Rights statement: This is the peer reviewed version of the following article: Pallmann P, Jaki T. Simultaneous confidence regions for multivariate bioequivalence. Statistics in Medicine. 2017;36:4585–4603. https://doi.org/10.1002/sim.7446 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sim.7446/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Simultaneous confidence regions for multivariate bioequivalence
AU - Pallmann, Philip Steffen
AU - Jaki, Thomas Friedrich
N1 - This is the peer reviewed version of the following article: Pallmann P, Jaki T. Simultaneous confidence regions for multivariate bioequivalence. Statistics in Medicine. 2017;36:4585–4603. https://doi.org/10.1002/sim.7446 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sim.7446/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2017/12/20
Y1 - 2017/12/20
N2 - Demonstrating bioequivalence of several pharmacokinetic (PK) parameters, such as AUC and Cmax, that are calculated from the same biological sample measurements is in fact a multivariate problem, even though this is neglected by most practitioners and regulatory bodies, who typically settle for separate univariate analyses. We believe, however, that a truly multivariate evaluation of all PK measures simultaneously is clearly more adequate. In this paper, we review methods to construct joint confidence regions around multivariate normal means and investigate their usefulness in simultaneous bioequivalence problems via simulation. Some of them work well for idealised scenarios but break down when faced with real-data challenges such as unknown variance and correlation among the PK parameters. We study the shapes of the confidence regions resulting from different methods, discuss how marginal simultaneous confidence intervals for the individual PK measures can be derived, and illustrate the application to data from a trial on ticlopidine hydrochloride. An R package is available.
AB - Demonstrating bioequivalence of several pharmacokinetic (PK) parameters, such as AUC and Cmax, that are calculated from the same biological sample measurements is in fact a multivariate problem, even though this is neglected by most practitioners and regulatory bodies, who typically settle for separate univariate analyses. We believe, however, that a truly multivariate evaluation of all PK measures simultaneously is clearly more adequate. In this paper, we review methods to construct joint confidence regions around multivariate normal means and investigate their usefulness in simultaneous bioequivalence problems via simulation. Some of them work well for idealised scenarios but break down when faced with real-data challenges such as unknown variance and correlation among the PK parameters. We study the shapes of the confidence regions resulting from different methods, discuss how marginal simultaneous confidence intervals for the individual PK measures can be derived, and illustrate the application to data from a trial on ticlopidine hydrochloride. An R package is available.
KW - bioavailability
KW - James-Stein estimator
KW - limaçon of Pascal
KW - simultaneous inference
KW - TOST
U2 - 10.1002/sim.7446
DO - 10.1002/sim.7446
M3 - Journal article
VL - 36
SP - 4585
EP - 4603
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 29
ER -