  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.
