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Multivariate longitudinal data analysis with mixed effects hidden Markov models

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  • Jesse D. Raffa
  • Joel A. Dubin

Abstract

Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies.

Suggested Citation

  • Jesse D. Raffa & Joel A. Dubin, 2015. "Multivariate longitudinal data analysis with mixed effects hidden Markov models," Biometrics, The International Biometric Society, vol. 71(3), pages 821-831, September.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:3:p:821-831
    DOI: 10.1111/biom.12296
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    References listed on IDEAS

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    7. Proust-Lima, Cécile & Joly, Pierre & Dartigues, Jean-François & Jacqmin-Gadda, Hélène, 2009. "Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1142-1154, February.
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    Cited by:

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    4. Ruijin Lu & Tonja R. Nansel & Zhen Chen, 2023. "A Perception-Augmented Hidden Markov Model for Parent–Child Relations in Families of Youth with Type 1 Diabetes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 288-308, April.

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