Abstract
ObjectivesThis study aimed to evaluate the performance of EQ-5D data mapped from SF-12 in terms of estimating cost effectiveness in cost-utility analysis (CUA). The comparability of SF-6D (derived from SF-12) was also assessed.MethodsIncremental quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs) were calculated based on two Markov models assessing the cost effectiveness of haemodialysis (HD) and peritoneal dialysis (PD) using utility values based on EQ-5D-5L, EQ-5D using three direct-mapping algorithms and two response-mapping algorithms (mEQ-5D), and SF-6D. Bootstrap method was used to estimate the 95% confidence interval (percentile method) of incremental QALYs and ICERs with 1000 replications for the utilities.ResultsIn both models, compared to the observed EQ-5D values, mEQ-5D values expressed much lower incremental QALYs (range − 14.9 to − 33.2%) and much higher ICERs (range 17.5 to 49.7%). SF-6D also estimated lower incremental QALYs (− 29.0 and − 14.9%) and higher ICERs (40.9 and 17.5%) than did the observed EQ-5D. The 95% confidence interval of incremental QALYs and ICERs confirmed the lower incremental QALYs and higher ICERs estimated using mEQ-5D and SF-6D.ConclusionCompared to observed EQ-5D, EQ-5D mapped from SF-12 and SF-6D would under-estimate the QALYs gained in cost-utility analysis and thus lead to higher ICERs. It would be more sensible to conduct CUA studies using directly collected EQ-5D data and to designate one single preference-based measure as reference case in a jurisdiction to achieve consistency in healthcare decision-making.
Highlights
Estimation of health utility and quality-adjusted life years (QALYs) is an important part of cost-utility analysis (CUA) in economic evaluation [1]
EuroQol 5-dimension (EQ-5D)-3L 3-level EuroQol-5D, SF-12 Short Form-12, SF-6D Short Form 6-dimension, TTO time trade-off, DCE discrete choice experiment a physical component summary (PCS) and mental component summary (MCS) were centered on the sample mean and included in ordinary least squares model with the interaction terms b PCS, MCS, and the interaction terms were included in ordinary least squares model c PCS and MCS were included in ordinary least squares model d PCS, MCS, and the interaction terms were used in multinomial logit model e Individual SF-12 questions were used in multinomial logit model
Among the mEQ-5D values, the error margins estimated by the response-mapping functions were wider than the direct-mapping functions
Summary
Estimation of health utility and quality-adjusted life years (QALYs) is an important part of cost-utility analysis (CUA) in economic evaluation [1]. The “source” predictive measures used to map to EQ-5D could be condition-specific quality of life measures (such as EORTC QLQ-C30 for cancer patients [8]), generic quality of life measures (such as Short Form 12-item (SF-12) [9]),. Data can be mapped to either the EQ-5D utility values or the EQ-5D item responses [11]. There are currently no clear guidelines on the best mapping method to EQ-5D for QALY estimation; so when deciding which mapping algorithm should be used in a particular study, whether it could generate comparable utility and cost-effectiveness estimates as the primarily collected EQ-5D would be the main consideration
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