DICE: A Bayesian model for early dose finding in phase I trials with multiple treatment courses.
Bayesian model
cumulative toxicity
dose finding design
oncology
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
Dec 2022
Dec 2022
Historique:
revised:
02
09
2021
received:
04
12
2021
accepted:
04
09
2021
pubmed:
4
11
2021
medline:
15
12
2022
entrez:
3
11
2021
Statut:
ppublish
Résumé
Dose-finding clinical trials in oncology aim to determine the maximum tolerated dose (MTD) of a new drug, generally defined by the proportion of patients with short-term dose-limiting toxicities (DLTs). Model-based approaches for such phase I oncology trials have been widely designed and are mostly restricted to the DLTs occurring during the first cycle of treatment, although patients continue to receive treatment for multiple cycles. We aim to estimate the probability of DLTs over sequences of treatment cycles via a Bayesian cumulative modeling approach, where the probability of DLT is modeled taking into account the cumulative effect of the administered drug and the DLT cycle of occurrence. We propose a design, called DICE (Dose-fInding CumulativE), for dose escalation and de-escalation according to previously observed toxicities, which aims at finding the MTD sequence (MTS). We performed an extensive simulation study comparing this approach to the time-to-event continual reassessment method (TITE-CRM) and a benchmark. In general, our approach achieved a better or comparable percentage of correct MTS selection. Moreover, we investigated the DICE prediction ability.
Identifiants
pubmed: 34729815
doi: 10.1002/bimj.202000369
doi:
Substances chimiques
Antineoplastic Agents
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1486-1497Informations de copyright
© 2021 Wiley-VCH GmbH.
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