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Activity Number: 187 - Surrogate Markers and the Role of Mediation Analysis in Drug Development
Type: Invited
Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
Sponsor: Biometrics Section
Abstract #316835
Title: Predicting Outcomes of Phase III Oncology Trials with Bayesian Mediation Modeling
Author(s): Jie Zhou and Xun Jiang and Amy Xia and Peng Wei and Brian Hobbs*
Companies: Cleveland Clinic Foundation and Amgen and Amgen and The University of Texas MD Anderson Cancer Center and The University of Texas at Austin
Keywords: Bayesian prediction; oncology; mediation modeling; RCTs; trial simulation

Pivotal cancer trials often fail to yield evidence in support of new therapies thought to offer promising alternatives to standards-of-care. Conducting RCTs in oncology tends to be 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. Additionally the models can be used to inform re-estimations of sample size for under-powered trials that demonstrate survival benefit via tumor response mediation. The methods are applied to predict the outcomes of three colorectal cancer RCTs.

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

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