Non-linear random effects model for multivariate responses with missing data

Guillermo Marshall, Rolando De la Cruz-Mesía, Anna E. Barón, James H. Rutledge, Gary O. Zerbe

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


The use of random-effects models for the analysis of longitudinal data with missing responses has been discussed by several authors. In this paper, we extend the non-linear random-effects model for a single response to the case of multiple responses, allowing for arbitrary patterns of observed and missing data. Parameters for this model are estimated via the EM algorithm and by the first-order approximation available in SAS Proc NLMIXED. The set of equations for this estimation procedure is derived and these are appropriately modified to deal with missing data. The methodology is illustrated with an example using data coming from a study involving 161 pregnant women presenting to a private obstetrics clinic in Santiago, Chile.

Original languageEnglish
Pages (from-to)2817-2830
Number of pages14
JournalStatistics in Medicine
Issue number16
StatePublished - 30 Aug 2006


  • EM algorithm
  • Longitudinal data
  • Missing data
  • Multiple responses
  • Random effects


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