Some Alternatives to Asymptotic Tests for the Analysis of Pharmacogenetic Data Using Nonlinear Mixed Effects Models

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 Wa...

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Bibliographic Details
Published in:Biometrics Vol. 68; no. 1; pp. 146 - 155
Main Authors: Bertrand, Julie, Comets, Emmanuelle, Chenel, Marylore, Mentré, France
Format: Journal Article
Language:English
Published: Malden, USA Blackwell Publishing Inc 01.03.2012
Wiley-Blackwell
Blackwell Publishing Ltd
Subjects:
algorithms
Asymptotic methods
BIOMETRIC METHODOLOGY
Biometrics
biometry
Biometry - methods
Computer Simulation
Data Interpretation, Statistical
Datasets
Drug design
drugs
Estimating techniques
F-distribution-based approach
First-order approximation
genes
Genetics
genotype
Humans
Mathematical vectors
Modeling
Models, Statistical
Nonlinear Dynamics
Nonlinear mixed effects models
P values
Parametric models
patients
Permutation tests
Pharmacogenetics
Pharmacogenetics - methods
Pharmacogenetics - statistics & numerical data
pharmacogenomics
Pharmacokinetics
Pharmacology
Statistical variance
statistics
ISSN:0006-341X, 1541-0420, 1541-0420
Online Access:Get full text
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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.
Bibliography:http://dx.doi.org/10.1111/j.1541-0420.2011.01665.x
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ArticleID:BIOM1665
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/j.1541-0420.2011.01665.x