Parast Layla, Cai Tianxi, Tian Lu
Statistics Group, RAND Corporation, Santa Monica, California.
Department of Biostatistics, Harvard University, Boston, Massachusetts.
Biometrics. 2019 Dec;75(4):1253-1263. doi: 10.1111/biom.13067. Epub 2019 Apr 22.
The development of methods to identify, validate, and use surrogate markers to test for a treatment effect has been an area of intense research interest given the potential for valid surrogate markers to reduce the required costs and follow-up times of future studies. Several quantities and procedures have been proposed to assess the utility of a surrogate marker. However, few methods have been proposed to address how one might use the surrogate marker information to test for a treatment effect at an earlier time point, especially in settings where the primary outcome and the surrogate marker are subject to censoring. In this paper, we propose a novel test statistic to test for a treatment effect using surrogate marker information measured prior to the end of the study in a time-to-event outcome setting. We propose a robust nonparametric estimation procedure and propose inference procedures. In addition, we evaluate the power for the design of a future study based on surrogate marker information. We illustrate the proposed procedure and relative power of the proposed test compared to a test performed at the end of the study using simulation studies and an application to data from the Diabetes Prevention Program.
鉴于有效替代标志物具有降低未来研究所需成本和随访时间的潜力，开发用于识别、验证和使用替代标志物来检验治疗效果的方法一直是研究的热点领域。已经提出了几个量和程序来评估替代标志物的效用。然而，很少有方法被提出来解决如何在更早的时间点使用替代标志物信息来检验治疗效果，特别是在主要结局和替代标志物受到删失的情况下。在本文中，我们提出了一种新颖的检验统计量，用于在生存结局设置中使用研究结束前测量的替代标志物信息来检验治疗效果。我们提出了一种稳健的非参数估计程序并提出了推断程序。此外，我们基于替代标志物信息评估未来研究设计的检验效能。我们通过模拟研究以及对糖尿病预防计划数据的应用，说明了所提出的程序以及所提出检验相对于在研究结束时进行的检验的相对效能。
