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Survival analysis models time to event data, typically time to death or failure time. Censored data are a common problem for survival analyses.

Survival analysis includes an array of non-parametric, semi-parametric, and fully parametric methods for analyzing time to event data. Often, these analyses aim to estimate a survival function, $S(t)$, which describes the proportion of subjects surviving at time $t$. A key feature of survival analysis is the ability to incorporate censored data, in which the event of interest does not occur during the observation period.

The most common form of censoring is "right censoring" where the event doesn't happen by the time the data are collected (e.g. patients who are still alive at the end of the study). Left censoring is when the event happens before the study starts and that is the only information on the time of the event. Interval censoring is when the event occurs at some point in the study, but the point is not precisely known.