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Learning the Maximum Tolerated Dose for Continuous Toxicity via Monotone Bayesian Trees
Phase I cancer trials seek the maximum tolerated dose (MTD) while protecting patients from excessive toxicity. Dose assignments must therefore balance patient safety with learning the dose--toxicity relationship as data accrue. We model continuously measured toxicity outcomes using two forms of Baye
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