Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
International Conference on Machine Learning
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Villar Moreschi, S. Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints. International Conference on Machine Learning. https://doi.org/10.17863/CAM.54535
Phase I dose-finding trials are increasingly chal- lenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex. Despite this, most commonly used methods in practice fo- cus on identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity events. We present a novel adaptive clinical trial methodol- ogy, called Safe Efficacy Exploration Dose Al- location (SEEDA), that aims at maximizing the cumulative efficacies while satisfying the toxicity safety constraint with high probability. We evalu- ate performance objectives that have operational meanings in practical clinical trials, including cumulative efficacy, recommendation/allocation success probabilities, toxicity violation probabil- ity, and sample efficiency. An extended SEEDA- Plateau algorithm that is tailored for the increase- then-plateau efficacy behavior of molecularly tar- geted agents (MTA) is also presented. Through numerical experiments using both synthetic and real-world datasets, we show that SEEDA out- performs state-of-the-art clinical trial designs by finding the optimal dose with higher success rate and fewer patients.
SSV thanks the funding received from the National Institute for Health Research Cambridge Biomedical Research Cen- tre at the Cambridge University Hospitals NHS Foundation Trust and the UK Medical Research Council (grant number: MC_UU_00002/3).
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This record's DOI: https://doi.org/10.17863/CAM.54535
This record's URL: https://www.repository.cam.ac.uk/handle/1810/307440
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