School of Electrical Engineering, Korea University, Seoul, South Korea.
PLoS One. 2020 Oct 1;15(10):e0239760. doi: 10.1371/journal.pone.0239760. eCollection 2020.
In general survival analysis, multiple studies have considered a single failure time corresponding to the time to the event of interest or to the occurrence of multiple events under the assumption that each event is independent. However, in real-world events, one event may impact others. Essentially, the potential structure of the occurrence of multiple events can be observed in several survival datasets. The interrelations between the times to the occurrences of events are immensely challenging to analyze because of the presence of censoring. Censoring commonly arises in longitudinal studies in which some events are often not observed for some of the subjects within the duration of research. Although this problem presents the obstacle of distortion caused by censoring, the advanced multivariate survival analysis methods that handle multiple events with censoring make it possible to measure a bivariate probability density function for a pair of events. Considering this improvement, this paper proposes a method called censored network estimation to discover partially correlated relationships and construct the corresponding network composed of edges representing non-zero partial correlations on multiple censored events. To demonstrate its superior performance compared to conventional methods, the selecting power for the partially correlated events was evaluated in two types of networks with iterative simulation experiments. Additionally, the correlation structure was investigated on the electronic health records dataset of the times to the first diagnosis for newborn babies in South Korea. The results show significantly improved performance as compared to edge measurement with competitive methods and reliability in terms of the interrelations of real-life diseases.
在一般的生存分析中，许多研究都考虑了单个失效时间，该时间对应于感兴趣事件的时间或多个事件的发生，假设每个事件都是独立的。然而，在现实世界的事件中，一个事件可能会影响其他事件。本质上，可以在几个生存数据集观察到多个事件发生的潜在结构。由于存在删失，分析事件发生时间之间的相互关系极具挑战性。删失通常出现在纵向研究中，其中对于一些研究对象，一些事件在研究期间经常未被观察到。尽管这个问题存在由删失引起的扭曲障碍，但处理带有删失的多个事件的先进多元生存分析方法使其能够测量一对事件的双变量概率密度函数。考虑到这一改进，本文提出了一种称为删失网络估计的方法，用于发现部分相关关系，并构建由代表多个删失事件上非零部分相关的边组成的相应网络。为了证明与传统方法相比的优越性能，通过迭代模拟实验在两种类型的网络上评估了部分相关事件的选择能力。此外，还在韩国新生儿首次诊断时间的电子健康记录数据集上研究了相关结构。结果表明，与竞争方法的边缘测量相比，性能有显著提高，并且在现实疾病的相互关系方面具有可靠性。
