Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, MD 20892, USA.
Stat Med. 2019 Feb 20;38(4):530-544. doi: 10.1002/sim.7676. Epub 2018 Apr 19.
Cox model inference and the log-rank test have been the cornerstones for design and analysis of clinical trials with survival outcomes. In this article, we summarize some recently developed methods for analyzing survival data when the hazards may possibly be nonproportional and also propose some new estimators for summary measures of the treatment effect. These methods utilize the short-term and long-term hazard ratio model proposed in Yang and Prentice (2005), which contains the Cox model and also accommodates various nonproportional hazards scenarios. Without the proportional hazards assumption, these methods often improve the log-rank test and inference procedures based on the Cox model, as well as nonparametric procedures currently available in the literature. The proposed methods have sound theoretical justifications and can be computed quickly. R codes for implementing them are available. Detailed illustrations with 3 clinical trials are provided.
Cox 模型推断和对数秩检验一直是生存结局临床试验设计和分析的基石。本文总结了一些最近开发的方法，用于分析当风险比可能不成比例时的生存数据，同时也提出了一些用于治疗效果综合度量的新估计量。这些方法利用了 Yang 和 Prentice（2005 年）提出的短期和长期风险比模型，该模型包含了 Cox 模型，也适应了各种不成比例风险的情况。在没有比例风险假设的情况下，这些方法通常会改进基于 Cox 模型的对数秩检验和推断程序，以及文献中当前可用的非参数程序。所提出的方法具有合理的理论依据，并且可以快速计算。提供了用于实现它们的 R 代码。通过 3 个临床试验进行了详细说明。
