Informative right censoring in nonparametric survival models

Survival analysis models allow us to analyze and predict the time until a certain event occurs. Existing nonparametric models assume that the censoring of observations is random and unrelated to the study conditions. The estimators of the survival and hazard functions assume a constant survival probability between modes, have poor interpretability for datasets with multimodal time distributions, and lead to poor-quality data descriptions. In this paper, we investigate the quality of nonparametric models on four medical datasets with informative censoring and multimodal time distribution and propose a modification to improve the description quality. Proved properties of IBS and AUPRC metrics show that the best quality is achieved at survival function with unimodal time distribution. We propose modifying the nonparametric model based on virtual events from a truncated normal distribution that allows for the suppression of informative censoring. We compared the quality of the nonparametric models on multiple random subsets of datasets of different sizes using the AUPRC and IBS metrics. According to the comparison of the quality using Welch’s test, the proposed model with virtual events significantly outperformed the existing Kaplan–Meier model for all datasets (p-value $$