Use of pharmacodynamic modeling for Bayesian information borrowing in pediatric clinical trials.

Autor: Sebastien B; Data and Data Science, Sanofi, R&D, Chilly-Mazarin, France., Cheung SYA; Integrated Drug Development, Certara USA Inc, Princeton, NJ, USA., Corriol-Rohou S; Regulatory Affairs and Policy, AstraZeneca, Global Regulatory Policy, R&D, Paris, France., Gamalo-Siebers M; Global Biometrics & Data Management, Pfizer, Innovative Health, Collegeville, PA, USA., Jreich R; Data and Data Science, Sanofi, R&D, Chilly-Mazarin, France., Krishna R; Clinical Pharmacology, Certara USA Inc, Princeton, NJ, USA., Liu J; Clinical Pharmacology, Pfizer Inc, Groton, CT, USA.
Jazyk: angličtina
Zdroj: Journal of biopharmaceutical statistics [J Biopharm Stat] 2023 Nov 02; Vol. 33 (6), pp. 726-736. Date of Electronic Publication: 2022 Dec 16.
DOI: 10.1080/10543406.2022.2149772
Abstrakt: The use of Bayesian methodology to design and analyze pediatric efficacy trials is one of the possible options to reduce their sample size. This reduction of the sample size results from the use of an informative prior for the parameters of interest. In most of the applications, the principle of 'information borrowing' from adults' trials is applied, which means that the informative prior is constructed using efficacy results in adult of the drug under investigation. This implicitly assumes similarity in efficacy between the selected pediatric dose and the efficacious dose in adults. The goal of this article is to propose a method to construct prior distribution for the parameter of interest, not directly constructed from the efficacy results of the efficacious dose in adult patients but using pharmacodynamic modeling of a bridging biomarker using early phase pediatric data. When combined with a model bridging the biomarker with the clinical endpoints, the prior is constructed using a variational method after simulation of the parameters of interest. A use case application illustrates how the method can be used to construct a realistic informative prior.
Databáze: MEDLINE
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