Department of Applied Mathematics, Computer Science and Statistics, Ghent, Belgium.
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
Stat Med. 2021 Aug 15;40(18):4108-4121. doi: 10.1002/sim.9017. Epub 2021 May 12.
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.
对于时间事件终点的随机试验分析几乎总是受到删失问题的困扰。在实践中，这种分析通常假设删失是无信息的。虽然随着更多的基线协变量被调整，这种假设通常变得更加合理，但这种调整也引起了关注。对于哪些协变量将被调整（以及如何调整）的预设是困难的，因此促使使用数据驱动的变量选择程序，这可能会阻碍有效推断的得出。协变量的调整还增加了对模型误设定的关注，以及每个调整集的变化也改变了删失假设和处理效果估计量的事实。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，旨在在大样本中对零假设进行有效检验。该建议可以使用现成的（惩罚）Cox 回归软件来实现，并且在模拟研究和实际数据分析中被证明效果良好。
