Shan Guogen, Li Yulin, Lu Xinlin, Zhang Yahui, Wu Samuel S
Department of Biostatistics, University of Florida, Gainesville, 32610, FL, USA.
BMC Med Res Methodol. 2024 Jan 25;24(1):22. doi: 10.1186/s12874-024-02151-3.
When multiple influential covariates need to be balanced during a clinical trial, stratified blocked randomization and covariate-adaptive randomization procedures are frequently used in trials to prevent bias and enhance the validity of data analysis results. The latter approach is increasingly used in practice for a study with multiple covariates and limited sample sizes. Among a group of these approaches, the covariate-adaptive procedures proposed by Pocock and Simon are straightforward to be utilized in practice. We aim to investigate the optimal design parameters for the patient treatment assignment probability of their developed three methods. In addition, we seek to answer the question related to the randomization performance when additional covariates are added to the existing randomization procedure. We conducted extensive simulation studies to address these practically important questions.
在临床试验中,当需要平衡多个有影响的协变量时,分层区组随机化和协变量自适应随机化程序经常用于试验中,以防止偏差并提高数据分析结果的有效性。后一种方法在具有多个协变量且样本量有限的研究中越来越多地在实践中使用。在这组方法中,Pocock和Simon提出的协变量自适应程序在实践中易于使用。我们旨在研究他们开发的三种方法中患者治疗分配概率的最优设计参数。此外,我们试图回答当将额外的协变量添加到现有的随机化程序中时与随机化性能相关的问题。我们进行了广泛的模拟研究来解决这些实际重要问题。