两样本生存概率曲线：一种用于分析临床试验中事件发生时间数据的图形方法。
Two-sample survival probability curves: A graphical approach for the analysis of time to event data in clinical trials.
作者信息
Castro-Pearson Sandra, Le Chap T, Luo Xianghua
机构信息
Division of Biostatistics, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN 55455, USA.
Division of Biostatistics, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA.
出版信息
Contemp Clin Trials. 2022 Apr;115:106707. doi: 10.1016/j.cct.2022.106707. Epub 2022 Feb 14.
With the aim to improve the communication of trial results, we introduce a novel graphical approach that complements the analysis of time to event outcomes in two-arm randomized trials. We define the so-called two-sample survival probability curve and propose a nonparametric estimator of the curve based on a random walk using Kaplan-Meier survival estimates for the two arms. We then use the estimated curve to visualize treatment effect as well as potential effect modification of factors of interest. We also propose to estimate two-sample survival probability curves within the framework of the Cox model to graphically assess model fit. The proposed two-sample survival probability plot puts trials in a standardized [0,1] × [0,1] space, allowing for a simple visualization of the main effect, effect modification, and the adequacy of a model fit.
为了改善试验结果的交流，我们引入了一种新颖的图形方法，该方法补充了双臂随机试验中事件发生时间结局的分析。我们定义了所谓的两样本生存概率曲线，并基于随机游走提出了该曲线的非参数估计器，该随机游走使用了两组的Kaplan-Meier生存估计值。然后，我们使用估计的曲线来直观显示治疗效果以及感兴趣因素的潜在效果修正。我们还建议在Cox模型的框架内估计两样本生存概率曲线，以图形方式评估模型拟合。所提出的两样本生存概率图将试验置于标准化的[0,1]×[0,1]空间中，从而可以简单直观地显示主效应、效应修正和模型拟合的充分性。
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