An efficient and robust method for analyzing population pharmacokinetic data in genome-wide pharmacogenomic studies: a generalized estimating equation approach

TL;DR

This paper introduces a generalized estimating equation (GEE) method for analyzing population pharmacokinetic data in genome-wide pharmacogenomic studies, offering faster computation and robustness against model misspecification compared to traditional nonlinear mixed-effect models.

Contribution

The paper develops a valid GEE-based approach for population PK analysis that is computationally efficient and robust to random-effects distribution assumptions, with theoretical justification.

Findings

GEE approach shows high computational speed and stability.

GEE inference remains valid under any distribution of random effects.

Application to pharmacogenomic data demonstrates practical utility.

Abstract

Powerful array-based single-nucleotide polymorphism--typing platforms have recently heralded a new era in which genome-wide studies are conducted with increasing frequency. A genetic polymorphism associated with population pharmacokinetics (PK) is typically analyzed using nonlinear mixed-effect models (NLMM). Applying NLMM to large-scale data, such as those generated by genome-wide studies, raises several issues related to the assumption of random effects, as follows: (i) Computation time: it takes a long time to compute the marginal likelihood. (ii) Convergence of iterative calculation: an adaptive Gauss-Hermite quadrature is generally used to estimate NLMM; however, iterative calculations may not converge in complex models. (iii) Random-effects misspecification leads to slightly inflated type-I error rates. As an alternative effective approach to resolving these issues, in this article we propose a generalized estimating equation (GEE) approach for analyzing population PK data. In general, GEE analysis does not account for inter-individual variability in PK parameters; therefore, the usual GEE estimators cannot be interpreted straightforwardly, and their validities have not been justified. Here, we propose valid inference methods for using GEE even under conditions of inter-individual variability, and provide theoretical justifications of the proposed GEE estimators for population PK data. In numerical evaluations by simulations, the proposed GEE approach exhibited high computational speed and stability relative to the NLMM approach. Furthermore, the NLMM analysis was sensitive to the misspecification of the random-effects distribution, and the proposed GEE inference is valid for any distributional form. An illustration is provided using data from a genome-wide pharmacogenomic study of an anticancer drug.