Applying Linear Mixed-Effects Models to the Problem of Measurement Error in Epidemiologic Studies

DOI10.1081/SAC-120017500zbMath1075.62649OpenAlexW2035282033MaRDI QIDQ4707017

Christopher H. Morrell, Geert Verbeke, Jay D. Pearson, Jerome L. Fleg, Larry J. Brant

Publication date: 4 June 2003

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1081/sac-120017500

zbMATH Keywords

Attenuation, Logistic regression, Measurement error bias, Mixed-effects regression, Regression dilution bias

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (1)

Modeling a cross-sectional response variable with longitudinal predictors: an example of pulse pressure and pulse wave velocity

Cites Work

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• Random-Effects Models for Longitudinal Data
• Biostatistics and Bayes. With comments and a rejoinder by the author
• Covariate measurement errors and parameter estimation in a failure time regression model
• The loss in efficiency from misspecifying covariates in proportional hazards regression models
• Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data
• Maximum Likelihood Computations with Repeated Measures: Application of the EM Algorithm
• Informative Drop-Out in Longitudinal Data Analysis

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