On nonlinear random effects models for repeated measurements

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

Research output: Contribution to journalArticlepeer-review

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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