title: Latent time-varying factors in longitudinal analysis: a linear mixed hidden Markov model for heart rates.
abstract: 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.
authors.affiliation: University of Roma Tre, Rome, Italy; Max Planck Institute for Demographic Research, Rostock, Germany.
authors.forename: Francesco
authors.initials: F
authors.lastname: Lagona
authors.forename: Dmitri
authors.initials: D
authors.lastname: Jdanov
authors.forename: Maria
authors.initials: M
authors.lastname: Shkolnikova
mesh_terms: D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology* / Q000523:psychology; D015415:Biomarkers; D015897:Comorbidity; D005260:Female; D006304:Health Status; D006331:Heart Diseases / Q000453:epidemiology*; D006339:Heart Rate; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D012426:Russia / Q000453:epidemiology; D017678:Sex Distribution; D013315:Stress, Psychological / Q000453:epidemiology / Q000503:physiopathology*; D013997:Time Factors
chemical_list: D015415:Biomarkers
keywords: EM algorithm; heart rate; hidden Markov model; linear mixed model; longitudinal data