Birmingham, UK.
authors.forename: Zahid
authors.initials: Z
authors.lastname: Bashir
authors.affiliation: Liverpool Heart and Chest Hospital, Liverpool, UK.
authors.forename: Joe
authors.initials: J
authors.lastname: Maguirre
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000523:psychology*; D015331:Cohort Studies; D005260:Female; D005500:Follow-Up Studies; D006304:Health Status; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011446:Prospective Studies; D011788:Quality of Life / Q000523:psychology*; D011795:Surveys and Questionnaires