General Right Censoring and Its Impact on the Analysis of Survival Data

TLDR

General right censoring complicates survival data analysis by partitioning models into noninformative and informative censoring classes, yet most methods assume noninformative censoring. The study aims to examine the impact of general right censoring on survival analysis, focusing on noninformative and informative censoring models, their identifiability, and the consequences of incorrect censoring assumptions. The authors analyze relationships among three noninformative censoring models, employ likelihood methods to infer survival distributions, and assess censoring effects on K‑sample comparisons and model testing.

Abstract

This paper concerns general right censoring and some of the difficulties it creates in the analysis of survival data. A general formulation of censored-survival processes leads to the partition of all models into those based on noninformative and informative censoring. Nearly all statistical methods for censored data assume that censoring is noninformative. Topics considered within this class include: the relationships between three models for noninformative censoring, the use of likelihood methods for inferences about the distribution of survival time, the effects of censoring on the K-sample problem, and the effects of censoring on model testing. Also considered are several topics which relate to informative censoring models. These include: problems of nonidentifiability that can be encountered when attempting to assess a set of data for the type of censoring in effect, the consequences of falsely assuming that censoring is noninformative, and classes of informative censoring models.