School of Electrical Engineering, Korea University, Seoul, Republic of Korea.
School of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon, Republic of Korea.
PLoS One. 2023 Nov 16;18(11):e0294513. doi: 10.1371/journal.pone.0294513. eCollection 2023.
Traditionally, datasets with multiple censored time-to-events have not been utilized in multivariate analysis because of their high level of complexity. In this paper, we propose the Censored Time Interval Analysis (CTIVA) method to address this issue. It estimates the joint probability distribution of actual event times in the censored dataset by implementing a statistical probability density estimation technique on the dataset. Based on the acquired event time, CTIVA investigates variables correlated with the interval time of events via statistical tests. The proposed method handles both categorical and continuous variables simultaneously-thus, it is suitable for application on real-world censored time-to-event datasets, which include both categorical and continuous variables. CTIVA outperforms traditional censored time-to-event data handling methods by 5% on simulation data. The average area under the curve (AUC) of the proposed method on the simulation dataset exceeds 0.9 under various conditions. Further, CTIVA yields novel results on National Sample Cohort Demo (NSCD) and proteasome inhibitor bortezomib dataset, a real-world censored time-to-event dataset of medical history of beneficiaries provided by the National Health Insurance Sharing Service (NHISS) and National Center for Biotechnology Information (NCBI). We believe that the development of CTIVA is a milestone in the investigation of variables correlated with interval time of events in presence of censoring.
传统上，由于多 censored 时间事件数据集的复杂性很高，因此在多变量分析中未使用此类数据集。在本文中，我们提出了 Censored Time Interval Analysis (CTIVA) 方法来解决此问题。它通过在数据集上实施统计概率密度估计技术来估计 censored 数据集中实际事件时间的联合概率分布。基于获得的事件时间，CTIVA 通过统计检验研究与事件间隔时间相关的变量。所提出的方法同时处理分类和连续变量-因此，它适用于包含分类和连续变量的真实 censored 时间事件数据集。在模拟数据中，CTIVA 比传统的 censored 时间事件数据处理方法提高了 5%。在各种条件下，该方法在模拟数据集上的平均曲线下面积 (AUC) 均超过 0.9。此外，CTIVA 在 National Sample Cohort Demo (NSCD) 和蛋白酶体抑制剂硼替佐米数据集上产生了新的结果，该数据集是由 National Health Insurance Sharing Service (NHISS) 和 National Center for Biotechnology Information (NCBI) 提供的受益人的医疗历史真实 censored 时间事件数据集。我们相信，CTIVA 的开发是研究存在 censoring 时与事件间隔时间相关的变量的一个里程碑。
