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Activity Number: 315 - SPEED: Biopharmaceutical Applications: Trials, Biomarkers, and Enpoint Validation
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 9:25 AM to 10:10 AM
Sponsor: Biopharmaceutical Section
Abstract #332585
Title: Phase I Designs That Allow for Uncertainty in the Attribution of Adverse Events
Author(s): Alexia Iasonos* and John O'Quigley
Companies: Memorial Sloan Kettering Cancer Center and Universit'e Pierre et Marie Curie,
Keywords: clinical trials; Phase I designs; dose limiting toxicity; continual reassessment method; bayesian methods; sequential monitoring

In determining dose limiting toxicities in Phase I studies, it is necessary to attribute adverse events (AE) to being drug related or not. Such determination is subjective and may introduce bias. In this paper, we develop methods for removing or at least diminishing the impact of this bias on the estimation of the maximum tolerated dose (MTD). The approach that we suggest takes into account possible errors in the attribution of AE. Modeling can be approached in different ways and can include any prior known information we may have on the different types of error. Alternatively, we can work with direct input from the clinical team, based on current and accumulated toxicity data, on the probability of an observed toxicity being drug-related. The uncertainty can be incorporated into a quasi Bernoulli likelihood and readily extended to a Bayesian structure. This allows investigators to explicitly express their degree of uncertainty in whether an AE is drug-related or disease-related. We show that even quite imprecise prior notions on whether or not AEs are drug related can still be accompanied by more accurate inference than would be obtained by ignoring errors.

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

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