Covariate-adjusted linear mixed effects model with an application to longitudinal data

Danh V. Nguyen, Damla Şentürk, Raymond J. Carroll

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Linear mixed effects (LME) models are useful for longitudinal data/repeated measurements. We propose a new class of covariate-adjusted LME models for longitudinal data that nonparametrically adjusts for a normalising covariate. The proposed approach involves fitting a parametric LME model to the data after adjusting for the nonparametric effects of a baseline confounding covariate. In particular, the effect of the observable covariate on the response and predictors of the LME model is modelled nonparametrically via smooth unknown functions. In addition to covariate-adjusted estimation of fixed/population parameters and random effects, an estimation procedure for the variance components is also developed. Numerical properties of the proposed estimators are investigated with simulation studies. The consistency and convergence rates of the proposed estimators are also established. An application to a longitudinal data set on calcium absorption, accounting for baseline distortion from body mass index, illustrates the proposed methodology.

Original languageEnglish (US)
Pages (from-to)459-481
Number of pages23
JournalJournal of Nonparametric Statistics
Issue number6
StatePublished - Aug 2008


  • Binning
  • Covariance structure
  • Covariate-adjusted regression (CAR)
  • Longitudinal data
  • Mixed model
  • Multiplicative effect
  • Varying coefficient models

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability


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