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Impact of mapped EQ-5D utilities on cost-effectiveness analysis: in the case of dialysis treatments

机译:映射的 EQ-5D 实用程序对成本效益分析的影响:在透析治疗的情况下

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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.
机译:目的本研究旨在评估从 SF-12 映射的 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。方法基于EQ-5D-5L、EQ-5D、3种直接映射算法和2种响应映射算法(mEQ-5D)和SF-6D的效用值,基于评估血液透析(HD)和腹膜透析(PD)成本效益的两个马尔可夫模型计算增量质量调整生命年(QALYs)和增量成本效益比(ICERs)。Bootstrap 方法用于估计增量 QALY 和 ICER 的 95% 置信区间(百分位数法),其中效用有 1000 次重复。结果在两个模型中,与观察到的EQ-5D值相比,mEQ-5D值的增量QALYs要低得多(范围-14.9至-33.2%),ICER值要高得多(范围为17.5%至49.7%)。SF-6D还估计,与观察到的EQ-5D相比,增量QALY(-29.0和-14.9%)和ICER(40.9%和17.5%)更高。增量 QALY 和 ICER 的 95% 置信区间证实了使用 mEQ-5D 和 SF-6D 估计的较低增量 QALY 和较高的 ICER。结论与实测EQ-5D相比,SF-12和SF-6D映射的EQ-5D会低估成本效用分析中获得的QALYs,从而导致更高的ICER。更明智的做法是使用直接收集的 EQ-5D 数据进行 CUA 研究,并将一个基于偏好的单一措施指定为司法管辖区的参考案例,以实现医疗保健决策的一致性。

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