标题： 风险包容的早期临床试验情境强化学习 显示英文标题

标题： Risk-inclusive Contextual Bandits for Early Phase Clinical Trials

摘要： 早期临床试验面临选择平衡安全性和有效性的最佳药物剂量的挑战，这是由于剂量-反应关系的不确定性以及参与者的特征差异。 传统的随机剂量分配方法往往因未考虑个体协变量而使参与者暴露于次优剂量，这需要更大的样本量并延长药物开发时间。 本文介绍了一种包含风险的上下文老虎机算法，该算法利用多臂老虎机（MAB）策略通过整合参与者特定数据来优化给药。 通过结合两个独立的汤普森采样器，一个用于有效性，一个用于安全性，该算法增强了剂量分配中有效性和安全性的平衡。 效应大小通过渐近置信序列的广义版本进行估计（AsympCS，Waudby-Smith 等，2024），为随时间推移的序列因果推断提供统一的覆盖保证。 AsympCS 的有效性也在可能模型错误指定的 MAB 设置中得到确立。 实证结果表明，与随机分配和仅关注有效性的传统上下文老虎机相比，该方法在优化剂量分配方面表现出优势。 此外，对来自最近 IIb 期研究的真实数据的应用与实际发现一致。 显示英文摘要

摘要： Early-phase clinical trials face the challenge of selecting optimal drug doses that balance safety and efficacy due to uncertain dose-response relationships and varied participant characteristics. Traditional randomized dose allocation often exposes participants to sub-optimal doses by not considering individual covariates, necessitating larger sample sizes and prolonging drug development. This paper introduces a risk-inclusive contextual bandit algorithm that utilizes multi-arm bandit (MAB) strategies to optimize dosing through participant-specific data integration. By combining two separate Thompson samplers, one for efficacy and one for safety, the algorithm enhances the balance between efficacy and safety in dose allocation. The effect sizes are estimated with a generalized version of asymptotic confidence sequences (AsympCS, Waudby-Smith et al., 2024), offering a uniform coverage guarantee for sequential causal inference over time. The validity of AsympCS is also established in the MAB setup with a possibly mis-specified model. The empirical results demonstrate the strengths of this method in optimizing dose allocation compared to randomized allocations and traditional contextual bandits focused solely on efficacy. Moreover, an application on real data generated from a recent Phase IIb study aligns with actual findings.