A latent factor linear mixed model for high-dimensional longitudinal data analysis.
Abstract
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. Although 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, we reduced the high-dimensional responses
to low-dimensional latent factors by the factor analysis model, and then we used the
multivariate linear mixed model to study the longitudinal trends of these latent factors.
We developed an expectation-maximization algorithm to estimate the model. We used
simulation studies to investigate the computational properties of the expectation-maximization
algorithm and compare the LFLMM model with other approaches for high-dimensional longitudinal
data analysis. We used a real data example to illustrate the practical usefulness
of the model.
Type
Journal articleSubject
HumansData Interpretation, Statistical
Factor Analysis, Statistical
Linear Models
Longitudinal Studies
Cognition
Algorithms
Physical Fitness
Aged
Female
Male
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https://hdl.handle.net/10161/16701Published Version (Please cite this version)
10.1002/sim.5825Publication Info
An, Xinming; Yang, Qing; & Bentler, Peter M (2013). A latent factor linear mixed model for high-dimensional longitudinal data analysis.
Statistics in medicine, 32(24). 10.1002/sim.5825. Retrieved from https://hdl.handle.net/10161/16701.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Qing Yang
Associate Research Professor in the School of Nursing
Dr. Qing Yang is Associate Professor and Biostatistician at Duke School of Nursing.
She received her PhD in Biostatistics from University of California, Los Angeles.
Dr. Yang’s statistical expertise is longitudinal data analysis and time-to-event data
analysis. As a biostatistician, she has extensive experience collaborating with researchers
in different therapeutic areas, including diabetes, cancer, cardiovascular disease
and mental health. Her current research interests are advanced late

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