title: Mapping EORTC QLQ-C30 and QLQ-MY20 to EQ-5D in patients with multiple myeloma.
abstract: BACKGROUND
  In oncology, health-related quality of life (HRQoL) data are often collected using disease-specific patient questionnaires while generic, patient-level utility data required for health economic modeling are often not collected.
  
  
  METHODS
  We developed a mapping algorithm for multiple myeloma that relates HRQoL scores from the European Organization for Research and Treatment of Cancer (EORTC) questionnaires QLQ-C30 and QLQ-MY20 to a utility value from the European QoL-5 Dimensions (EQ-5D) questionnaire. Data were obtained from 154 multiple myeloma patients who had participated in a multicenter cohort study in the UK or Germany. All three questionnaires were administered at a single time point. Scores from all 19 domains of the QLQ-C30 and QLQ-MY20 instruments were univariately tested against EQ-5D values and retained in a multivariate regression model if statistically significant. A 10-fold cross-validation model selection method was also used as an alternative testing means. Two models were developed: one based on QLQ-C30 plus QLQ-MY20 scores and one based on QLQ-C30 scores alone. Adjusted R-squared, correlation coefficients, and plots of observed versus predicted EQ-5D values were presented for both models.
  
  
  RESULTS
  Mapping revealed that Global Health Status/QoL, Physical Functioning, Pain, and Insomnia were significant predictors of EQ-5D utility values. Similar results were observed when QLQ-MY20 scores were excluded from the model, except that Emotional Functioning and became a significant predictor and Insomnia was no longer a significant predictor. Adjusted R-squared values were of similar magnitude with or without inclusion of QLQ-MY20 scores (0.70 and 0.69, respectively), suggesting that the EORTC QLQ-MY20 adds little in terms of predicting utility values in multiple myeloma.
  
  
  CONCLUSIONS
  This algorithm successfully mapped EORTC HRQoL data onto EQ-5D utility in patients with multiple myeloma. Current mapping will aid in the analysis of cost-effectiveness of novel therapies for this indication.
authors.affiliation: Evidera, 7575 Trans-Canada Highway, Suite 500, H4T 1V6 Montreal, QC, Canada. Irina.Proskorovsky@evidera.com.
authors.forename: Irina
authors.initials: I
authors.lastname: Proskorovsky
authors.forename: Philip
authors.initials: P
authors.lastname: Lewis
authors.forename: Cathy D
authors.initials: CD
authors.lastname: Williams
authors.forename: Karin
authors.initials: K
authors.lastname: Jordan
authors.forename: Charalampia
authors.initials: C
authors.lastname: Kyriakou
authors.forename: Jack
authors.initials: J
authors.lastname: Ishak
authors.forename: Faith E
authors.initials: FE
authors.lastname: Davies
mesh_terms: D000368:Aged; D000465:Algorithms; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D005062:European Union; D005260:Female; D005858:Germany; D006305:Health Status Indicators; D006801:Humans; D016015:Logistic Models; D008297:Male; D009101:Multiple Myeloma / Q000175:diagnosis / Q000523:psychology*; D011594:Psychometrics / Q000295:instrumentation* / Q000592:standards; D011788:Quality of Life; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D011795:Surveys and Questionnaires; D006113:United Kingdom