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Correcting for Measurement Error in Individual‐Level Covariates in Nonlinear Mixed Effects Models
Author(s) -
KO Hyejin,
Davidian Marie
Publication year - 2000
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2000.00368.x
Subject(s) - covariate , observational error , estimator , covariance , statistics , errors in variables models , mixed model , random effects model , econometrics , calibration , analysis of covariance , standard error , regression , mathematics , nonlinear system , computer science , medicine , meta analysis , physics , quantum mechanics
Summary. The nonlinear mixed effects model is used to represent data in pharmacokinetics, viral dynamics, and other areas where an objective is to elucidate associations among individual‐specific model parameters and covariates; however, covariates may be measured with error. For additive measurement error, we show substitution of mismeasured covariates for true covariates may lead to biased estimators for fixed effects and random effects covariance parameters, while regression calibration may eliminate bias in fixed effects but fail to correct that in covariance parameters. We develop methods to take account of measurement error that correct this bias and may be implemented with standard software, and we demonstrate their utility via simulation and application to data from a study of HIV dynamics.