分布性随机化:在临床试验中选或评估多个同时干预的实用分数因子设计
Skerdi Haviari1,2, France Mentré3,4
1Université Paris Cité, Inserm, IAME, Paris, 75018, France. skerdi.haviari@aphp.fr.
BMC medical research methodology
|March 12, 2024
概括
这项研究引入了分布式随机化,一种试验设计,可以有效地同时评估许多干预措施,与具有许多潜在治疗条件的传统方法相比,显著减少样本大小.
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
- 实验设计 实验设计
背景情况:
- 许多医疗适应症有大量的干预措施,其有效性尚未得到证实,但具有良好的安全性.
- 同时对此类干预措施进行评估是可行和可取的.
- 本研究侧重于一个务实的分数因数试验设计.
研究的目的:
- 评估一个务实的分数因子试验设计,用于同时进行干预评估.
- 为了比较这个设计与因数式,平行臂和头对头试验.
- 为设计和分析分布式随机化试验提供最佳实践.
主要方法:
- 模拟场景包括4-20个候选干预措施,每个参与者分配2-8个干预方案.
- 假设二元结果,并测试各种干预有效性和相互作用场景.
- 利用高效的组合学,对样本大小/功率的模拟,以及统计测试 (比例差异,逻辑回归沃尔德测试) 与邦费罗尼校正.
主要成果:
- 分布性试验实现了2至7倍的样本大小减少,而不是平行臂试验.
- 与因数试验相比,当分配的干预措施较少时,分布性试验需要1到2倍更大的样本大小.
- 调整与相互作用项或包括控制臂的分析减轻了来自意想不到的有效干预的功率下降.
结论:
- 分布性随机化是大规模并行评估干预措施的可行设计,特别是在受试者数量有限的人群中.
- 推用于许多不充分描述的干预措施的环境,如疾病预防,数字健康或补充剂.
- 强调试验前模拟,并提供设计和分析的工具.
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