AI 中文总结
本研究提出用等渗和单调贝叶斯加性回归树建模连续毒性以学习最大耐受剂量，结合过量控制升级指导剂量选择，模拟显示其优于参数方法，并给出理论下界与模拟器。
AI 中文摘要
I期癌症试验旨在寻找最大耐受剂量（MTD），同时保护患者免受过度毒性。因此，剂量分配必须在患者安全与随着数据积累学习剂量-毒性关系之间取得平衡。我们使用两种形式的贝叶斯加性回归树（BART）对连续测量的毒性结果进行建模：等渗BART将后验响应曲线投影到非递减函数上，而单调BART则对模型施加约束。联合曲线和方差抽样产生MTD后验分布，通过过量控制升级（EWOC）指导剂量选择。我们在模拟中将这两种方法与三种参数化程序在七条剂量-毒性曲线上进行比较。我们评估剂量限制毒性（DLT）计数、高于MTD的分配、符号化末次剂量误差和相对绝对误差。树程序在四条非线性曲线上达到了最低的平均RAE，并在四条曲线上共同最小化了平均DLT计数和高于MTD的分配。贝叶斯强化学习视角将这些顺序决策表述为有限时域规划问题。剂量限制给出了末次剂量误差的下界；在精确后验预测评估下，基于EWOC的滚动策略的期望加权损失不大于其基线。辅助材料中附带了Dose Trial Lab，一个用于所有五种程序的桌面模拟器。
英文摘要
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 Bayesian additive regression trees (BART): isotonic BART projects posterior response curves onto nondecreasing functions, whereas monotone BART constrains the model. Joint curve and variance draws induce an MTD posterior that guides dose selection through escalation with overdose control (EWOC). We compare these methods with three parametric procedures across seven dose--toxicity curves in simulation. We assess dose-limiting toxicity (DLT) counts, above-MTD assignments, signed last-dose error, and relative absolute error. The tree procedures attained the lowest mean RAE on four nonlinear curves and jointly minimized mean DLT counts and above-MTD assignments on four curves. A Bayesian reinforcement learning perspective formulates these sequential decisions as a finite-horizon planning problem. Dose restrictions yield a lower bound on last-dose error; under exact posterior-predictive evaluation, an EWOC-based rollout policy has no greater expected weighted loss than its baseline. Dose Trial Lab, a desktop simulator for all five procedures, accompanies the supplementary material.
