Willems Sjw, Schat A, van Noorden M S, Fiocco M
1 Mathematical Institute, Leiden University, Leiden, The Netherlands.
2 Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands.
Stat Methods Med Res. 2018 Feb;27(2):323-335. doi: 10.1177/0962280216628900. Epub 2016 Mar 17.
Censored data make survival analysis more complicated because exact event times are not observed. Statistical methodology developed to account for censored observations assumes that patients' withdrawal from a study is independent of the event of interest. However, in practice, some covariates might be associated to both lifetime and censoring mechanism, inducing dependent censoring. In this case, standard survival techniques, like Kaplan-Meier estimator, give biased results. The inverse probability censoring weighted estimator was developed to correct for bias due to dependent censoring. In this article, we explore the use of inverse probability censoring weighting methodology and describe why it is effective in removing the bias. Since implementing this method is highly time consuming and requires programming and mathematical skills, we propose a user friendly algorithm in R. Applications to a toy example and to a medical data set illustrate how the algorithm works. A simulation study was carried out to investigate the performance of the inverse probability censoring weighted estimators in situations where dependent censoring is present in the data. In the simulation process, different sample sizes, strengths of the censoring model, and percentages of censored individuals were chosen. Results show that in each scenario inverse probability censoring weighting reduces the bias induced in the traditional Kaplan-Meier approach where dependent censoring is ignored.
删失数据使生存分析变得更加复杂，因为无法观察到确切的事件发生时间。为处理删失观测值而开发的统计方法假定患者退出研究与感兴趣的事件无关。然而，在实际中，一些协变量可能与生存时间和删失机制都有关联，从而导致相依删失。在这种情况下，像Kaplan-Meier估计量这样的标准生存技术会给出有偏差的结果。逆概率删失加权估计量就是为校正由相依删失导致的偏差而开发的。在本文中，我们探讨逆概率删失加权方法的使用，并描述其为何能有效消除偏差。由于实施该方法非常耗时且需要编程和数学技能，我们在R语言中提出了一种用户友好型算法。应用于一个简单示例和一个医学数据集说明了该算法的工作原理。进行了一项模拟研究，以调查在数据中存在相依删失的情况下逆概率删失加权估计量的性能。在模拟过程中，选择了不同的样本量、删失模型的强度以及删失个体的百分比。结果表明，在每种情况下，逆概率删失加权都能减少在忽略相依删失的传统Kaplan-Meier方法中所诱导的偏差。
