Testing mapping algorithms of the cancer-specific EORTC QLQ-C30 onto EQ-5D in malignant mesothelioma

BackgroundIn order to estimate utilities for cancer studies where the EQ-5D was not used, the EORTC QLQ-C30 can be used to estimate EQ-5D using existing mapping algorithms. Several mapping algorithms exist for this transformation, however, algorithms tend to lose accuracy in patients in poor health states. The aim of this study was to test all existing mapping algorithms of QLQ-C30 onto EQ-5D, in a dataset of patients with malignant pleural mesothelioma, an invariably fatal malignancy where no previous mapping estimation has been published.MethodsHealth related quality of life (HRQoL) data where both the EQ-5D and QLQ-C30 were used simultaneously was obtained from the UK-based prospective observational SWAMP (South West Area Mesothelioma and Pemetrexed) trial. In the original trial 73 patients with pleural mesothelioma were offered palliative chemotherapy and their HRQoL was assessed across five time points. This data was used to test the nine available mapping algorithms found in the literature, comparing predicted against observed EQ-5D values. The ability of algorithms to predict the mean, minimise error and detect clinically significant differences was assessed.ResultsThe dataset had a total of 250 observations across 5 timepoints. The linear regression mapping algorithms tested generally performed poorly, over-estimating the predicted compared to observed EQ-5D values, especially when observed EQ-5D was below 0.5. The best performing algorithm used a response mapping method and predicted the mean EQ-5D with accuracy with an average root mean squared error of 0.17 (Standard Deviation; 0.22). This algorithm reliably discriminated between clinically distinct subgroups seen in the primary dataset.ConclusionsThis study tested mapping algorithms in a population with poor health states, where they have been previously shown to perform poorly. Further research into EQ-5D estimation should be directed at response mapping methods given its superior performance in this study.

[1]  G. Torrance Measurement of health state utilities for economic appraisal. , 1986, Journal of health economics.

[2]  D. Feeny,et al.  Multiattribute utility function for a comprehensive health status classification system. Health Utilities Index Mark 2. , 1996, Medical care.

[3]  Jin-Hee Ahn,et al.  Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients , 2012, Health and Quality of Life Outcomes.

[4]  Karl Claxton,et al.  Unrelated Future Costs and Unrelated Future Benefits: Reflections on NICE Guide to the Methods of Technology Appraisal. , 2016, Health economics.

[5]  Donna Rowen,et al.  Mapping to obtain EQ-5D utility values for use in NICE health technology assessments. , 2013, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[6]  Jeffrey A. Johnson,et al.  A Comparison of EQ-5D Index Scores Derived from the US and UK Population-Based Scoring Functions , 2007, Medical decision making : an international journal of the Society for Medical Decision Making.

[7]  M. Neovius,et al.  National EQ-5D tariffs and quality-adjusted life-year estimation: comparison of UK, US and Danish utilities in south Swedish rheumatoid arthritis patients , 2011, Annals of the rheumatic diseases.

[8]  R. Prescott,et al.  A randomised controlled trial of postoperative radiotherapy following breast-conserving surgery in a minimum-risk older population. The PRIME trial. , 2007, Health technology assessment.

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

[10]  J. Brazier,et al.  Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: a systematic review, statistical modelling and survey. , 2014, Health technology assessment.

[11]  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.

[12]  P. Sakthong,et al.  Health and Quality of Life Outcomes , 2008 .

[13]  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.

[14]  J. Brazier,et al.  The estimation of a preference-based measure of health from the SF-36. , 2002, Journal of health economics.

[15]  J. Cairns,et al.  The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation. , 2007, Health technology assessment.

[16]  N. Thatcher,et al.  Raltitrexed plus cisplatin is cost-effective compared with pemetrexed plus cisplatin in patients with malignant pleural mesothelioma. , 2012, Lung cancer.

[17]  G. Giaccone,et al.  Prognostic factors in patients with pleural mesothelioma: the European Organization for Research and Treatment of Cancer experience. , 1998, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[18]  A. Smala,et al.  Cost-effectiveness of pemetrexed plus cisplatin: malignant pleural mesothelioma treatment in UK clinical practice. , 2008, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[19]  M. Versteegh,et al.  Condition-specific preference-based measures: benefit or burden? , 2012, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[20]  Donna Rowen,et al.  Mapping onto Eq-5 D for patients in poor health , 2010, Health and quality of life outcomes.

[21]  Helen Dakin,et al.  Review of studies mapping from quality of life or clinical measures to EQ-5D: an online database , 2013, Health and Quality of Life Outcomes.

[22]  Aki Tsuchiya,et al.  A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures , 2010, The European Journal of Health Economics.

[23]  John Brazier,et al.  Comparison of generic, condition-specific, and mapped health state utility values for multiple myeloma cancer. , 2012, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[24]  P. Dolan,et al.  Modeling valuations for EuroQol health states. , 1997, Medical care.

[25]  J. Bond,et al.  A pragmatic, randomised controlled trial of the cost-effectiveness of palliative therapies for patients with inoperable oesophageal cancer , 2004 .

[26]  A. Haycox,et al.  Pemetrexed disodium for the treatment of malignant pleural mesothelioma: a systematic review and economic evaluation. , 2007, Health Technology Assessment.

[27]  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.

[28]  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.

[29]  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.

[30]  John Brazier,et al.  Deriving an algorithm to convert the eight mean SF-36 dimension scores into a mean EQ-5D preference-based score from published studies (where patient level data are not available). , 2008, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[31]  F. Shepherd,et al.  Derivation of Utility Values from European Organization for Research and Treatment of Cancer Quality of Life-Core 30 Questionnaire Values in Lung Cancer , 2010, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[32]  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.

[33]  Jack Ishak,et al.  Mapping EORTC QLQ-C30 and QLQ-MY20 to EQ-5D in patients with multiple myeloma , 2014, Health and Quality of Life Outcomes.

[34]  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.