Correcting for measurement error in individual-level covariates in nonlinear mixed effects models

Area of Science:

• Biostatistics
• Pharmacometrics
• Epidemiology

Background:

• Nonlinear mixed effects (NLME) models are crucial for analyzing complex data in pharmacokinetics, viral dynamics, and other fields.
• Accurate estimation of associations between model parameters and covariates is often hindered by measurement error in covariates.

Purpose of the Study:

• To develop and validate methods for addressing additive measurement error in covariates within NLME models.
• To correct bias in both fixed effects and random effects covariance parameters caused by mismeasured covariates.

Main Methods:

• The study investigates the impact of covariate measurement error on NLME model estimators.
• Methods are developed to account for additive measurement error, offering corrections for biased parameter estimates.
• Regression calibration is evaluated as a potential bias-correction technique.

Main Results:

• Substitution of mismeasured covariates introduces bias in fixed and random effects covariance estimators.
• Regression calibration corrects bias in fixed effects but not in covariance parameters.
• The proposed methods effectively correct bias in NLME models with measurement error in covariates.

Conclusions:

• Accurate covariate measurement is essential for unbiased parameter estimation in NLME models.
• The developed methods provide a robust solution for handling covariate measurement error in various scientific applications.
• These techniques are implementable with standard statistical software, facilitating their practical use.