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在小型双臂临床试验中用于随机化目的的数学编程工具:使用真实数据的案例研究

Alan R Vazquez¹, Weng-Kee Wong²

¹School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico.

Pharmaceutical statistics

|April 13, 2024

概括

现代临床试验随机化使用适应性方法. 数学编程增强了自适应性随机化,平衡受试者共变量和组大小,在小型试验中表现优于传统方法.

科学领域:

生物统计学生物统计学
临床试验设计临床试验设计
统计方法统计方法

背景情况:

适应性随机化在现代临床试验中是标准的,使用累积的数据来分配受试者.
数学编程提供先进的自适应方法,以平衡试验组的大小和共变量分布.
现有的共变量适应性随机化方法具有局限性,特别是在小型试验中.

研究的目的:

审查和比较基于数学编程的自适应随机化方法与常见的共变量自适应方法.
引入一种新的能量距离测量方法,以评估基于联合共变量分布的组差异.
为了证明使用这种新指标的数学编程方法的优越性.

主要方法:

对适应性随机化技术的审查,重点是数学编程方法.
引入能量距离度量来量化群体之间的共变量分布差异.
数字实验比较数学编程方法与标准的共变量适应方法.

主要成果:

数学编程方法在平衡主题共变量方面具有显著的优势.
拟议的能源距离测量方法提供了比边际分布比较更全面的对集团平衡的评估.
数值实验证实了在新能源距离度量下数学编程的有效性.

结论:

基于数学编程的自适应随机化在临床试验中提供了对组平衡的优越控制.
能量距离测量是评估随机化方法性能的一个有价值的工具.
这些先进的方法在小型临床试验中尤为有益,提高了研究有效性.

关键词:

同变适应性试验试验.能量距离,能量距离.的最小化方法.预先提供信息.

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