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Estimation in AB/BA crossover trials with application to bioequivalence studies with incomplete and complete data designs
Author(s) -
Jaki Thomas,
Pallmann Philip,
Wolfsegger Martin J.
Publication year - 2013
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.5886
Subject(s) - bioequivalence , crossover , confidence interval , sample size determination , statistics , type i and type ii errors , context (archaeology) , computer science , coverage probability , crossover study , nominal level , sampling (signal processing) , mathematics , medicine , machine learning , paleontology , placebo , alternative medicine , filter (signal processing) , pathology , pharmacology , computer vision , biology , bioavailability
Crossover studies are frequently used in clinical research as they allow within‐subject comparisons instead of the between‐subject evaluation of parallel group designs. Estimation of interesting parameters from such designs is, however, not trivial. We provide three methods for estimating treatment effects and associated standard errors from an AB/BA crossover trial. Assuming at least asymptotic normality, we can obtain the confidence intervals for single parameters as well as for differences or ratios of treatment effects. The latter is particularly useful in a pharmacokinetic context to establish bioequivalence using area under the concentration versus time curves (AUCs). In this work, we will illustrate how Fieller‐type confidence intervals can be constructed for the ratio of AUCs estimated using a noncompartmental approach in a sparse sampling setting from a two‐treatment, two‐period, two‐sequence crossover trial. In particular, we will discuss a flexible batch design, which includes traditional serial sampling and complete data designs as special cases. Via simulation, we show that the proposed intervals have nominal coverage and keep the type I error even for small sample sizes. Moreover, we illustrate the methodology in a real data example. Copyright © 2013 John Wiley & Sons, Ltd.