论文标题
原则选择基线协变量以说明具有生存终点的随机试验中的审查
Principled Selection of Baseline Covariates to Account for Censoring in Randomized Trials with a Survival Endpoint
论文作者
论文摘要
对时间端点的随机试验的分析几乎总是受到审查问题的困扰。由于通常未知的审查机制，因此分析通常采用非信息审查的假设。尽管随着越来越多的基线协变量的调整，这种假设通常变得更加合理，但这种调整也引起了人们的关注。预先指定哪些协变量将被调整为（以及如何）困难，从而促使使用数据驱动的变量选择过程，这可能会阻碍要绘制有效的推论。此外，对协变量的调整还增加了对模型错误指定的担忧，以及调整集的每个变化都会改变审查假设和治疗效果估计的事实。在本文中，我们讨论了这些关注点，并提出了一种简单的变量选择策略，旨在在大型样本中对空作品进行有效测试。该提案可以使用现成的软件来实施（惩罚）COX回归，并在经验上发现在模拟研究和实际数据分析中效果很好。
The analysis of randomized trials with time-to-event endpoints is nearly always plagued by the problem of censoring. As the censoring mechanism is usually unknown, analyses typically employ the assumption of non-informative censoring. While this assumption usually becomes more plausible as more baseline covariates are being adjusted for, such adjustment also raises concerns. Pre-specification of which covariates will be adjusted for (and how) is difficult, thus prompting the use of data-driven variable selection procedures, which may impede valid inferences to be drawn. The adjustment for covariates moreover adds concerns about model misspecification, and the fact that each change in adjustment set, also changes the censoring assumption and the treatment effect estimand. In this paper, we discuss these concerns and propose a simple variable selection strategy that aims to produce a valid test of the null in large samples. The proposal can be implemented using off-the-shelf software for (penalized) Cox regression, and is empirically found to work well in simulation studies and real data analyses.
