用于小型双臂临床试验随机化的数学编程工具:真实数据案例研究

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-04-13 DOI:10.1002/pst.2388
Alan R. Vazquez, Weng‐Kee Wong
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引用次数: 0

摘要

临床试验中的现代随机化方法无一例外都是自适应的,也就是说,在将下一个受试者分配到治疗组时,会使用试验中积累的信息。最近的一些自适应随机化方法使用数学编程来构建有吸引力的临床试验,以平衡治疗组的特征,如治疗组的规模和受试者的协变量分布。我们回顾了其中一些方法,并将它们的性能与小型临床试验中常见的协变量自适应随机化方法进行了比较。我们引入了一种能量距离测量方法,利用受试者协变量的联合分布来比较两组之间的差异。与使用受试者的边际协变量分布来评估两组之间的差异相比,这种度量方法更具吸引力。通过数值实验,我们证明了数学编程方法在新指标下的优势。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。
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Mathematical programming tools for randomization purposes in small two‐arm clinical trials: A case study with real data
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.
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来源期刊
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics. The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.
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