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Some Alternatives to Asymptotic Tests for the Analysis of Pharmacogenetic Data Using Nonlinear Mixed Effects Models
Author(s) -
Bertrand Julie,
Comets Emmanuelle,
Chenel Marylore,
Mentré France
Publication year - 2012
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2011.01665.x
Subject(s) - wald test , score test , type i and type ii errors , statistics , likelihood ratio test , mathematics , linearization , sample size determination , resampling , permutation (music) , statistical hypothesis testing , nonlinear system , physics , quantum mechanics , acoustics
Summary 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.