Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens - Sorbonne Université
Article Dans Une Revue Statistics in Medicine Année : 2021

Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens

Résumé

Bayesian joint modeling, bivariate toxicity, cumulative probability of toxicity, dose regimen, early phase oncology, pharmacokinetics/pharmacodynamics 1 INTRODUCTION Most phase I dose-finding trials in oncology aim to determine the maximum tolerated dose (MTD), which is defined as the highest dose that does not exceed a predefined probability of dose-limiting toxicity (DLT), in a prespecified observational window. The DLT is a binary outcome defined to summarize the patient's toxicity profile and is usually derived from Moreno Ursino and Marie-Karelle Riviere contributed equally to this study. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Cancer
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Dates et versions

hal-03288022 , version 1 (16-07-2021)

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Emma Gerard, Sarah Zohar, Christelle Lorenzato, Moreno Ursino, Marie‐karelle Riviere. Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens. Statistics in Medicine, 2021, ⟨10.1002/sim.9113⟩. ⟨hal-03288022⟩

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