Mapping EORTC QLQ-C30 and QLQ-MY20 to EQ-5D in patients with multiple myeloma

BackgroundIn 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.MethodsWe 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.ResultsMapping 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.ConclusionsThis 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.

[1]  D. Esseltine,et al.  Health‐related quality of life in elderly, newly diagnosed multiple myeloma patients treated with VMP vs. MP: results from the VISTA trial , 2012, European journal of haematology.

[2]  Ruth E. Brown,et al.  Lenalidomide for multiple myeloma: cost-effectiveness in patients with one prior therapy in England and Wales , 2013, The European Journal of Health Economics.

[3]  Nick Kontodimopoulos,et al.  Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ-5D, SF-6D, and 15D instruments. , 2009, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[4]  D. Osoba,et al.  The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. , 1993, Journal of the National Cancer Institute.

[5]  R. Hays,et al.  Commentary on using the SF-36 or MOS-HIV in studies of persons with HIV disease , 2003, Health and quality of life outcomes.

[6]  Andrew Briggs,et al.  Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences , 2010, The European Journal of Health Economics.

[7]  R. Hájek,et al.  An international field study of the reliability and validity of a disease-specific questionnaire module (the QLQ-MY20) in assessing the quality of life of patients with multiple myeloma. , 2007, European journal of cancer.

[8]  M. Dimopoulos,et al.  Lenalidomide, melphalan, and prednisone, followed by lenalidomide maintenance, improves health-related quality of life in newly diagnosed multiple myeloma patients aged 65 years or older: results of a randomized phase III trial , 2013, Haematologica.

[9]  Benjamin M Craig,et al.  Deriving a preference-based measure for cancer using the EORTC QLQ-C30. , 2011, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[10]  K. Gelmon,et al.  The ability of cancer-specific and generic preference-based instruments to discriminate across clinical and self-reported measures of cancer severities , 2011, Health and quality of life outcomes.

[11]  M Sculpher,et al.  EOS 2D/3D X-ray imaging system: a systematic review and economic evaluation. , 2012, Health technology assessment.

[12]  F. Davies,et al.  Effect of general symptom level, specific adverse events, treatment patterns, and patient characteristics on health-related quality of life in patients with multiple myeloma: results of a European, multicenter cohort study , 2013, Supportive Care in Cancer.

[13]  R. Deyo,et al.  Generic and Disease-Specific Measures in Assessing Health Status and Quality of Life , 1989, Medical care.

[14]  Hye-young Kang,et al.  Mapping the cancer-specific EORTC QLQ-C30 and EORTC QLQ-BR23 to the generic EQ-5D in metastatic breast cancer patients , 2012, Quality of Life Research.

[15]  M. Dimopoulos,et al.  Factors that influence health-related quality of life in newly diagnosed patients with multiple myeloma aged ≥ 65 years treated with melphalan, prednisone and lenalidomide followed by lenalidomide maintenance: results of a randomized trial , 2013, Leukemia & lymphoma.

[16]  M. Barkham,et al.  Developing and testing methods for deriving preference-based measures of health from condition-specific measures (and other patient-based measures of outcome). , 2012, Health technology assessment.

[17]  Jonathan C Tosh,et al.  Utility values in National Institute for Health and Clinical Excellence (NICE) Technology Appraisals. , 2011, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[18]  M. Versteegh,et al.  Mapping QLQ-C30, HAQ, and MSIS-29 on EQ-5D , 2012, Medical decision making : an international journal of the Society for Medical Decision Making.

[19]  M. Farrell,et al.  Mapping FACT-P and EORTC QLQ-C30 to patient health status measured by EQ-5D in metastatic hormone-refractory prostate cancer patients. , 2007, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[20]  M. van der Pol,et al.  Mapping the EORTC QLQ C-30 onto the EQ-5D instrument: the potential to estimate QALYs without generic preference data. , 2009, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[21]  R. Dennis Cook,et al.  Cross-Validation of Regression Models , 1984 .