Covariate-Adjusted Linear Mixed Effects Model with an Application to Longitudinal Data. Academic Article uri icon

abstract

  • 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 normalizing 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 modeled 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.

published proceedings

  • J Nonparametr Stat

author list (cited authors)

  • Nguyen, D. V., Sentrk, D., & Carroll, R. J.

citation count

  • 34

complete list of authors

  • Nguyen, Danh V||Sentürk, Damla||Carroll, Raymond J

publication date

  • August 2008