Application of Latent Variable Methods to the Study of Cognitive Decline When Tests Change over Time
Application of Latent Variable Methods to the Study of Cognitive Decline When Tests Change over Time
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DOI：
10.1097/ede.0000000000000379
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发表时间：
2015-11-01
期刊：
影响因子：
5.4
通讯作者：
Bandeen-Roche, Karen
中科院分区：
文献类型：
--
作者：
Gross, Alden L.;Power, Melinda C.;Bandeen-Roche, Karen
背景：不同队列研究访问中构建的测量方式可能有所不同，这使得纵向比较变得复杂。我们演示了如何使用因子分析将不同的认知测试组合与代表一般认知表现、记忆、执行功能和语言的常见指标联系起来。 方法：我们使用了社区神经认知研究动脉粥样硬化风险研究 (N = 14,252) 的 3 次访问（超过 26 年）的数据。我们允许个体测试根据种族提供不同的信息，这是认知老化中需要考虑的一个重要因素。使用广义估计方程，我们使用一般和特定领域因子得分与等权标准化测试得分的平均值来比较糖尿病与认知变化的关联。结果：因子得分提供了与糖尿病更强的关联，但代价是估计值的变异性更大（例如，对于一般认知表现，-0.064 个标准差单位/年，标准误差 = 0.015，vs. -0.041 标准差单位/年，标准误差 = 0.014），这与因子得分比标准化测试的平均值更明确地解决测量评估特征中的错误的概念是一致的。结论：因子分析有助于在测量值随时间变化时使用所有可用数据，此外，它允许对差异项目功能进行客观评估和纠正。
Background: The way a construct is measured can differ across cohort study visits, complicating longitudinal comparisons. We demonstrated the use of factor analysis to link differing cognitive test batteries over visits to common metrics representing general cognitive performance, memory, executive functioning, and language.Methods: We used data from three visits (over 26 years) of the Atherosclerosis Risk in Communities Neurocognitive Study (N = 14,252). We allowed individual tests to contribute information differentially by race, an important factor to consider in cognitive aging. Using generalized estimating equations, we compared associations of diabetes with cognitive change using general and domain-specific factor scores versus averages of equally weighted standardized test scores.Results: Factor scores provided stronger associations with diabetes at the expense of greater variability around estimates (e.g., for general cognitive performance, -0.064 standard deviation units/year, standard error = 0.015, vs. -0.041 standard deviation units/year, standard error = 0.014), which is consistent with the notion that factor scores more explicitly address error in measuring assessed traits than averages of standardized tests.Conclusions: Factor analysis facilitates use of all available data when measures change over time, and further, it allows objective evaluation and correction for differential item functioning.