为多中心临床试验选择随机化方法,并考虑随机招聘
Oleksandr Sverdlov1, Yevgen Ryeznik2, Volodymyr Anisimov3
1Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA. alex.sverdlov@novartis.com.
BMC medical research methodology
|February 28, 2024
概括
动态平衡随机化 (DBR) 为具有竞争性患者招募的多中心随机对照试验 (RCT) 提供了优越的平衡-随机性权衡. 精心挑选的门可以提高DBR的性能.
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
- 医疗保健服务研究 医疗服务研究
背景情况:
- 多中心随机对照试验 (RCT) 需要仔细考虑设计,包括样本大小,中心选择和参与者招募策略.
- 顺序随机化方法对于多中心RCT,有或没有分层因子至关重要.
- 本研究的重点是根据竞争性患者招募政策评估随机化方法.
研究的目的:
- 系统地评估各种随机化方法用于多中心1:1RCT.
- 在竞争性患者招募流程的背景下评估这些方法.
- 为了比较不同分层和平衡技术的性能.
主要方法:
- 使用Poisson-gamma模型来模拟患者招募过程.
- 研究了16种不同的随机化方法,包括非分层,区域分层,中心分层和动态平衡随机化 (DBR).
- 蒙特卡洛模拟评估了统计属性,如招聘时间,治疗失衡和分配效率.
主要成果:
- 在平衡-随机性权衡方面,最大可容忍失衡 (MTI) 方法 (例如,大棒,Ehrenfest) 优于常规的可变区块设计 (PBD).
- DBR有效地控制了试验,区域和中心水平之间的不平衡,同时保持了随机化.
- 增加研究中心加速招聘,但可以增加中心级失衡;更大的块大小或MTI门提高了随机性-平衡权衡.
结论:
- 选择合适的随机化方法对于多中心RCT设计至关重要.
- 动态平衡随机化 (DBR) 成为具有竞争性患者招募的试验的高效策略.
- 在DBR中优化MTI值是强大的试验设计的建议.
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