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Schütz H, Burger DA, Cobo E, Dubins DD, Farkás T, Labes D, Lang B, Ocaña J, Ring A, Shitova A, Stus V, Tomashevskiy M. Group-by-Treatment Interaction Effects in Comparative Bioavailability Studies. AAPS J 2024; 26:50. [PMID: 38632178 DOI: 10.1208/s12248-024-00921-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/03/2024] [Indexed: 04/19/2024] Open
Abstract
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.
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Affiliation(s)
- Helmut Schütz
- Center for Medical Data Science of the Medical University of Vienna, 1090, Vienna, Austria.
- Faculty of Pharmacy, Universidade de Lisboa, 1649-004, Lisbon, Portugal.
- BEBAC, Neubaugasse 36/11, 1070, Vienna, Austria.
| | - Divan A Burger
- University of Pretoria, Pretoria, South Africa
- Syneos Health, Bloemfontein, South Africa
| | - Erik Cobo
- Department of Statistics and Operations Research, Universitat Politecnica de Catalunya, Barcelona, Catalunya, Spain
| | - David D Dubins
- Leslie Dan Faculty of Pharmacy, Toronto, Ontario, Canada
| | | | | | - Benjamin Lang
- Boehringer Ingelheim Pharma GmbH & Co. KG, Ingelheim am Rhein, Germany
| | - Jordi Ocaña
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona, Catalunya, Spain
| | - Arne Ring
- University of the Free State, Bloemfontein, South Africa
- Hexal - a Sandoz Brand, Holzkirchen, Germany
| | | | - Volodymyr Stus
- Zakłady Farmaceutyczne Polpharma S.A., Starogard Gdanski, Poland
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Chen R, Schumitzky A, Kryshchenko A, Nieforth K, Tomashevskiy M, Hu S, Garreau R, Otalvaro J, Yamada W, Neely MN. RPEM: Randomized Monte Carlo parametric expectation maximization algorithm. CPT Pharmacometrics Syst Pharmacol 2024. [PMID: 38622792 DOI: 10.1002/psp4.13113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 12/22/2023] [Accepted: 01/26/2024] [Indexed: 04/17/2024] Open
Abstract
Inspired from quantum Monte Carlo, by sampling discrete and continuous variables at the same time using the Metropolis-Hastings algorithm, we present a novel, fast, and accurate high performance Monte Carlo Parametric Expectation Maximization (MCPEM) algorithm. We named it Randomized Parametric Expectation Maximization (RPEM). We compared RPEM with NONMEM's Importance Sampling Method (IMP), Monolix's Stochastic Approximation Expectation Maximization (SAEM), and Certara's Quasi-Random Parametric Expectation Maximization (QRPEM) for a realistic two-compartment voriconazole model with ordinary differential equations using simulated data. We show that RPEM is as fast and as accurate as the algorithms IMP, QRPEM, and SAEM for the voriconazole model in reconstructing the population parameters, for the normal and log-normal cases.
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Affiliation(s)
- Rong Chen
- Certara, Inc., Princeton, New Jersey, USA
- Laboratory of Applied Pharmacokinetics and Bioinformatics, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Alan Schumitzky
- Laboratory of Applied Pharmacokinetics and Bioinformatics, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Department of Mathematics, University of Southern California, Los Angeles, California, USA
| | - Alona Kryshchenko
- Department of Mathematics, California State University Channel Islands, Camarillo, California, USA
| | | | | | - Shuhua Hu
- Certara, Inc., Princeton, New Jersey, USA
| | - Romain Garreau
- UMR CNRS 5558, Laboratoire de Biométrie et Biologie Evolutive, Université de Lyon, Université Lyon 1, Villeurbanne, France
- Hospices Civils de Lyon, GH Nord, Service de Pharmacie, Lyon, France
| | - Julian Otalvaro
- Laboratory of Applied Pharmacokinetics and Bioinformatics, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Walter Yamada
- Laboratory of Applied Pharmacokinetics and Bioinformatics, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Michael N Neely
- Laboratory of Applied Pharmacokinetics and Bioinformatics, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Pediatric Infectious Diseases, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
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Schütz H, Labes D, Tomashevskiy M, la Parra MGD, Shitova A, Fuglsang A. Reference Datasets for Studies in a Replicate Design Intended for Average Bioequivalence with Expanding Limits. AAPS J 2020; 22:44. [PMID: 32034551 DOI: 10.1208/s12248-020-0427-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 01/26/2020] [Indexed: 11/30/2022]
Abstract
In order to help companies qualify and validate the software used to evaluate bioequivalence trials in a replicate design intended for average bioequivalence with expanding limits, this work aims to define datasets with known results. This paper releases 30 reference datasets into the public domain along with proposed consensus results. A proposal is made for results that should be used as validation targets. The datasets were evaluated by seven different software packages according to methods proposed by the European Medicines Agency. For the estimation of CVwR and Method A, all software packages produced results that are in agreement across all datasets. Due to different approximations of the degrees of freedom, slight differences were observed in two software packages for Method B in highly incomplete datasets. All software packages were suitable for the estimation of CVwR and Method A. For Method B, different methods for approximating the denominator degrees of freedom could lead to slight differences, which eventually could lead to contrary decisions in very rare borderline cases.
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Affiliation(s)
| | | | - Michael Tomashevskiy
- OnTarget Group, Ul, Pechatnika Grigoryeva, 8, Saint Petersburg, Russian Federation, 191119
| | | | - Anastasia Shitova
- Quinta-Analytica Yaroslavl, Leningradsky pr. 52G, Yaroslavl, Russian Federation, 150045
| | - Anders Fuglsang
- Fuglsang Pharma, Hiort Lorenzens Vej 6c st.tv., 6100, Haderslev, Denmark
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