A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers.

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  • Additional Information
    • Source:
      Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
    • Publication Information:
      Original Publication: Chichester ; New York : Wiley, c1982-
    • Subject Terms:
      Likelihood Functions* ; Longitudinal Studies* ; Multivariate Analysis*; Accelerometry ; Algorithms ; Computer Simulation ; Exercise ; Humans ; Linear Models
    • Abstract:
      A mixed effect model is proposed to jointly analyze multivariate longitudinal data with continuous, proportion, count, and binary responses. The association of the variables is modeled through the correlation of random effects. We use a quasi-likelihood type approximation for nonlinear variables and transform the proposed model into a multivariate linear mixed model framework for estimation and inference. Via an extension to the EM approach, an efficient algorithm is developed to fit the model. The method is applied to physical activity data, which uses a wearable accelerometer device to measure daily movement and energy expenditure information. Our approach is also evaluated by a simulation study.
      (Copyright © 2017 John Wiley & Sons, Ltd.)
    • References:
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    • Grant Information:
      U01 CA057030 United States CA NCI NIH HHS
    • Contributed Indexing:
      Keywords: accelerometers; longitudinal data; mixed effects model; multivariate longitudinal data; penalized quasi-likelihood
    • Publication Date:
      Date Created: 20170809 Date Completed: 20180626 Latest Revision: 20181113
    • Publication Date:
      20231215
    • Accession Number:
      PMC5656438
    • Accession Number:
      10.1002/sim.7401
    • Accession Number:
      28786180