Using a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction

Michael R. Elliott, Joseph J Gallo, Thomas R. Ten Have, Hillary R. Bogner, Ira R. Katz

Research output: Contribution to journalArticle

Abstract

Positive and negative affect data are often collected over time in psychiatric care settings, yet no generally accepted means are available to relate these data to useful diagnoses or treatments. Latent class analysis attempts data reduction by classifying subjects into one of K unobserved classes based on observed data. Latent class models have recently been extended to accommodate longitudinally observed data. We extend these approaches in a Bayesian framework to accommodate trajectories of both continuous and discrete data. We consider whether latent class models might be used to distinguish patients on the basis of trajectories of observed affect scores, reported events, and presence or absence of clinical depression.

Original languageEnglish (US)
Pages (from-to)119-143
Number of pages25
JournalBiostatistics
Volume6
Issue number1
DOIs
Publication statusPublished - 2005
Externally publishedYes

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Keywords

  • Cardiovascular disease
  • Depression
  • DIC
  • General growth mixture modeling
  • Gibbs sampling
  • Label switching
  • Model choice

ASJC Scopus subject areas

  • Medicine(all)
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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