Fitting and interpreting continuous-time latent Markov models for panel data

Jane M. Lange, Vladimir N. Minin

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Multistate models characterize disease processes within an individual. Clinical studies often observe the disease status of individuals at discrete time points, making exact times of transitions between disease states unknown. Such panel data pose considerable modeling challenges. Assuming the disease process progresses accordingly, a standard continuous-time Markov chain (CTMC) yields tractable likelihoods, but the assumption of exponential sojourn time distributions is typically unrealistic. More flexible semi-Markov models permit generic sojourn distributions yet yield intractable likelihoods for panel data in the presence of reversible transitions. One attractive alternative is to assume that the disease process is characterized by an underlying latent CTMC, with multiple latent states mapping to each disease state. These models retain analytic tractability due to the CTMC framework but allow for flexible, duration-dependent disease state sojourn distributions. We have developed a robust and efficient expectation-maximization algorithm in this context. Our complete data state space consists of the observed data and the underlying latent trajectory, yielding computationally efficient expectation and maximization steps. Our algorithm outperforms alternative methods measured in terms of time to convergence and robustness. We also examine the frequentist performance of latent CTMC point and interval estimates of disease process functionals based on simulated data. The performance of estimates depends on time, functional, and data-generating scenario. Finally, we illustrate the interpretive power of latent CTMC models for describing disease processes on a dataset of lung transplant patients. We hope our work will encourage wider use of these models in the biomedical setting.

Original languageEnglish (US)
Pages (from-to)4581-4595
Number of pages15
JournalStatistics in Medicine
Volume32
Issue number26
DOIs
StatePublished - Nov 20 2013
Externally publishedYes

Keywords

  • Disease process
  • EM algorithm
  • Multistate model
  • Panel data
  • Phase-type

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Fingerprint Dive into the research topics of 'Fitting and interpreting continuous-time latent Markov models for panel data'. Together they form a unique fingerprint.

Cite this