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Reference Datasets for 2-Treatment, 2-Sequence, 2-Period Bioequivalence Studies

Overview of attention for article published in The AAPS Journal, September 2014
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Title
Reference Datasets for 2-Treatment, 2-Sequence, 2-Period Bioequivalence Studies
Published in
The AAPS Journal, September 2014
DOI 10.1208/s12248-014-9661-0
Pubmed ID
Authors

Helmut Schütz, Detlew Labes, Anders Fuglsang

Abstract

It is difficult to validate statistical software used to assess bioequivalence since very few datasets with known results are in the public domain, and the few that are published are of moderate size and balanced. The purpose of this paper is therefore to introduce reference datasets of varying complexity in terms of dataset size and characteristics (balance, range, outlier presence, residual error distribution) for 2-treatment, 2-period, 2-sequence bioequivalence studies and to report their point estimates and 90% confidence intervals which companies can use to validate their installations. The results for these datasets were calculated using the commercial packages EquivTest, Kinetica, SAS and WinNonlin, and the non-commercial package R. The results of three of these packages mostly agree, but imbalance between sequences seems to provoke questionable results with one package, which illustrates well the need for proper software validation.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Austria 1 5%
Switzerland 1 5%
Unknown 17 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 21%
Student > Ph. D. Student 4 21%
Student > Master 3 16%
Lecturer 2 11%
Other 1 5%
Other 2 11%
Unknown 3 16%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 5 26%
Medicine and Dentistry 3 16%
Mathematics 2 11%
Agricultural and Biological Sciences 2 11%
Arts and Humanities 1 5%
Other 2 11%
Unknown 4 21%