A case-cohort design is a two-phase sampling design frequently used to analyze censored survival data in a cost-effective way, where a subcohort is usually selected using simple random sampling or stratified simple random sampling. In this paper, we propose an efficient sampling procedure based on balanced sampling when selecting a subcohort in a case-cohort design. A sample selected via a balanced sampling procedure automatically calibrates auxiliary variables. When fitting a Cox model, calibrating sampling weights has been shown to lead to more efficient estimators of the regression coefficients (Breslow et al., 2009a, b). The reduced variabilities over its counterpart with a simple random sampling are shown via extensive simulation experiments. The proposed design and estimation procedure are also illustrated with the well-known National Wilms Tumor Study dataset.

翻译：病例队列设计是一种两阶段抽样设计，常用于以经济有效的方式分析删失生存数据，其中通常采用简单随机抽样或分层简单随机抽样来选择子队列。本文提出了一种基于平衡抽样的高效抽样方法，用于在病例队列设计中选取子队列。通过平衡抽样程序选取的样本能够自动校准辅助变量。已有研究表明，在拟合Cox模型时，校准抽样权重可得到更高效的回归系数估计量（Breslow等，2009a, b）。通过大量模拟实验证明了其相比简单随机抽样的方差减小特性。本文还使用著名的国家威尔姆斯肿瘤研究数据集对所提出的设计与估计程序进行了说明。