A latent factor linear mixed model for high-dimensional longitudinal data analysis.
A latent factor linear mixed model for high-dimensional longitudinal data analysis.
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DOI：
10.1002/sim.5825
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发表时间：
2013-10-30
影响因子：
2
通讯作者：
Bentler, Peter M.
中科院分区：
文献类型：
--
作者：
An, Xinming;Yang, Qing;Bentler, Peter M.
在生物医学和社会科学中，经常会遇到涉及潜在变量（如抑郁和焦虑）的高维纵向数据，这些变量无法直接量化。使用多个响应来表征这些潜在量，并收集重复测量以捕获它们随时间的趋势。此外，实质性的研究问题可能涉及的问题，如潜在变量之间的相互关联的趋势，只能通过共同建模来解决。虽然单变量纵向数据的统计分析已经得到很好的发展，但多变量高维纵向数据的建模方法仍在发展中。在本文中，我们提出了一个潜在的因素线性混合模型（LFLMM）分析这类数据。该模型是因子分析和多元线性混合模型的结合。在此模型框架下，通过因子分析模型将高维响应降维为低维潜在因子，并利用多元线性混合模型研究这些潜在因子的纵向变化趋势。EM算法被开发来估计模型。仿真研究被用来调查的EM算法的计算性能和比较LFLMM模型与其他方法的高维纵向数据分析。一个真实的数据实例说明了该模型的实用性。
High dimensional longitudinal data involving latent variables such as depression and anxiety that cannot be quantified directly are often encountered in biomedical and social sciences. Multiple responses are used to characterize these latent quantities, and repeated measures are collected to capture their trends over time. Furthermore, substantive research questions may concern issues such as interrelated trends among latent variables that can only be addressed by modeling them jointly. While statistical analysis of univariate longitudinal data has been well developed, methods for modeling multivariate high dimensional longitudinal data are still under development. In this paper we propose a latent factor linear mixed model (LFLMM) for analyzing this type of data. This model is a combination of the factor analysis and multivariate linear mixed models. Under this modeling framework, the high dimensional responses are reduced to low dimensional latent factors by the factor analysis model, while the multivariate linear mixed model is used to study the longitudinal trends of these latent factors. An EM algorithm is developed to estimate the model. Simulation studies are used to investigate the computational properties of the EM algorithm and compare the LFLMM model with other approaches for high dimensional longitudinal data analysis. A real data example is used to illustrate the practical usefulness of the model.
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DOI：
10.1198/016214501753208762
发表时间：
2001-09-01
影响因子：
3.7
作者：
Gueorguieva, RV;Agresti, A
通讯作者：
Agresti, A
DOI：
10.1016/s0169-2607(02)00017-2
发表时间：
2002-11-01
影响因子：
6.1
作者：
Thiébaut, R;Jacqmin-Gadda, H;Commenges, D
通讯作者：
Commenges, D
影响因子：
0.8
作者：
GALECKI, AT
通讯作者：
GALECKI, AT
影响因子：
12.7
作者：
BRUCE, ML;SEEMAN, TE;BLAZER, DG
通讯作者：
BLAZER, DG
影响因子：
1.9
作者：
Roy, J;Lin, XH
通讯作者：
Lin, XH