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Two-part hidden Markov models for semicontinuous longitudinal data with nonignorable missing covariates.

Xiaoxiao Zhou1, Kai Kang1, Xinyuan Song1

  • 1Department of Statistics, Chinese University of Hong Kong, Hong Kong.

Statistics in Medicine
|February 27, 2020
PubMed
Summary

This study introduces a novel two-part hidden Markov model (HMM) for analyzing semicontinuous longitudinal data with missing covariates. The model provides new insights into Alzheimer's disease pathology and risk factors.

Keywords:
Bayesian adaptive lassohidden Markov modelsnonignorable missing covariatessemicontinuous datatwo-part models

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Longitudinal data with semicontinuous outcomes and missing covariates present analytical challenges.
  • Standard statistical models may not adequately capture the complex dynamics of such data.
  • Understanding Alzheimer's Disease progression requires robust analytical methods.

Purpose of the Study:

  • To develop a flexible statistical framework for analyzing semicontinuous longitudinal data with missing covariates.
  • To investigate the influence of covariates on transitions between hidden states and their effects on the response.
  • To apply the developed model to the Alzheimer's Disease Neuroimaging Initiative dataset for novel insights.

Main Methods:

  • A two-part hidden Markov model (HMM) was developed, separating semicontinuous variables into binary (zero occurrence) and continuous components.
  • The HMM incorporates a transition model for covariate effects on state transitions and a conditional regression model for state-specific covariate effects.
  • A Bayesian adaptive least absolute shrinkage and selection operator (lasso) procedure was employed for simultaneous variable selection and estimation.
  • Shared random effects were included to address unobserved heterogeneity and non-ignorable missing covariates.

Main Results:

  • The proposed hidden Markov model effectively analyzes semicontinuous longitudinal data with missing covariates.
  • The Bayesian adaptive lasso procedure enabled simultaneous variable selection and estimation.
  • Application to the Alzheimer's Disease Neuroimaging Initiative dataset yielded significant findings.
  • New insights into Alzheimer's disease pathology and potential risk factors were obtained.

Conclusions:

  • The developed two-part hidden Markov model offers a powerful tool for analyzing complex semicontinuous longitudinal data.
  • The methodology effectively handles missing covariates and unobserved heterogeneity.
  • The study provides valuable new perspectives on Alzheimer's disease progression and risk factors.