在随机临床试验中的最小化
Elisabeth Coart1, Perrine Bamps1, Emmanuel Quinaux1
1IDDI, Louvain-la-Neuve, Belgium.
Statistics in medicine
|October 23, 2023
概括
在临床试验中,最小化,一种共变量适应程序,与完全随机或分层封锁设计相比,提高了治疗组平衡. 这种方法对于具有多个预后因素或中心的复杂试验尤其有益.
科学领域:
- 临床试验设计和方法论
- 生物统计学 生物统计学
- 医学研究 医学研究
背景情况:
- 随机试验通过随机分配确保治疗组的可比性,控制混因素.
- 尽管随机分配,预后因素的不平衡可能会发生,可能会导致结果偏差.
- 试验者,监管机构和利益相关者更喜欢在治疗组之间平衡预后因素.
研究的目的:
- 为了比较最小化,共变量适应程序的性能,与完全随机分配和分层封锁设计进行比较.
- 根据运营特点,可预测性和实现的平衡来评估这些分配程序.
- 确定在临床试验中实现最小化的最佳场景.
主要方法:
- 将最小化与完全随机分配和分层封锁设计进行比较.
- 利用来自两个临床试验 (卵巢癌,与年龄相关的黄斑变性) 的个体患者数据.
- 分析运行特征使用异常和随机化测试,评估可预测性,并测量实现的平衡.
- 研究者在50个实际试验中使用最小化实现了平衡.
主要成果:
- 与完全随机和分层封锁设计相比,最小化证明了预后因素的优越平衡.
- 像最小化这样的共变量适应程序可以更好地控制基线不平衡.
- 在具有众多预后因素,多个中心或适度样本大小的试验中,最小化显示了特定的优势.
结论:
- 最小化是一种有效的共变量适应程序,用于在随机试验中实现均衡的治疗组.
- 它在复杂的试验环境中特别有价值,提高了治疗效果估计的可靠性.
- 该研究强调了最小化对于改善临床试验设计和执行的有用性.
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