Bayesian Phase 1–2 Designs with Adaptive Rules for Staggering Patient Entry
具有自适应患者错峰入组规则的贝叶斯Ⅰ—Ⅱ期试验设计AI 解读 ↓
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具有自适应患者错峰入组规则的贝叶斯Ⅰ—Ⅱ期试验设计
- 研究问题
- 首次人体剂量探索试验中，如何在剂量限制性毒性、疾病进展及治疗反应之间权衡，减少队列内错峰治疗造成的延迟？
- 方法与思路
- 作者提出Adaptive Stagger，结合低等级毒性与严重毒性的关系、疾病进展风险，以预期效用和安全要求决定是否延迟治疗。
- 材料陈述的贡献
- 摘要称，相较严格错峰规则，模拟中该设计略改善安全性、提高最优剂量选择率并缩短试验时间，另给出细胞治疗试验示例。
- 阅读关注点
- 应核对效用函数、安全门槛、毒性与疾病进展模型，以及模拟情景是否充分覆盖患者延迟治疗的代价。
- 阅读边界
- 仅据OpenAlex索引摘要，稿件版本未注明；摘要未给出改善幅度或安全门槛，模拟结论不能直接视为实际临床效果。
原文摘要
For first-in-human dose-finding trials, to protect patient safety, regulatory agencies may enforce strict within-cohort staggering rules that require delaying treatment of each patient in the first cohort at an untried dose until dose-limiting toxicities (DLTs) of all previously treated patients have been evaluated. Consequently, many new patients may face therapy delays, which reduces their probability of achieving a response due to disease progression, or be treated off-protocol, which may significantly extend trial duration. To address this, we propose a Bayesian phase 1-2 design, Adaptive Stagger, that reduces delays while protecting patients by making adaptive within-cohort staggering decisions. Adaptive Stagger exploits the relationship between the number of low-grade toxicities and DLT, and accounts for the risk of disease progression. A utility function is used to quantify the tradeoff between DLT and response, with a patient’s treatment delayed only if it has greater expected utility than immediate treatment at the current recommended dose, or the current dose fails a safety requirement. Otherwise, the patient is treated without delay at the current dose. Simulations show that, compared to a design with strict within-cohort staggering rules, Adaptive Stagger improves safety slightly, increases the optimal dose selection rate, and substantially shortens trial duration. The design is illustrated by a trial of CD70 natural killer cells for treating hematologic malignancies.