Network estimation for censored time-to-event data for multiple events based on multivariate survival analysis.

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.