在临床试验中,对临床试验的时间到事件安全终点的顺序监测
Michael J Martens1, Qinghua Lian1, Nancy L Geller2
1Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, USA.
Clinical trials (London, England)
|May 21, 2025
概括
这项研究引入了临床试验安全监测的时间对事件分析,通过将预期毒性降低高达20%的性能,优于传统的二进制方法. 开发的R包"停止规则"有助于设计有效的安全停车规则.
科学领域:
- 临床试验 临床试验
- 生物统计学 生物统计学
- 药物监督 药物监督 药物监督
背景情况:
- 在II期和III期临床试验中,安全监测至关重要,以保护患者免受治疗毒性影响.
- 目前的停止规则经常使用二进制毒性数据,可能错过了更有效地识别风险的机会.
- 时间到事件数据,如果可用,可以提供更大的统计能力和减少试验持续时间,以检测过度风险.
研究的目的:
- 调查和比较安全监测的统计方法,使用临床试验中的时间到事件数据.
- 开发和说明一个R软件包",stoppingrule",用于设计和评估这些新的停止规则.
- 评估基于时间到事件的停止规则与传统二进制方法的性能.
主要方法:
- 基于Wang-Tsiatis测试,贝叶斯式Gamma-Poisson模型和顺序概率比测试的安全停车规则的评估性能指标.
- 开发并使用R包"停止规则"来设计和评估基于时间到事件的停止规则.
- 将这些方法应用于骨髓移植的II期临床试验场景 (NCT01998633).
主要成果:
- 与二元毒性方法相比,时间到事件方法显示预期毒性的显著降低,高达20%,二元毒性方法.
- 积极的早期监测方法 (例如,Gamma-Poisson具有较弱的先验) 通常显示出较低的毒性计数和功率.
- 某些测试 (Pocock,最大化顺序概率比测试) 显示了高的早期停止,但降低了整体功率和增加了毒性.
结论:
- 基于时间到事件的安全监控在最大限度地减少患者的毒性暴露方面提供了卓越的性能.
- 建议使用弱先验或截断序列概率比测试的玛-波桑模型来构建强大的安全停车规则.
- "停止规则"R包为研究人员设计和评估临床试验中先进的安全监测程序提供了宝贵的工具.
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