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Activity Number: 115 - Novel Statistical Methods for Emerging Problems in Modern Clinical Trials and Drug Development
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract #306618 Presentation
Title: Bayesian Models for Precision Oncology Clinical Trials
Author(s): Peter Müller* and Yanxun Xu and Don Berry and Apostolia Tsimberidou
Companies: University of Texas Austin and Johns Hopkins University and MDACC and MDACC
Keywords: Basket Trials;; adaptive design; targeted therapy; bayesian

Targeted therapies on the basis of genomic aberrations analysis of the tumor have shown promising results in cancer prognosis and treatment. Regardless of tumor type, trials that match patients to targeted therapies for their particular genomic aberrations have become a mainstream direction of therapeutic management of patients with cancer. Therefore, finding the subpopulation of patients who can most benefit from an aberration-specific targeted therapy across multiple cancer types is important. We propose an adaptive Bayesian clinical trial design for patient allocation and subpopulation identification. We start with a decision theoretic approach, including a utility function and a probability model across all possible subpopulation models. The main features of the proposed design and population finding methods are the use of a flexible non-parametric Bayesian survival regression based on a random covariate-dependent partition of patients, and decisions based on a flexible utility function that reflects the requirement of the clinicians appropriately and realistically, and the adaptive allocation of patients to their superior treatments. Biometrical J

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

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