A Bayesian time-to-event pharmacokinetic model for phase I dose-escalation trials with multiple schedules.
Stan
multiple schedules
pharmacokinetic models
phase I dose-escalation trials
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
30 11 2020
30 11 2020
Historique:
received:
23
11
2018
revised:
21
04
2020
accepted:
30
06
2020
pubmed:
17
8
2020
medline:
22
6
2021
entrez:
16
8
2020
Statut:
ppublish
Résumé
Phase I dose-escalation trials must be guided by a safety model in order to avoid exposing patients to unacceptably high risk of toxicities. Traditionally, these trials are based on one type of schedule. In more recent practice, however, there is often a need to consider more than one schedule, which means that in addition to the dose itself, the schedule needs to be varied in the trial. Hence, the aim is finding an acceptable dose-schedule combination. However, most established methods for dose-escalation trials are designed to escalate the dose only and ad hoc choices must be made to adapt these to the more complicated setting of finding an acceptable dose-schedule combination. In this article, we introduce a Bayesian time-to-event model which takes explicitly the dose amount and schedule into account through the use of pharmacokinetic principles. The model uses a time-varying exposure measure to account for the risk of a dose-limiting toxicity over time. The dose-schedule decisions are informed by an escalation with overdose control criterion. The model is formulated using interpretable parameters which facilitates the specification of priors. In a simulation study, we compared the proposed method with an existing method. The simulation study demonstrates that the proposed method yields similar or better results compared with an existing method in terms of recommending acceptable dose-schedule combinations, yet reduces the number of patients enrolled in most of scenarios. The R and Stan code to implement the proposed method is publicly available from Github ( https://github.com/gunhanb/TITEPK_code).
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3986-4000Informations de copyright
© 2020 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
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