Mazumdar Madhu, Tu Donsheng, Zhou Xi Kathy
Division of Biostatistics and Epidemiology, Department of Public Health, Weill Cornell Medical College, New York, NY 10021, USA.
Stat Med. 2006 Dec 15;25(23):3949-59. doi: 10.1002/sim.2521.
Non-randomized studies for the evaluation of a medical intervention are useful for quantitative hypothesis generation before the initiation of a randomized trial and also when randomized clinical trials are difficult to conduct. A strata-matched non-randomized design is often utilized where subjects treated by a test intervention are matched to a fixed number of subjects treated by a standard intervention within covariate based strata. In this paper, we consider the issue of sample size calculation for this design. Based on the asymptotic formula for the power of a stratified log-rank test, we derive a formula to calculate the minimum number of subjects in the test intervention group that is required to detect a given relative risk between the test and standard interventions. When this minimum number of subjects in the test intervention group is available, an equation is also derived to find the multiple that determines the number of subjects in the standard intervention group within each stratum. The methodology developed is applied to two illustrative examples in gastric cancer and sarcoma.
用于评估医学干预措施的非随机研究，在随机试验启动前用于定量假设生成，以及在难以开展随机临床试验时都很有用。通常采用分层匹配的非随机设计，即在基于协变量的分层内，将接受试验干预的受试者与固定数量接受标准干预的受试者进行匹配。在本文中，我们考虑这种设计的样本量计算问题。基于分层对数秩检验功效的渐近公式，我们推导出一个公式，用于计算试验干预组中检测试验与标准干预之间给定相对风险所需的最小受试者数量。当试验干预组中有此最小受试者数量时，还推导出一个方程，以找到确定各层中标准干预组受试者数量的倍数。所开发的方法应用于胃癌和肉瘤的两个示例。
