A ramdom-effects Markov transition model for poisson-distributed repeated measures with non-ignorable missing values

Jinhui Li, Xiaowei Yang, Yingnian Wu, Steven Shoptaw

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

In biomedical research with longitudinal designs, missing values due to intermittent non-response or premature withdrawal are usually 'non-ignorable' in the sense that unobserved values are related to the patterns of missingness. By drawing the framework of a shared-parameter mechanism, the process yielding the repeated count measures and the process yielding missing values can be modelled separately, conditionally on a group of shared parameters. For chronic diseases, Markov transition models can be used to study the transitional features of the pathologic processes. In this paper, Markov Chain Monte Carlo algorithms are developed to fit a random-effects Markov transition model for incomplete count repeated measures, within which random effects are shared by the counting process and the missing-data mechanism. Assuming a Poisson distribution for the count measures, the transition probabilities are estimated using a Poisson regression model. The missingness mechanism is modelled with a multinomial-logit regression to calculate the transition probabilities of the missingness indicators. The method is demonstrated using both simulated data sets and a practical data set from a smoking cessation clinical trial.

Original languageEnglish (US)
Pages (from-to)2519-2532
Number of pages14
JournalStatistics in Medicine
Volume26
Issue number12
DOIs
StatePublished - May 30 2007

Keywords

  • Markov transition models
  • Non-ignorable missing values
  • Poisson regression model
  • Repeated measures
  • Shared-parameter missingness

ASJC Scopus subject areas

  • Epidemiology

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