Centre for Health Economics, University of York, Heslington, York, YO10 5DD, UK.
Office of Health Economics, London, UK.
Eur J Health Econ. 2019 Feb;20(1):99-105. doi: 10.1007/s10198-018-0987-x. Epub 2018 Jun 14.
This 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.
Incremental 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.
In 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.
Compared 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.
本研究旨在评估从 SF-12 映射而来的 EQ-5D 数据在成本效用分析(CUA)中估计成本效益的性能。还评估了 SF-6D(源自 SF-12)的可比性。
基于两个马尔可夫模型,根据基于 EQ-5D-5L 的效用值、使用三种直接映射算法和两种响应映射算法(mEQ-5D)的 EQ-5D 和 SF-6D,计算了血液透析(HD)和腹膜透析(PD)的增量质量调整生命年(QALY)和增量成本效益比(ICER)。使用 bootstrap 方法对效用的 1000 次重复进行了估计,以获得增量 QALY 和 ICER 的 95%置信区间(百分位法)。
在两个模型中,与观察到的 EQ-5D 值相比,mEQ-5D 值表示的增量 QALY 要低得多(范围为-14.9 至-33.2%),而 ICER 则高得多(范围为 17.5 至 49.7%)。SF-6D 还估计了较低的增量 QALY(-29.0 和-14.9%)和较高的 ICER(40.9 和 17.5%),而不是观察到的 EQ-5D。增量 QALY 和 ICER 的 95%置信区间证实了 mEQ-5D 和 SF-6D 估算的增量 QALY 较低,ICER 较高。
与观察到的 EQ-5D 相比,从 SF-12 和 SF-6D 映射而来的 EQ-5D 在成本效用分析中会低估获得的 QALY,从而导致更高的 ICER。在一个司法管辖区内,使用直接收集的 EQ-5D 数据进行 CUA 研究,并指定一个单一的偏好衡量标准作为参考案例,以实现医疗保健决策的一致性,这将更为明智。