Beginner Explanation
Imagine you’re watching a movie, but you have to leave the theater before it ends. You know how long you watched, but you don’t know how it ends. This is like censoring in statistics. Sometimes, we collect data, but we don’t get the full picture because something happens—like a person dropping out of a study. We know some information, but not everything, which can make it tricky to understand the whole story.
Technical Explanation
Censoring occurs when the value of an observation is only partially known. In survival analysis, this often happens when subjects leave a study before it ends or are lost to follow-up. There are two main types of censoring: right censoring (when the event of interest hasn’t occurred by the end of the study) and left censoring (when the event occurred before the study began). To handle censoring, we often use methods such as Kaplan-Meier estimators or Cox proportional hazards models. For example, in R, we can use the ‘survival’ package to analyze censored data:
“`R
library(survival)
fit <- survfit(Surv(time, status) ~ group, data = mydata)
plot(fit)
```
Academic Context
Censoring is a critical concept in survival analysis and reliability engineering, where it is essential to account for incomplete data. Theoretical foundations can be traced back to survival models developed by Kaplan and Meier (1958) and Cox (1972). Key papers include Kaplan and Meier’s ‘Nonparametric Estimation from Incomplete Observations’ and Cox’s ‘Regression Models and Life-Tables.’ The mathematical treatment of censoring involves likelihood functions and the use of the Kaplan-Meier estimator to provide unbiased survival estimates despite incomplete data.
View Source: https://arxiv.org/abs/2511.16551v1