On nonlinear random effects models for repeated measurements

Kathryn Hirst, Gary O. Zerbe, David W. Boyle, Randall B. Wilkening

Research output: Contribution to journalArticle

34 Scopus citations


Linear random effects models for longitudinal data discussed by Laird and Ware (1982), Jennrich and Schluchter (1986), Lange and Laird (1989), and others are extended in a straight forward manner to nonlinear random effects models. This results in a simple computational approach which accommodates patterned covariance matrices and data insufficient for fitting each subject separately. The technique is demonstrated with an interesting medical data set, and a short, simple SAS PROC IML program based on the EM algorithm is presented.

Original languageEnglish (US)
Pages (from-to)463-478
Number of pages16
JournalCommunications in Statistics - Simulation and Computation
Issue number2-3
StatePublished - Jan 1 1991
Externally publishedYes


  • EM
  • algorithm
  • longitudinal data
  • nonlinear mixed effects model
  • stochastic parameters

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation

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