Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK.
Rheumatology (Oxford). 2013 May;52(5):944-50. doi: 10.1093/rheumatology/kes400. Epub 2013 Jan 21.
This study aims to provide robust estimates of EQ-5D as a function of the HAQ and pain in patients with RA.
Repeated observations were made of patients diagnosed with RA in a US observational cohort (n = 100 398 observations) who provided data on HAQ, pain on a visual analogue scale and the EQ-5D questionnaire. We used a bespoke statistical method based on mixture modelling to appropriately reflect the characteristics of the EQ-5D instrument and to compare this with results from standard multiple regression.
EQ-5D can be predicted from summary HAQ and pain scores. We identify four different classes of respondents who differ in terms of disease severity. Unlike the multiple regression, the mixture model exhibits very good fit to the data and does not suffer from problems of bias or predict values outside the feasible range.
It is appropriate to model the relationship between HAQ and EQ-5D but only if suitable statistical methods are applied. Linear models underestimate the quality-adjusted life year benefits, and therefore the cost-effectiveness, of therapies. The bespoke mixture model approach outlined here overcomes this problem. The addition of pain as an explanatory variable greatly improves the estimates. Reimbursement agencies rely on these types of analyses when formulating policy on the use of new drug therapies. Clinicians as well as economists should be concerned with these issues.
本研究旨在提供更可靠的 EQ-5D 作为 RA 患者 HAQ 和疼痛的函数的估计值。
对美国观察性队列中诊断为 RA 的患者进行了重复观察（n=100398 次观察），这些患者提供了 HAQ、视觉模拟量表上的疼痛和 EQ-5D 问卷的数据。我们使用了一种基于混合模型的定制统计方法，以适当反映 EQ-5D 工具的特点，并将其与标准多元回归的结果进行比较。
EQ-5D 可以从总结的 HAQ 和疼痛评分中预测。我们确定了四个不同类别的应答者，他们在疾病严重程度方面有所不同。与多元回归不同，混合模型对数据具有很好的拟合度，并且不会出现偏差或预测值超出可行范围的问题。
对 HAQ 和 EQ-5D 之间的关系进行建模是合适的，但前提是应用适当的统计方法。线性模型低估了治疗的质量调整生命年获益，因此也低估了治疗的成本效益。这里概述的定制混合模型方法克服了这个问题。将疼痛作为一个解释变量加入，可以大大提高估计值。报销机构在制定新药物治疗使用政策时依赖于这些类型的分析。临床医生和经济学家都应该关注这些问题。
