title: Unified approach for extrapolation and bridging of adult information in early-phase dose-finding paediatric studies.
abstract: The number of trials conducted and the number of patients per trial are typically small in paediatric clinical studies. This is due to ethical constraints and the complexity of the medical process for treating children. While incorporating prior knowledge from adults may be extremely valuable, this must be done carefully. In this paper, we propose a unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults. The dose-range is calculated under three extrapolation options, linear, allometry and maturation adjustment, using adult pharmacokinetic data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose-toxicity and dose-efficacy relationships are obtained using early-phase adult toxicity and efficacy data at several dose levels. Priors are integrated into the dose-finding process through Bayesian model selection or adaptive priors. This calibrates the model to adjust for misspecification, if the adult and pediatric data are very different. We performed a simulation study which indicates that incorporating prior adult information in this way may improve dose selection in children.
authors.affiliation: 1 INSERM, UMRS 1138, CRC, Team 22, University of Paris 5, University of Paris 6, Paris, France.
authors.forename: Caroline
authors.initials: C
authors.lastname: Petit
authors.affiliation: 2 LJK, UMR CNRS 5224, University of Grenoble Alpes, Grenoble, France.
authors.forename: Adeline
authors.initials: A
authors.lastname: Samson
authors.affiliation: 3 Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan.
authors.forename: Satoshi
authors.initials: S
authors.lastname: Morita
authors.affiliation: 1 INSERM, UMRS 1138, CRC, Team 22, University of Paris 5, University of Paris 6, Paris, France.
authors.forename: Moreno
authors.initials: M
authors.lastname: Ursino
authors.affiliation: 4 INSERM, IAME, UMR 1137, Université Paris Diderot, Sorbonne Paris Cité, Paris, France.
authors.forename: Jérémie
authors.initials: J
authors.lastname: Guedj
authors.affiliation: 5 Pharmacology Department, Hôpital Européen Georges Pompidou, Paris Descartes University, INSERM U1129, Paris, France.
authors.forename: Vincent
authors.initials: V
authors.lastname: Jullien
authors.affiliation: 4 INSERM, IAME, UMR 1137, Université Paris Diderot, Sorbonne Paris Cité, Paris, France.|6 INSERM CIC 1414, Université de Rennes 1, Rennes.
authors.forename: Emmanuelle
authors.initials: E
authors.lastname: Comets
authors.affiliation: 1 INSERM, UMRS 1138, CRC, Team 22, University of Paris 5, University of Paris 6, Paris, France.
authors.forename: Sarah
authors.initials: S
authors.lastname: Zohar
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D054796:Drug Dosage Calculations; D006801:Humans; D015233:Models, Statistical; D019370:Observation; D010372:Pediatrics; D011336:Probability
keywords: Bayesian inference; Dose-finding; adults observations; bridging; extrapolation; paediatrics trials