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    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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Simultaneous confidence regions for multivariate bioequivalence

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Simultaneous confidence regions for multivariate bioequivalence. / Pallmann, Philip Steffen; Jaki, Thomas Friedrich.

In: Statistics in Medicine, Vol. 36, No. 29, 20.12.2017, p. 4585-4603.

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@article{9139625da63d48dd8f1bbc68364b71c2,
title = "Simultaneous confidence regions for multivariate bioequivalence",
abstract = "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.",
keywords = "bioavailability, James-Stein estimator, lima{\c c}on of Pascal, simultaneous inference, TOST",
author = "Pallmann, {Philip Steffen} and Jaki, {Thomas Friedrich}",
note = "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.",
year = "2017",
month = dec,
day = "20",
doi = "10.1002/sim.7446",
language = "English",
volume = "36",
pages = "4585--4603",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "29",

}

RIS

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 -