Jump to ContentJump to Main Navigation
Show Summary Details
More options …

The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.

2 Issues per year


IMPACT FACTOR 2017: 0.840
5-year IMPACT FACTOR: 1.000

CiteScore 2017: 0.97

SCImago Journal Rank (SJR) 2017: 1.150
Source Normalized Impact per Paper (SNIP) 2017: 1.022

Mathematical Citation Quotient (MCQ) 2016: 0.09

Online
ISSN
1557-4679
See all formats and pricing
More options …

Power for Testing Multiple Instances of the Two One-Sided Tests Procedure

Kem F Phillips
Published Online: 2009-05-07 | DOI: https://doi.org/10.2202/1557-4679.1169

The two one-sided tests procedure is used to test the equivalence of two measurements taken under different conditions. For example, two formulations of a drug are said to be bioequivalent if the average blood levels of the drug over time (AUC) are similar for the two formulations. In some studies there may be more than one parameter to test, such as a drug's AUC and maximum concentration, Cmax, or AUCs from a parent drug and a metabolite. The power of testing two or more equivalence hypotheses simultaneously is less than the power to test any one hypothesis separately, and depends on the correlations of the measurements. This paper develops an exact mathematical formula for the power for two or more simultaneous comparisons for normally distributed variables when several comparisons are evaluated separately. The formula requires numerical integration with respect to the variance-covariance terms. These terms are distributed according to the Wishart distribution, and are integrated over a subset of positive-definite matrices defined by the equivalence criteria. An R program for the case of two comparisons is included.

Keywords: power; TOST; simultaneous testing

About the article

Published Online: 2009-05-07


Citation Information: The International Journal of Biostatistics, Volume 5, Issue 1, ISSN (Online) 1557-4679, DOI: https://doi.org/10.2202/1557-4679.1169.

Export Citation

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[2]
Yang Cao, Daniel Obeng, Guodong Hui, Luting Xue, Yukun Ren, Xianjie Yu, Fei Wang, and Chad Atwell
Biotechnology Progress, 2017
[3]
Philip Pallmann and Thomas Jaki
Statistics in Medicine, 2017

Comments (0)

Please log in or register to comment.
Log in