Department of Statistics, University of Virginia, 22904 Charlottesville, USA.
Contemp Clin Trials. 2013 Mar;34(2):262-9. doi: 10.1016/j.cct.2012.12.004. Epub 2012 Dec 24.
The balance of important baseline covariates is essential for convincing treatment comparisons. Stratified permuted block design and minimization are the two most commonly used balancing strategies, both of which require the covariates to be discrete. Continuous covariates are typically discretized in order to be included in the randomization scheme. But breakdown of continuous covariates into subcategories often changes the nature of the covariates and makes distributional balance unattainable. In this article, we propose to balance continuous covariates based on Kernel density estimations, which keeps the continuity of the covariates. Simulation studies show that the proposed Kernel-Minimization can achieve distributional balance of both continuous and categorical covariates, while also keeping the group size well balanced. It is also shown that the Kernel-Minimization is less predictable than stratified permuted block design and minimization. Finally, we apply the proposed method to redesign the NINDS trial, which has been a source of controversy due to imbalance of continuous baseline covariates. Simulation shows that imbalances such as those observed in the NINDS trial can be generally avoided through the implementation of the new method.
重要基线协变量的平衡对于令人信服的治疗比较至关重要。分层随机区组设计和最小化是两种最常用的平衡策略，这两种策略都要求协变量是离散的。连续协变量通常被离散化以便包含在随机化方案中。但是，将连续协变量细分为子类别通常会改变协变量的性质，并且无法实现分布平衡。在本文中，我们提出基于核密度估计来平衡连续协变量，这保持了协变量的连续性。模拟研究表明，所提出的核最小化可以实现连续和分类协变量的分布平衡，同时还保持了组大小的良好平衡。还表明，核最小化比分层随机区组设计和最小化更不可预测。最后，我们将所提出的方法应用于重新设计 NINDS 试验，该试验由于连续基线协变量不平衡而引起争议。模拟表明，通过实施新方法，通常可以避免 NINDS 试验中观察到的不平衡等问题。
