Progressive multi-state models for informatively incomplete longitudinal data

Baojiang Chen, Grace Y. Yi, Richard J. Cook

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Progressive multi-state models provide a convenient framework for characterizing chronic disease processes where the states represent the degree of damage resulting from the disease. Incomplete data often arise in studies of such processes, and standard methods of analysis can lead to biased parameter estimates when observation of data is response-dependent. This paper describes a joint analysis useful for fitting progressive multi-state models to data arising in longitudinal studies in such settings. Likelihood based methods are described and parameters are shown to be identifiable. An EM algorithm is described for parameter estimation, and variance estimation is carried out using the Louis' method. Simulation studies demonstrate that the proposed method works well in practice under a variety of settings. An application to data from a smoking prevention study illustrates the utility of the method.

Original languageEnglish (US)
Pages (from-to)80-93
Number of pages14
JournalJournal of Statistical Planning and Inference
Volume141
Issue number1
DOIs
StatePublished - Jan 1 2011
Externally publishedYes

Keywords

  • Dependent observation
  • EM algorithm
  • Longitudinal data
  • Maximum likelihood
  • Progressive Markov model
  • Response dependent missingness

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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