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Activity Number: 355 - Contributed Poster Presentations: Biopharmaceutical Section
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #304388
Title: Using BLRM to Find MTDs for Loading Dose and Maintenance Dose in Oncology Trials
Author(s): Kejian Liu* and Yinge Sun
Companies: Sanofi and University of Virginia
Keywords: Bayesian; MTD; oncology; phase 1
Abstract:

Bayesian logistic regression model (BLRM) is being used more commonly in the oncology phase 1 trials to find MTD. Over the last several years, immunotherapy has emerged as a very promising new frontier to fight cancers. Due to the concern of acute reactions such as Cytokine Release Syndrome, immunotherapy may be administered first with a lower loading dose followed by a higher maintenance dose. Two stage design is proposed to find MTD1 for the loading dose in the first stage and MTD2 for the maintenance dose in the second stage. To extend BLRM to find dual MTDs in 2 stage design, we first model the probability of DLT during the loading dose treatment period as a function of loading dose. We then model the conditional probability of DLT during the maintenance dose treatment period given no DLT during the loading dose treatment period as a function of both loading dose and maintenance dose. These 2 models are combined to obtain the marginal probability of DLT which is used to guide dose escalation and identification of MTDs in both stages of the trial. Simulation is used to evaluate the performance of the proposed approach and compare with 3+3 design.


Authors who are presenting talks have a * after their name.

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