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基于接收器操作特征分析或预测建模的锚定最小重要变化,可能需要根据改善患者的比例进行调整。

The anchor-based minimal important change, based on receiver operating characteristic analysis or predictive modeling, may need to be adjusted for the proportion of improved patients.

机构信息

Department of General Practice and Elderly Care Medicine, EMGO Institute for Health and Care Research, VU University Medical Center, Van der Boechorststraat 7, Amsterdam 1081 BT, The Netherlands.

Department of Epidemiology and Biostatistics, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands; Department of Child Health, Netherlands Organisation for Applied Scientific Research (TNO), Schipholweg 77-89, Leiden 2316 ZL, The Netherlands.

出版信息

J Clin Epidemiol. 2017 Mar;83:90-100. doi: 10.1016/j.jclinepi.2016.12.015. Epub 2017 Jan 14.

Abstract

OBJECTIVES

Patients have their individual minimal important changes (iMICs) as their personal benchmarks to determine whether a perceived health-related quality of life (HRQOL) change constitutes a (minimally) important change for them. We denote the mean iMIC in a group of patients as the "genuine MIC" (gMIC). The aims of this paper are (1) to examine the relationship between the gMIC and the anchor-based minimal important change (MIC), determined by receiver operating characteristic analysis or by predictive modeling; (2) to examine the impact of the proportion of improved patients on these MICs; and (3) to explore the possibility to adjust the MIC for the influence of the proportion of improved patients.

STUDY DESIGN AND SETTING

Multiple simulations of patient samples involved in anchor-based MIC studies with different characteristics of HRQOL (change) scores and distributions of iMICs. In addition, a real data set is analyzed for illustration.

RESULTS

The receiver operating characteristic-based and predictive modeling MICs equal the gMIC when the proportion of improved patients equals 0.5. The MIC is estimated higher than the gMIC when the proportion improved is greater than 0.5, and the MIC is estimated lower than the gMIC when the proportion improved is less than 0.5. Using an equation including the predictive modeling MIC, the log-odds of improvement, the standard deviation of the HRQOL change score, and the correlation between the HRQOL change score and the anchor results in an adjusted MIC reflecting the gMIC irrespective of the proportion of improved patients.

CONCLUSION

Adjusting the predictive modeling MIC for the proportion of improved patients assures that the adjusted MIC reflects the gMIC.

LIMITATIONS

We assumed normal distributions and global perceived change scores that were independent on the follow-up score. Additionally, floor and ceiling effects were not taken into account.

摘要

目的

患者有其个人的最小重要变化(iMIC)作为个人基准,以确定感知到的健康相关生活质量(HRQOL)变化是否对他们构成(最小)重要变化。我们将一组患者的平均 iMIC 表示为“真实 MIC”(gMIC)。本文的目的是:(1)检验 gMIC 与基于锚定的最小重要变化(MIC)之间的关系,该 MIC 通过接受者操作特征分析或预测建模来确定;(2)检验改善患者比例对这些 MIC 的影响;(3)探讨为了调整 MIC 以适应改善患者比例的影响的可能性。

研究设计与设置

涉及基于锚定的 MIC 研究的患者样本的多次模拟,这些研究具有不同的 HRQOL(变化)评分和 iMIC 分布特征。此外,还分析了一个真实数据集进行说明。

结果

当改善患者的比例等于 0.5 时,基于接受者操作特征的 MIC 和预测建模 MIC 等于 gMIC。当改善患者的比例大于 0.5 时,MIC 估计高于 gMIC,当改善患者的比例小于 0.5 时,MIC 估计低于 gMIC。使用包含预测建模 MIC、改善的对数几率、HRQOL 变化评分的标准差以及 HRQOL 变化评分与锚定结果之间的相关性的方程,得出一个调整后的 MIC,可以反映 gMIC,而与改善患者的比例无关。

结论

调整预测建模 MIC 以适应改善患者的比例可确保调整后的 MIC 反映 gMIC。

局限性

我们假设正态分布和独立于随访评分的整体感知变化评分。此外,未考虑地板效应和天花板效应。

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