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 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 language | English (US) |
|---|---|
| Pages (from-to) | 459-481 |
| Number of pages | 23 |
| Journal | Journal of Nonparametric Statistics |
| Volume | 20 |
| Issue number | 6 |
| DOIs | |
| State | Published - Aug 2008 |
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Keywords
- 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
Cite this
Covariate-adjusted linear mixed effects model with an application to longitudinal data. / Nguyen, Danh V.; Şentürk, Damla; Carroll, Raymond J.
In: Journal of Nonparametric Statistics, Vol. 20, No. 6, 08.2008, p. 459-481.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Covariate-adjusted linear mixed effects model with an application to longitudinal data
AU - Nguyen, Danh V.
AU - Şentürk, Damla
AU - Carroll, Raymond J.
PY - 2008/8
Y1 - 2008/8
N2 - 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.
AB - 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.
KW - Binning
KW - Covariance structure
KW - Covariate-adjusted regression (CAR)
KW - Longitudinal data
KW - Mixed model
KW - Multiplicative effect
KW - Varying coefficient models
UR - http://www.scopus.com/inward/record.url?scp=49549105313&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=49549105313&partnerID=8YFLogxK
U2 - 10.1080/10485250802226435
DO - 10.1080/10485250802226435
M3 - Article
VL - 20
SP - 459
EP - 481
JO - Journal of Nonparametric Statistics
JF - Journal of Nonparametric Statistics
SN - 1048-5252
IS - 6
ER -