Group-by-Treatment Interaction Effects in Comparative Bioavailability Studies

Comparative bioavailability studies often involve multiple groups of subjects for a variety of reasons, such as clinical capacity limitations. This raises questions about the validity of pooling data from these groups in the statistical analysis and whether a group-by-treatment interaction should be...

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Bibliographic Details
Published inThe AAPS journal Vol. 26; no. 3; p. 50
Main Authors Schütz, Helmut, Burger, Divan A., Cobo, Erik, Dubins, David D., Farkás, Tibor, Labes, Detlew, Lang, Benjamin, Ocaña, Jordi, Ring, Arne, Shitova, Anastasia, Stus, Volodymyr, Tomashevskiy, Michael
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 17.04.2024
Springer
Subjects
Biochemistry
Biological Availability
Biomedical and Life Sciences
Biomedicine
Biotechnology
Comparative analysis
Cross-Over Studies
Humans
Medical research
Medicine, Experimental
Pharmacology/Toxicology
Pharmacy
Research Article
Research Design
Simulation methods
Monte-Carlo simulations
group-by-treatment interaction
regulatory guidelines
average bioequivalence
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Summary:Comparative bioavailability studies often involve multiple groups of subjects for a variety of reasons, such as clinical capacity limitations. This raises questions about the validity of pooling data from these groups in the statistical analysis and whether a group-by-treatment interaction should be evaluated. We investigated the presence or absence of group-by-treatment interactions through both simulation techniques and a meta-study of well-controlled trials. Our findings reveal that the test falsely detects an interaction when no true group-by-treatment interaction exists. Conversely, when a true group-by-treatment interaction does exist, it often goes undetected. In our meta-study, the detected group-by-treatment interactions were observed at approximately the level of the test and, thus, can be considered false positives. Testing for a group-by-treatment interaction is both misleading and uninformative. It often falsely identifies an interaction when none exists and fails to detect a real one. This occurs because the test is performed between subjects in crossover designs, and studies are powered to compare treatments within subjects. This work demonstrates a lack of utility for including a group-by-treatment interaction in the model when assessing single-site comparative bioavailability studies, and the clinical trial study structure is divided into groups. Graphical Abstract
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ISSN:1550-7416
1550-7416
DOI:10.1208/s12248-024-00921-x