Latent transition regression for mixed outcomes

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Health status is a complex outcome, often characterized by multiple measures. When assessing changes in health status over time, multiple measures are typically collected longitudinally. Analytic challenges posed by these multivariate longitudinal data are further complicated when the outcomes are combinations of continuous, categorical, and count data. To address these challenges, we propose a fully Bayesian latent transition regression approach for jointly analyzing a mixture of longitudinal outcomes from any distribution. Health status is assumed to be a categorical latent variable, and the multiple outcomes are treated as surrogate measures of the latent health state, observed with error. Using this approach, both baseline latent health state prevalences and the probabilities of transitioning between the health states over time are modeled as functions of covariates. The observed outcomes are related to the latent health states through regression models that include subject-specific effects to account for residual correlation among repeated measures over time, and covariate effects to account for differential measurement of the latent health states. We illustrate our approach with data from a longitudinal study of back pain.

Original languageEnglish (US)
Pages (from-to)710-720
Number of pages11
JournalBiometrics
Volume59
Issue number3
DOIs
StatePublished - Sep 2003
Externally publishedYes

Keywords

  • Hierarchical model
  • Latent class analysis
  • Latent traits
  • Latent transition analysis
  • Longitudinal data
  • Markov chain Monte Carlo
  • Multivariate response

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Public Health, Environmental and Occupational Health
  • Agricultural and Biological Sciences (miscellaneous)
  • Applied Mathematics
  • Statistics and Probability

Fingerprint

Dive into the research topics of 'Latent transition regression for mixed outcomes'. Together they form a unique fingerprint.

Cite this