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Activity Number: 385 - SPEED: Statistical Methods and Applications in Medical Research, Risk Analysis, and Marketing Part 1
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract #323154
Title: Improving Dose-Escalation Design with Historical and Concurrent Trial Data
Author(s): Abhishek Kumar Dubey* and Arun Kumar Kumar and Kaushal Kumar Mishra
Companies: Bristol Myer Squibb and Bristol Myer Squibb and Bristol Myer Squibb
Keywords: Multiple drug regimen; Clinical trial; Bayesian optimal design

Dose escalation in multiple drug regimens in clinical trials can be efficient if data is borrowed across the regimens. Food and Drug Administration encourages efficient dose escalation through data borrowing in their recent guidelines for protocol submission to reduce the risk of patients being treated at a suboptimal- or over-toxic dose. Data borrowing reduces the trial cost and hence is attractive to trial sponsors as well. In this work, we explore the risks and advantages of data borrowing in dose escalation of multiple drug regimens for the popular Bayesian Optimal Interval design. Operating characteristics of different borrowing methodologies are explored through simulations for scenarios inspired by the real-world problems encountered in the pharmaceutical industry. Based on the simulation results, we provide recommendations for how data borrowing should be done in dose escalation of multiple regimens.

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

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