Abstract
BackgroundCost-effectiveness models require quality of life utilities calculated from generic preference-based questionnaires, such as EQ-5D. We evaluated the performance of available algorithms for QLQ-C30 conversion into EQ-5D-3L based utilities in a metastatic colorectal cancer (mCRC) patient population and subsequently developed a mCRC specific algorithm. Influence of mapping on cost-effectiveness was evaluated.MethodsThree available algorithms were compared with observed utilities from the CAIRO3 study. Six models were developed using 5-fold cross-validation: predicting EQ-5D-3L tariffs from QLQ-C30 functional scale scores, continuous QLQ-C30 scores or dummy levels with a random effects model (RE), a most likely probability method on EQ-5D-3L functional scale scores, a beta regression model on QLQ-C30 functional scale scores and a separate equations subgroup approach on QLQ-C30 functional scale scores. Performance was assessed, and algorithms were tested on incomplete QLQ-C30 questionnaires. Influence of utility mapping on incremental cost/QALY gained (ICER) was evaluated in an existing Dutch mCRC cost-effectiveness model.ResultsThe available algorithms yielded mean utilities of 1: 0.87 ± sd:0.14,2: 0.81 ± 0.15 (both Dutch tariff) and 3: 0.81 ± sd:0.19. Algorithm 1 and 3 were significantly different from the mean observed utility (0.83 ± 0.17 with Dutch tariff, 0.80 ± 0.20 with U.K. tariff). All new models yielded predicted utilities drawing close to observed utilities; differences were not statistically significant. The existing algorithms resulted in an ICER difference of €10,140 less and €1765 more compared to the observed EQ-5D-3L based ICER (€168,048). The preferred newly developed algorithm was €5094 higher than the observed EQ-5D-3L based ICER. Disparity was explained by minimal diffences in incremental QALYs between models.ConclusionAvailable mapping algorithms sufficiently accurately predict utilities. With the commonly used statistical methods, we did not succeed in developping an improved mapping algorithm. Importantly, cost-effectiveness outcomes in this study were comparable to the original model outcomes between different mapping algorithms. Therefore, mapping can be an adequate solution for cost-effectiveness studies using either a previously designed and validated algorithm or an algorithm developed in this study.
Highlights
Cost-effectiveness models require quality of life utilities calculated from generic preference-based questionnaires, such as EQ-5D
Mapping can be an adequate solution for cost-effectiveness studies using either a previously designed and validated algorithm or an algorithm developed in this study
From a total of 2440 observations, 1905 concurrently collected, complete QLQ-C30 and EQ-5D-3L questionnaires were included in this analysis
Summary
Cost-effectiveness models require quality of life utilities calculated from generic preference-based questionnaires, such as EQ-5D. Measurement of health-related quality of life (HRQoL) with generic questionnaires (e.g. EQ-5D-3L) and disease specific questionnaires (e.g. EORTC QLQ-C30) are of great interest to clinicians and researchers, especially in the context of cost-effectiveness research. Quality of life can be measured through a generic preferencebased quality of life questionnaire such as the commonly used EQ-5D-3L questionnaire, which is requested by some reimbursement authorities [2] Based on this questionnaire, patient scores are transformed into healthrelated quality of life utilities, on a scale of 1 - being full health- to 0 - reflecting death (and even negative values reflecting health states worse than death), which can be combined with the duration (survival) of a patient to calculate the QALY [1, 3]
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