Covariate-adjusted regression for longitudinal data incorporating correlation between repeated measurements

Danh V. Nguyen, Damla Şentürk

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Summary We propose an estimation method that incorporates the correlation/covariance structure between repeated measurements in covariate-adjusted regression models for distorted longitudinal data. In this distorted data setting, neither the longitudinal response nor (possibly time-varying) predictors are directly observable. The unobserved response and predictors are assumed to be distorted/contaminated by unknown functions of a common observable confounder. The proposed estimation methodology adjusts for the distortion effects both in estimation of the covariance structure and in the regression parameters using generalized least squares. The finite-sample performance of the proposed estimators is studied numerically by means of simulations. The consistency and convergence rates of the proposed estimators are also established. The proposed method is illustrated with an application to data from a longitudinal study of cognitive and social development in children.

Original languageEnglish (US)
Pages (from-to)319-333
Number of pages15
JournalAustralian and New Zealand Journal of Statistics
Volume51
Issue number3
DOIs
StatePublished - Sep 2009

Keywords

  • Binning
  • Clustered data
  • Covariance structure
  • General linear model
  • Generalized least squares
  • Multiplicative effect
  • Varying-coefficient models

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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