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Conditional Estimation for Generalized Linear Models When Covariates Are Subject-Specific Parameters in a Mixed Model for Longitudinal Measurements
Journal article   Peer reviewed

Conditional Estimation for Generalized Linear Models When Covariates Are Subject-Specific Parameters in a Mixed Model for Longitudinal Measurements

Erning Li, Daowen Zhang and Marie Davidian
Biometrics, Vol.60(1), pp.1-7
Received May 2003. Revised October 2003. Accepted October 2003.
03/2004
DOI: 10.1111/j.0006-341X.2004.00170.x
PMCID: PMC1628348
PMID: 15032767
url
http://doi.org/10.1111/j.0006-341X.2004.00170.xView
Open Access

Abstract

The relationship between a primary endpoint and features of longitudinal profiles of a continuous response is often of interest, and a relevant framework is that of a generalized linear model with covariates that are subject-specific random effects in a linear mixed model for the longitudinal measurements. Naive implementation by imputing subject-specific effects from individual regression fits yields biased inference, and several methods for reducing this bias have been proposed. These require a parametric (normality) assumption on the random effects, which may be unrealistic. Adapting a strategy of Stefanski and Carroll (1987, Biometrika 74, 703-716), we propose estimators for the generalized linear model parameters that require no assumptions on the random effects and yield consistent inference regardless of the true distribution. The methods are illustrated via simulation and by application to a study of bone mineral density in women transitioning to menopause.
 Conditional score Longitudinal data Measurement error Mixed-effects model Regression calibration Semiparametric

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