Multivariate longitudinal models for complex change processes

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

Longitudinal studies offer us an opportunity to develop detailed descriptions of the process of growth and development or of the course of progression of chronic diseases. Most longitudinal analyses focus on characterizing change over time in a single outcome variable and identifying predictors of growth or decline. Both growth and degenerative diseases, however, are complex processes with multiple markers of change, so that it may be important to model more than one outcome measure and to understand their relationship over time. We consider random effects models for the association between the trajectories of two outcomes over time, and then compare their properties to approaches based on separate ordinary least-squares estimates of change. We then illustrate with an example from the Religious Orders Study, a longitudinal cohort study of more than 900 members of Catholic religious orders who have had up to eight annual clinical examinations.

Original languageEnglish (US)
Pages (from-to)231-239
Number of pages9
JournalStatistics in Medicine
Volume23
Issue number2
DOIs
StatePublished - Jan 30 2004

Fingerprint

Longitudinal Studies
Chronic Disease
Cohort Study
Least Squares Estimate
Ordinary Least Squares
Longitudinal Study
Random Effects Model
Growth
Least-Squares Analysis
Growth and Development
Progression
Annual
Predictors
Cohort Studies
Outcome Assessment (Health Care)
Model
Trajectory
Religion
Relationships

Keywords

  • Aging
  • Correlated growth processes
  • Longitudinal analysis
  • Multivariate data

ASJC Scopus subject areas

  • Epidemiology

Cite this

Multivariate longitudinal models for complex change processes. / Beckett, Laurel A; Tancredi, Daniel J; Wilson, R. S.

In: Statistics in Medicine, Vol. 23, No. 2, 30.01.2004, p. 231-239.

Research output: Contribution to journalArticle

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