| Literature DB >> 26877554 |
Beibei Guo1, Yisheng Li2, Ying Yuan2.
Abstract
Dose-finding methods aiming at identifying an optimal dose of a treatment with a given schedule may be at a risk of misidentifying the best treatment for patients. In this article we propose a phase I/II clinical trial design to find the optimal dose-schedule combination. We define schedule as the method and timing of administration of a given total dose in a treatment cycle. We propose a Bayesian dynamic model for the joint effects of dose and schedule. The proposed model allows us to borrow strength across dose-schedule combinations without making overly restrictive assumptions on the ordering pattern of the schedule effects. We develop a dose-schedule-finding algorithm to sequentially allocate patients to a desirable dose-schedule combination, and select an optimal combination at the end of the trial. We apply the proposed design to a phase I/II clinical trial of a γ-secretase inhibitor in patients with refractory metastatic or locally advanced solid tumours, and examine the operating characteristics of the design through simulations.Entities:
Keywords: Bayesian dynamic model; dose-schedule combination; efficacy; probit model; schedule-response relationship; toxicity
Year: 2016 PMID: 26877554 PMCID: PMC4747255 DOI: 10.1111/rssc.12113
Source DB: PubMed Journal: J R Stat Soc Ser C Appl Stat ISSN: 0035-9254 Impact factor: 1.864