Beyene Kassu M, El Ghouch Anouar, Oulhaj Abderrahim
Institute of Statistics, Biostatistics and Actuarial Sciences, Catholic University of Louvain, Louvain la Neuve, Belgium.
Institute of Public Health, College of Medicine and Health Sciences, UAE University, Al-Ain, United Arab Emirates.
Biom J. 2019 Nov;61(6):1430-1447. doi: 10.1002/bimj.201800376. Epub 2019 Jul 16.
During the last decades, several approaches have been proposed to estimate the time-dependent area under the receiver operating characteristic curve (AUC) of risk tools derived from survival data. The validity of these estimators relies on some regularity assumptions among which a survival function being proper. In practice, this assumption is not always satisfied because a fraction of the population may not be susceptible to experience the event of interest even for long follow-up. Studying the sensitivity of the proposed estimators to the violation of this assumption is of substantial interest. In this paper, we investigate the performance of a nonparametric simple estimator, developed for classical survival data, in the case when the population exhibits a cure fraction. Motivated from the current practice of deriving risk tools in oncology and cardiovascular disease prevention, we also assess the loss, in terms of predictive performance, when deriving risk tools from survival models that do not acknowledge the presence of cure. The simulation results show that the investigated method is valid even under the presence of cure. They also show that risk tools derived from survival models that ignore the presence of cure have smaller AUC compared to those derived from survival models that acknowledge the presence of cure. This was also attested with a real data analysis from a breast cancer study.
在过去几十年中，已经提出了几种方法来估计从生存数据得出的风险工具的随时间变化的受试者工作特征曲线下面积（AUC）。这些估计器的有效性依赖于一些正则性假设，其中生存函数是恰当的。在实际中，这个假设并不总是满足，因为即使经过长时间随访，仍有一部分人群可能不会经历感兴趣的事件。研究所提出的估计器对该假设违背情况的敏感性具有重要意义。在本文中，我们研究了一种为经典生存数据开发的非参数简单估计器在总体存在治愈比例情况下的性能。受肿瘤学和心血管疾病预防中当前推导风险工具的实践启发，我们还评估了从不承认存在治愈情况的生存模型推导风险工具时在预测性能方面的损失。模拟结果表明，即使存在治愈情况，所研究的方法仍然有效。结果还表明，从不承认存在治愈情况的生存模型推导的风险工具的AUC比从承认存在治愈情况的生存模型推导的风险工具的AUC小。这也在一项乳腺癌研究的真实数据分析中得到了证实。
