DataSheet1_Optimizing dose-schedule regimens with bayesian adaptive designs: opportunities and challenges.PDF

Due to the small sample sizes in early-phase clinical trials, the toxicity and efficacy profiles of the dose-schedule regimens determined for subsequent trials may not be well established. The recent development of novel anti-tumor treatments and combination therapies further complicates the problem. Therefore, there is an increasing recognition of the essential place of optimizing dose-schedule regimens, and new strategies are now urgently needed. Bayesian adaptive designs provide a potentially effective way to evaluate several doses and schedules simultaneously in a single clinical trial with higher efficiency, but real-world implementation examples of such adaptive designs are still few. In this paper, we cover the critical factors associated with dose-schedule optimization and review the related innovative Bayesian adaptive designs. The assumptions, characteristics, limitations, and application scenarios of those designs are introduced. The review also summarizes some unresolved issues and future research opportunities for dose-schedule optimization.

由于早期临床试验的样本量较小，后续试验所确定的剂量-给药方案（dose-schedule regimens）的毒性与疗效特征可能尚未得到充分确立。近年来新型抗肿瘤治疗手段与联合疗法的发展进一步加剧了这一问题的复杂性。因此，学界日益认识到优化剂量-给药方案的核心地位，当前亟需全新的解决方案。贝叶斯自适应设计（Bayesian adaptive designs）可为在单次临床试验中同时评估多种剂量与给药方案提供高效的潜在途径，但此类自适应设计的真实世界应用案例仍较为匮乏。本文梳理了与剂量-给药方案优化相关的关键影响因素，并综述了相关的创新性贝叶斯自适应设计，介绍了此类设计的假设前提、核心特征、局限性与应用场景，同时总结了剂量-给药方案优化领域尚未解决的诸多问题与未来研究方向。