Mapping ALSFRS-R and ALSUI to EQ-5D in Patients with Motor Neuron Disease
Mapping ALSFRS-R and ALSUI to EQ-5D in Patients with Motor Neuron Disease
复制标题
DOI：
10.1016/j.jval.2018.05.005
复制
发表时间：
2018-11-01
期刊：
影响因子：
4.5
通讯作者：
Hughes, Dyfrig A.
中科院分区：
文献类型：
--
作者：
Moore, Alan;Young, Carolyn A.;Hughes, Dyfrig A.
背景：肌萎缩侧索硬化症功能评定量表修订版 (ALSFRS-R) 是运动神经元疾病 (MND) 临床试验中衡量健康结果的首选指标。然而，它没有提供在卫生技术评估的经济评估中估计质量调整生命年所需的基于偏好的卫生效用评分。目标：开发用于根据 MND 临床研究中使用的测量进行映射的算法，以便在尚未收集效用数据的情况下，未来预测五级 EuroQol 五维问卷 (EQ-5D-5L) 在 MND 患者群体中的效用。方法：使用普通最小二乘法和 Tobit 回归分析开发直接映射模型，以估计 EQ-5D-5L 效用（基于英国关税），并使用来自英国队列研究的患者水平数据，将 ALSFRS-R 总分、领域分和项目分用作解释变量。间接映射模型也用于映射 EQ-5D-5L 域，使用相同的变量，以及使用多项逻辑回归分析的 MND 神经病理性疼痛量表和医院和焦虑抑郁量表。评估拟合优度以及每个映射模型的预测值。结果：预测 EQ-5D-5L 实用程序的性能最佳模型使用 ALSFRS-R 项目中的五个项目作为逐步普通最小二乘回归中的解释变量。均方误差为 0.0228，平均绝对误差为 0.1173。预测效果良好，55.4% 的估计值在观察到的 EQ-5D-5L 效用值的 0.1 以内，91.4% 在 0.25 以内。使用神经病理性疼痛量表以及 MND 医院和焦虑抑郁量表进行的间接映射提供的预测能力低于直接映射模型。结论：这是第一项提出 ALSFRS-R 和 EQ5D-5L 之间人行横道映射算法的研究。该分析表明，当未在试验中直接收集 EQ-5D-5L 效用时，ALSFRS-R 可用于估计它们。
Background: The Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) is the preferred measure of health outcome in clinical trials in motor neuron disease (MND). It, however, does not provide a preference-based health utility score required for estimating quality-adjusted life-years in economic evaluations for health technology assessments. Objectives: To develop algorithms for mapping from measures used in MND clinical studies to allow for future prediction of the five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) utility in populations of patients with MND when utility data have not been collected. Methods: Direct mapping models were developed using ordinary least squares and Tobit regression analyses to estimate EQ-5D-5L utilities (based on English tariffs), with ALSFRS-R total, domain, and item scores used as explanatory variables, using patient-level data from a UK cohort study. Indirect mapping models were also used to map EQ-5D-5L domains, using the same variables, along with the Neuropathic Pain Scale and the Hospital and Anxiety Depression Scale for MND using multinomial logistic regression analysis. Goodness of fit was assessed along with predicted values for each mapping model. Results: The best-performing model predicting EQ-5D-5L utilities used five items of the ALSFRS-R items as explanatory variables in a stepwise ordinary least squares regression. The mean squared error was 0.0228, and the mean absolute error was 0.1173. Prediction was good, with 55.4% of estimated values within 0.1 and 91.4% within 0.25 of the observed EQ-5D-5L utility value. Indirect mapping using the Neuropathic Pain Scale and the Hospital and Anxiety Depression Scale for MND provided less predictive power than direct mapping models. Conclusions: This is the first study to present mapping algorithms to crosswalk between ALSFRS-R and EQ5D-5L. This analysis demonstrates that the ALSFRS-R can be used to estimate EQ-5D-5L utilities when they have not been collected directly within a trial.