One of the key features of survival analysis is the use of survival curves, which show the probability of survival (or the probability of the event not occurring) over time. The shape of the survival curve can provide insights into the underlying distribution of the event time and can help identify important factors that affect the risk of the event.

What tends to make survival analysis special is censoring. Censoring arises in survival analysis when some study participants do not experience the event of interest during the study period, or are lost to follow-up before the event occurs. In these cases, their event time is unknown or “censored” at the time of study termination.

Survival analysis is commonly used in medical research, engineering, finance, and other fields where the timing of events is of interest. Some popular techniques used in survival analysis include the Kaplan-Meier estimator, Cox proportional hazards model, and parametric survival models.