The analysis of randomized trials with time-to-event endpoints is nearly always plagued by the problem of censoring. In practice, such analyses typically invoke the assumption of noninformative censoring. While this assumption usually becomes more plausible as more baseline covariates are being adjusted for, such adjustment also raises concerns. Prespecification 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 article, we discuss these concerns and propose a simple variable selection strategy designed 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.

中文翻译：

---

有原则地选择基线协变量以解释具有生存终点的随机试验中的审查

具有时间到事件终点的随机试验分析几乎总是受到审查问题的困扰。在实践中，此类分析通常会调用非信息审查的假设。虽然随着更多基线协变量被调整，这种假设通常变得更合理，但这种调整也引起了担忧。预先指定哪些协变量将被调整（以及如何调整）是困难的，因此促使使用数据驱动的变量选择程序，这可能会妨碍得出有效的推论。此外，协变量的调整增加了对模型错误指定的担忧，并且调整集的每次变化也会改变审查假设和治疗效果估计值。在本文中，我们讨论了这些问题，并提出了一种简单的变量选择策略，旨在在大样本中对空值进行有效测试。该提议可以使用现成的软件来实现（惩罚）Cox 回归，并且根据经验发现在模拟研究和真实数据分析中效果很好。