School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico.
Department of Biostatistics, University of California, Los Angeles, California, USA.
Pharm Stat. 2024 Nov-Dec;23(6):794-812. doi: 10.1002/pst.2388. Epub 2024 Apr 13.
Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate-adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.
临床试验中的现代随机化方法通常是适应性的，这意味着下一个受试者被分配到治疗组时，会使用试验中累积的信息。最近的一些适应性随机化方法使用数学规划来构建有吸引力的临床试验，以平衡组特征，如它们的大小和受试者的协变量分布。我们回顾了其中的一些方法，并将它们的性能与小型临床试验中常见的协变量适应性随机化方法进行了比较。我们引入了一种能量距离度量，该度量使用受试者协变量的联合分布来比较两组之间的差异。与使用组的边际协变量分布来评估组之间的差异相比，这种度量更具吸引力。通过数值实验，我们在新度量下展示了数学规划方法的优势。在补充材料中，我们提供了 R 代码来重现我们的研究结果，并方便比较不同的随机化程序。
