A Two-Step Approach to Measurement Error in Time-Dependent Covariates in Nonlinear Mixed-Effects Models, with Application to IGF-I Pharmacokinetics

The usual approach to the analysis of population pharmacokinetic studies is to represent the concentration-time data by a nonlinear mixed-effects model. Primary objectives are to characterize the pattern of drug disposition in the population and to identify individual-specific covariates associated with pharmacokinetic behavior. We consider data from a study of insulin-like growth factor I (IGF-I) administered by intravenous infusion to patients with severe head trauma. Failure to maintain steady-state levels of IGF-I was thought to be related to the temporal pattern of several covariates measured in the study, and an analysis investigating this issue was of interest. Observations on these potentially relevant covariates for each subject were made at time points different from those at which IGF-I concentrations were determined; moreover, the covariates themselves were likely subject to measurement error. The usual approach to time-dependent covariates in population analysis is to invoke a simple interpolation scheme, such as carrying forward the most recent covariate value, ignoring measurement error; however, for these data, the complicated observed covariate pattern makes this approach suspect. A nonlinear mixed-effects model incorporating a model for time-dependent covariates measured with error is used to describe the IGF-I data, and fitting is accomplished by a two-step strategy implemented using standard software. The performance of the method is evaluated via simulation.