当考试管理在学习期间发生变化时,如何评估认知能力下降?中国健康与退休纵向研究中认知得分的协调。

IF 2.8 Q2 NEUROSCIENCES
Journal of Alzheimer's disease reports Pub Date : 2024-12-08 eCollection Date: 2024-01-01 DOI:10.1177/25424823241302759
Yingyan Wu, Yuan S Zhang, Lindsay C Kobayashi, Elizabeth Rose Mayeda, Alden L Gross
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引用次数: 0

摘要

背景:进行纵向认知分析是了解阿尔茨海默病潜在机制的重要组成部分,特别是对社会和健康行为决定因素。然而,认知测试的管理很可能随着时间的推移而改变,从而使纵向分析复杂化。中国健康与退休纵向研究在2011年至2020年的五次研究中通过单词回忆测试来评估记忆。自2018年以来,测试刺激和管理的变化给纵向认知分析带来了挑战。目的:解决给药差异,保留年龄和教育等特征的差异,并得出CHARLS纵向分析中使用的等效分数。方法:为了确保整个样本(N = 19,364)跨波的基本测试能力一致,我们推导了一个校准样本(N = 11,148),平衡了年龄、性别和教育程度。在该样本中,我们使用加权等位数,相当于2015年至2018/2020年得分之间的人行横道百分位数排名,然后将该算法应用于整个样本。结果:平均原始延迟单词回忆在2018年(4.3个单词)和2020年(5.1个单词)高于2015年(3.2个单词)。在相等之后,2018年和2020年的得分与前几波更加一致(2015年,2018年,2020年的直接均值:4.1,3.6,4.0;延迟:3.2,2.4,2.9个单词)。结论:等百分位相等可以推导出可比较的分数,便于在认知测试管理程序随时间变化时进行纵向分析。我们建议使用CHARLS认知数据进行纵向分析时使用等效分数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How to assess cognitive decline when test administration changes across study waves? Harmonizing cognitive scores across waves in the China Health and Retirement Longitudinal Study.

Background: Conducting longitudinal cognitive analyses is an essential part of understanding the underlying mechanism of Alzheimer's disease, especially for social and health behavior determinants. However, the cognitive test administration is highly likely to change across time and thus complicate the longitudinal analyses. The China Health and Retirement Longitudinal Study assessed memory through word recall tests across five study waves from 2011 to 2020. Since 2018, changes in the test stimuli and administration posed challenges for longitudinal cognitive analyses.

Objective: To address differences in administration and to preserve differences attributed to characteristics such as age and education and to derive equated scores for use in longitudinal analyses in CHARLS.

Methods: To ensure consistent underlying test ability across waves in the full sample (N = 19,364), we derived a calibration sample (N = 11,148) balancing age, gender, and education. Within this sample, we used weighted equipercentile equating to crosswalk percentile ranks between 2015 and 2018/2020 scores, then applied the algorithm to the full sample.

Results: Mean original delayed word recall was higher in 2018 (4.3 words) and 2020 (5.1 words) versus 2015 (3.2 words). Following equating, scores in 2018 and 2020 aligned better with previous waves (2015, 2018, 2020 immediate means: 4.1, 3.6, 4.0; delayed: 3.2, 2.4, 2.9 words).

Conclusions: Equipercentile equating enables the derivation of comparable scores, facilitating longitudinal analysis when cognitive test administration procedures change over time. We recommended the use of equated scores for longitudinal analyses using CHARLS cognitive data.

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