在固定样本时间到事件的临床试验中,用自适应性随机化进行审查-强大估计
Navneet R Hakhu1, Daniel L Gillen2
1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States.
Biometrics
|December 19, 2025
概括
临床试验中的自适应随机化可以对时间到事件数据的结果产生偏见. 一个新的强大的估计器纠正了改变的审查模式,改善了治疗疗效估计.
科学领域:
- 临床试验方法论 临床试验方法论
- 生物统计学 生物统计学
- 生存分析的分析.
背景情况:
- 适应性随机化在临床试验期间动态调整治疗分配概率.
- 它对在具有时间变化的效果的时间对事件试验中估计治疗疗效的影响尚未完全理解.
- 当治疗效果随着时间的推移而改变时,现有的方法可能不可靠.
研究的目的:
- 调查适应性随机化对在时间与事件试验中估计边际危险比率的影响.
- 开发和验证一个强大的统计方法,以解决通过适应性随机化引入的潜在偏差.
- 将拟议的方法应用于现实世界的临床试验数据.
主要方法:
- 分析推导显示适应性随机化改变了审查模式.
- 蒙特卡洛模拟以评估Cox比例危险估计器中的偏差.
- 开发一种使用重新加权的部分概率得分进行审查的强大估计器.
- 导出非对称性属性和对拟议估计器的有限样本评估.
主要成果:
- 适应性随机化在时间到事件试验中明显改变了审查模式.
- 在适应性随机化下,标准的Cox比例危险估计器可以产生偏差的结果.
- 拟议的审查-强大的估计器有效地纠正了这些偏见.
- 该方法的性能通过模拟和应用到真正的艾滋病临床试验来验证.
结论:
- 由于潜在的偏差,自适应性随机化需要在时间到事件试验分析中仔细考虑.
- 拟议的强大估计器为估计治疗疗效提供了一种可靠的方法.
- 这种方法提高了适应性临床试验中生存分析的准确性.
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