Latent time-varying factors in longitudinal analysis: a linear mixed hidden Markov model for heart rates

Longitudinal data are often segmented by unobserved time‐varying factors, which introduce latent heterogeneity at the observation level, in addition to heterogeneity across subjects. We account for this latent structure by a linear mixed hidden Markov model. It integrates subject‐specific random eff...

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Bibliographic Details
Published inStatistics in medicine Vol. 33; no. 23; pp. 4116 - 4134
Main Authors Lagona, Francesco, Jdanov, Dmitri, Shkolnikova, Maria
Format Journal Article
LanguageEnglish
Published England Blackwell Publishing Ltd 15.10.2014
Wiley Subscription Services, Inc
Subjects
Age Distribution
Aged
Aged, 80 and over
Aging - physiology
Aging - psychology
Algorithms
Biomarkers
Cardiovascular system
Comorbidity
EM algorithm
Female
Health Status
Heart Diseases - epidemiology
Heart Rate
hidden Markov model
Humans
Likelihood Functions
linear mixed model
Linear Models
longitudinal data
Longitudinal Studies
Male
Markov analysis
Markov Chains
Medical statistics
Middle Aged
Russia - epidemiology
Sex Distribution
Stress, Psychological - epidemiology
Stress, Psychological - physiopathology
Time Factors
Russia
heart rate
hidden Markov model
linear mixed model
EM algorithm
longitudinal data
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Summary:Longitudinal data are often segmented by unobserved time‐varying factors, which introduce latent heterogeneity at the observation level, in addition to heterogeneity across subjects. We account for this latent structure by a linear mixed hidden Markov model. It integrates subject‐specific random effects and Markovian sequences of time‐varying effects in the linear predictor. We propose an expectationŰ‐maximization algorithm for maximum likelihood estimation, based on data augmentation. It reduces to the iterative maximization of the expected value of a complete likelihood function, derived from an augmented dataset with case weights, alternated with weights updating. In a case study of the Survey on Stress Aging and Health in Russia, the model is exploited to estimate the influence of the observed covariates under unobserved time‐varying factors, which affect the cardiovascular activity of each subject during the observation period. Copyright © 2014 John Wiley & Sons, Ltd.
Bibliography:Supporting info item
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lagona@uniroma3.it
ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.6220