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Design evaluation and optimisation in crossover pharmacokinetic studies analysed by nonlinear mixed effects models
Author(s) -
Nguyen Thu Thuy,
Bazzoli Caroline,
Mentré France
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4390
Subject(s) - crossover , bioequivalence , crossover study , equivalence (formal languages) , covariate , population , wald test , mathematics , fisher information , statistics , optimal design , random effects model , nonlinear system , computer science , econometrics , pharmacokinetics , statistical hypothesis testing , medicine , meta analysis , pharmacology , artificial intelligence , physics , alternative medicine , environmental health , pathology , discrete mathematics , quantum mechanics , placebo
Bioequivalence or interaction trials are commonly studied in crossover design and can be analysed by nonlinear mixed effects models as an alternative to noncompartmental approach. We propose an extension of the population Fisher information matrix in nonlinear mixed effects models to design crossover pharmacokinetic trials, using a linearisation of the model around the random effect expectation, including within‐subject variability and discrete covariates fixed or changing between periods. We use the expected standard errors of treatment effect to compute the power for the Wald test of comparison or equivalence and the number of subjects needed for a given power. We perform various simulations mimicking crossover two‐period trials to show the relevance of these developments. We then apply these developments to design a crossover pharmacokinetic study of amoxicillin in piglets and implement them in the new version 3.2 of the r function PFIM. Copyright © 2011 John Wiley & Sons, Ltd.