Background: Phase I dose-finding trials increasingly encounter delayed-onset toxicities, especially with immunotherapies and targeted agents. The time-to-event continual reassessment method (TITE-CRM) handles incomplete follow-up using fixed linear weights, but this ad hoc approach doesn't reflect actual delay patterns and may expose patients to excessive risk during dose escalation. Methods: We replace TITE-CRM's fixed weights with adaptive weights, posterior predictive probabilities derived from the evolving toxicity delay distribution. Under a Weibull timing model, we get closed-form weight updates through maximum likelihood estimation, making real-time implementation straightforward. We tested our method (AW-TITE) against TITE-CRM and standard designs (3+3, mTPI, BOIN) across three dose-toxicity scenarios through simulation (N = 30 patients, 2,000 replications). We also examined robustness across varying accrual rates, sample sizes, shape parameters, observation windows, and priors. Results: Our AW-TITE reduced patient overdosing by 40.6% compared to TITE-CRM (mean fraction above MTD: 0.202 vs 0.340; 95% CI: -0.210 to -0.067, p < 0.001) while maintaining comparable MTD selection accuracy (mean difference: +0.023, p = 0.21). Against algorithm-based methods, AW-TITE achieved higher MTD identification: +32.6% vs mTPI, +19.8% vs 3+3, and +5.6% vs BOIN. Performance remained robust across all sensitivity analyses. Conclusions: Adaptive weighting offers a practical way to improve Phase I trial safety while preserving MTD selection accuracy. The method requires minimal computation and is ready for real-time use.

翻译：背景：Ⅰ期剂量探索试验日益面临延迟性毒性的挑战，尤其在免疫疗法和靶向药物中。时间-事件连续重评估方法（TITE-CRM）采用固定线性权重处理不完整随访数据，但这种临时性方法无法反映真实的延迟模式，可能在剂量递增阶段使患者暴露于过度风险。方法：我们将TITE-CRM的固定权重替换为自适应权重——即从动态演化的毒性延迟分布中推导出的后验预测概率。基于威布尔时序模型，通过最大似然估计获得闭式权重更新，实现实时计算的便捷性。通过模拟研究（N=30例患者，2000次重复），我们在三种剂量-毒性场景下将本方法（AW-TITE）与TITE-CRM及标准设计（3+3、mTPI、BOIN）进行比较。同时考察了不同入组速率、样本量、形状参数、观察窗口和先验分布的稳健性。结果：相较于TITE-CRM，AW-TITE使患者过量给药减少40.6%（超过最大耐受剂量的平均比例：0.202 vs 0.340；95% CI：-0.210至-0.067，p<0.001），同时保持相当的MTD选择准确度（平均差异：+0.023，p=0.21）。与算法类方法相比，AW-TITE获得更高的MTD识别率：较mTPI提升32.6%，较3+3提升19.8%，较BOIN提升5.6%。所有敏感性分析中性能均保持稳健。结论：自适应加权为提高Ⅰ期试验安全性提供了实用途径，同时保持了MTD选择的准确性。该方法计算需求极低，可直接投入实时应用。