title: Some alternatives to asymptotic tests for the analysis of pharmacogenetic data using nonlinear mixed effects models.
abstract: Nonlinear mixed effects models allow investigating individual differences in drug concentration profiles (pharmacokinetics) and responses. Pharmacogenetics focuses on the genetic component of this variability. Two tests often used to detect a gene effect on a pharmacokinetic parameter are (1) the Wald test, assessing whether estimates for the gene effect are significantly different from 0 and (2) the likelihood ratio test comparing models with and without the genetic effect. Because those asymptotic tests show inflated type I error on small sample size and/or with unevenly distributed genotypes, we develop two alternatives and evaluate them by means of a simulation study. First, we assess the performance of the permutation test using the Wald and the likelihood ratio statistics. Second, for the Wald test we propose the use of the F-distribution with four different values for the denominator degrees of freedom. We also explore the influence of the estimation algorithm using both the first-order conditional estimation with interaction linearization-based algorithm and the stochastic approximation expectation maximization algorithm. We apply these methods to the analysis of the pharmacogenetics of indinavir in HIV patients recruited in the COPHAR2-ANRS 111 trial. Results of the simulation study show that the permutation test seems appropriate but at the cost of an additional computational burden. One of the four F-distribution-based approaches provides a correct type I error estimate for the Wald test and should be further investigated.
authors: UMR 738, INSERM, Université Paris Diderot, Paris, France. julie.bertrand@inserm.fr
authors: Julie
authors: J
authors: Bertrand
authors: Emmanuelle
authors: E
authors: Comets
authors: Marylore
authors: M
authors: Chenel
authors: France
authors: F
authors: Mentré
mesh_terms: D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010597:Pharmacogenetics / Q000379:methods* / Q000706:statistics & numerical data*