Non‐linear models for the analysis of longitudinal data

Edward F. Vonesh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

Abstract

Given the importance of longitudinal studies in biomedical research, it is not surprising that considerable attention has been given to linear and generalized linear models for the analysis of longitudinal data. A great deal of attention has also been given to non‐linear models for repeated measurements, particularly in the field of pharmacokinetics. In this article, a brief overview of non‐linear models for the analysis of repeated measures is given. Particular emphasis is placed on mixed‐effects non‐linear models and on various estimation procedures proposed for such models. Several of these estimation procedures are compared via a simulation study. In addition, simulation is used to investigate the effects of model misspecification, particularly with respect to one's choice of random‐effects. A relatively straightforward measure useful in selecting an appropriate set of random effects is investigated and found to perform reasonably well.

Original languageEnglish (US)
Pages (from-to)1929-1954
Number of pages26
JournalStatistics in Medicine
Volume11
Issue number14-15
DOIs
StatePublished - Jan 1 1992

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Fingerprint Dive into the research topics of 'Non‐linear models for the analysis of longitudinal data'. Together they form a unique fingerprint.

Cite this