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Activity Number: 541 - Bayesian Design in Clinical Trial and Some Challenging Issues
Type: Topic Contributed
Date/Time: Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #322603
Title: Predicting Outcomes of Phase III Oncology Trials with Bayesian Mediation Modeling of Tumor Response
Author(s): Xun Jiang* and Jie Zhou and Brian P Hobbs and Peng N/A Wei and Amy Xia
Companies: Amgen and Novartis and The University of Texas and The University of Texas MD Anderson Cancer Center and Amgen
Keywords: survival; mediation; trial prediction

Pivotal cancer trials often fail to yield evidence in support of new therapies thought to offer promising alternatives to standards-of-care. Conducting randomized controlled trials in oncology tends to be considerably more expensive than studies of other diseases with comparable sample size. Moreover, phase III trial design often takes place with a paucity of survival data for experimental therapies. Experts have explained the failures on the basis of design flaws which produce studies with unrealistic expectations. This article presents a framework for predicting outcomes of phase III oncology trials using Bayesian mediation models. Predictions, which arise from interim analyses, derive from multivariate modeling of the relationships among treatment, tumor response, and their conjoint effects on survival. Acting as a safeguard against inaccurate pre-trial design assumptions, the methodology may better facilitate rapid closure of negative studies. The methods are applied to predict the outcomes of two colorectal cancer studies. Simulation is used to evaluate and compare models in the absence versus presence of reliable surrogate markers of survival

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

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