Shared random effects analysis of multi-state Markov models: Application to a longitudinal study of transitions to dementia

Juan C. Salazar, Frederick A. Schmitt, Lei Yu, Marta M. Mendiondo, Richard J. Kryscio

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

Multi-state models are appealing tools for analysing data about the progression of a disease over time. In this paper, we consider a multi-state Markov chain with two competing absorbing states: dementia and death and three transient non-demented states: cognitively normal, amnestic mild cognitive impairment (amnestic MCI), and non-amnestic mild cognitive impairment (non-amnestic MCI). The likelihood function for the data is derived and estimates for the effects of the covariates on transitions are determined when the process can be viewed as a polytomous logistic regression model with shared random effects. The presence of a shared random effect not only complicates the formulation of the likelihood but also its evaluation and maximization. Three approaches for maximizing the likelihood are compared using a simulation study; the first method is based on the Gauss-quadrature technique, the second method is based on importance sampling ideas, and the third method is based on an expansion by Taylor series. The best approach is illustrated using a longitudinal study on a cohort of cognitively normal subjects, followed annually for conversion to mild cognitive impairment (MCI) and/or dementia, conducted at the Sanders Brown Center on Aging at the University of Kentucky.

Original languageEnglish
Pages (from-to)568-580
Number of pages13
JournalStatistics in Medicine
Volume26
Issue number3
DOIs
StatePublished - Feb 10 2007

Keywords

  • Alzheimer
  • Markov chain
  • Mild cognitive impairment
  • Multi-state models
  • Polytomous logistic regression

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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