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Biostatistics Advance Access published online on October 26, 2005
Biostatistics, doi:10.1093/biostatistics/kxj004
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© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.
Received April 26, 2005
Revised October 5, 2005
Accepted October 21, 2005

Article

Covariate Adjusted Varying Coefficient Models

Damla Sentürk 1*

1 Department of Statistics, Pennsylvania State University University Park, PA 16802, U.S.A.

* To whom correspondence should be addressed.
Damla Sentürk, E-mail: dsenturk{at}stat.psu.edu


  Abstract

Covariate adjusted regression (CAR) was recently proposed for situations where both predictors and response in a regression model are not directly observed, but are observed after being contaminated by unknown functions of a common observable covariate. The method has been appealing, because of its flexibility in targeting the regression coefficients under different forms of distortion. We extend this methodology proposed for regression into the framework of varying coefficient models, where the goal is to target the covariate adjusted relationship between longitudinal variables. The proposed method of covariate adjusted varying coefficient models (CAVCM) is illustrated with an analysis of a longitudinal data set containing calcium absorbtion and intake measurements on 188 subjects. We estimate the age dependent relationship between these two variables adjusted for the covariate body surface area. Simulation studies demonstrate the flexibility of CAVCM in handling different forms of distortion in the longitudinal setting.

Keywords: Covariate adjusted regression; Local polynomial regression; Longitudinal data; Multiplicative effects; Smoothing.
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