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A latent factor linear mixed model for high-dimensional longitudinal data analysis.

Publication ,  Journal Article
An, X; Yang, Q; Bentler, PM
Published in: Statistics in medicine
October 2013

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.

Duke Scholars

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Published In

Statistics in medicine

DOI

EISSN

1097-0258

ISSN

0277-6715

Publication Date

October 2013

Volume

32

Issue

24

Start / End Page

4229 / 4239

Related Subject Headings

  • Statistics & Probability
  • Physical Fitness
  • Male
  • Longitudinal Studies
  • Linear Models
  • Humans
  • Female
  • Factor Analysis, Statistical
  • Data Interpretation, Statistical
  • Cognition
 

Citation

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An, X., Yang, Q., & Bentler, P. M. (2013). A latent factor linear mixed model for high-dimensional longitudinal data analysis. Statistics in Medicine, 32(24), 4229–4239. https://doi.org/10.1002/sim.5825
An, Xinming, Qing Yang, and Peter M. Bentler. “A latent factor linear mixed model for high-dimensional longitudinal data analysis.Statistics in Medicine 32, no. 24 (October 2013): 4229–39. https://doi.org/10.1002/sim.5825.
An X, Yang Q, Bentler PM. A latent factor linear mixed model for high-dimensional longitudinal data analysis. Statistics in medicine. 2013 Oct;32(24):4229–39.
An, Xinming, et al. “A latent factor linear mixed model for high-dimensional longitudinal data analysis.Statistics in Medicine, vol. 32, no. 24, Oct. 2013, pp. 4229–39. Epmc, doi:10.1002/sim.5825.
An X, Yang Q, Bentler PM. A latent factor linear mixed model for high-dimensional longitudinal data analysis. Statistics in medicine. 2013 Oct;32(24):4229–4239.
Journal cover image

Published In

Statistics in medicine

DOI

EISSN

1097-0258

ISSN

0277-6715

Publication Date

October 2013

Volume

32

Issue

24

Start / End Page

4229 / 4239

Related Subject Headings

  • Statistics & Probability
  • Physical Fitness
  • Male
  • Longitudinal Studies
  • Linear Models
  • Humans
  • Female
  • Factor Analysis, Statistical
  • Data Interpretation, Statistical
  • Cognition