预测模型引导的随机化提高了早期试验的效率:来自调查和模拟的证据
Sihong Zhang1, Justin Zhao1, Yanguang Cao1,2
1Division of Pharmacotherapy and Experimental Therapeutics, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Clinical and translational science
|February 16, 2026
概括
改善早期瘤学试验需要更好的随机化. 使用像ROPRO这样的预后模型,而不仅仅是ECOG状态,增强了统计能力,并减少了检测治疗效应的样本大小需求.
科学领域:
- 在瘤学瘤学.
- 临床试验 临床试验
- 生物统计学 生物统计学
背景情况:
- 早期瘤学试验在检测治疗效果方面面临挑战,原因是样本规模小,患者异质.
- 标准随机化往往不充分利用关键预后因素,可能减少统计能力并引入偏差.
- 已建立的预后变量,如ECOG性能状态,在当前的试验设计中经常被不足利用.
研究的目的:
- 评估基于预后模型的随机化策略,使用现实世界预测得分 (ROPRO).
- 为了比较基于ROPRO的随机化与基于ECOG的随机化的统计能力和样本大小要求.
- 支持在早期瘤学试验中使用预后模型信息随机化,与FDA的最佳项目目标保持一致.
主要方法:
- 在clinicaltrials.gov上调查了113项随机瘤学试验,以评估预后因素的利用率.
- 开发了现实世界预测得分 (ROPRO),将27个基线变量集成到单个风险得分中.
- 采用半合成模拟来比较基于ROPRO的随机化与ECOG随机化在各种生存模型和治疗效果大小.
主要成果:
- 与ECOG随机化相比,基于ROPRO的随机化始终提高了统计能力.
- 罗普罗战略减少了检测治疗效应所需的样本大小.
- 功率优势在+1至+11个百分点之间,在适度的样本大小下显著增长.
结论:
- 以预测模型为基础的随机化策略,如使用ROPRO,在早期瘤学试验中增强了统计能力.
- 这种方法可以通过减少样本大小要求,导致更有效的试验设计.
- 在注册试验之前,实施先进的随机化方法对于优化剂量和治疗方案选择至关重要.
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