Survival analysis is a statistical method used to analyze the time until an event of interest occurs. The event of interest could be anything from the failure of a mechanical component, to the onset of a disease, to the death of an organism. Survival analysis allows us to estimate the probability of the event occurring at any given time, and to model the relationship between the event and one or more variables of interest.
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.
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