Survival Analysis

Survival analysis is a branch of statistics which deals with death in biological organisms and failure in mechanical systems. This topic is called reliability theory or reliability analysis in engineering, and duration analysis or duration modeling in economics or event history analysis in sociology. Survival analysis attempts to answer questions such as: what is the fraction of a population which will survive past a certain time? Of those that survive, at what rate will they die or fail? Can multiple causes of death or failure be taken into account? How do particular circumstances or characteristics increase or decrease the odds of survival?

To answer such questions, it is necessary to define "lifetime". In the case of biological survival, death is unambiguous, but for mechanical reliability, failure may not be well-defined, for there may well be mechanical systems in which failure is partial, a matter of degree, or not otherwise localized in time. Even in biological problems, some events (for example, heart attack or other organ failure) may have the same ambiguity. The theory outlined below assumes well-defined events at specific times; other cases may be better treated by models which explicitly account for ambiguous events.

More generally, survival analysis involves the modeling of time to event data; in this context, death or failure is considered an "event" in the survival analysis literature – traditionally only a single event occurs for each subject, after which the organism or mechanism is dead or broken. Recurring event or repeated event models relax that assumption. The study of recurring events is relevant in systems reliability, and in many areas of social sciences and medical research.


Read more about Survival AnalysisCensoring, Fitting Parameters To Data, Non-parametric Estimation, Distributions Used in Survival Analysis

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List Of Important Publications In Statistics - Survival Analysis
2281868 Description First description of the now ubiquitous Kaplan-Meier estimator of survival functions from data with censored observations Importance ... data Importance Influence Evaluation of survival data and two new rank order statistics arising in its consideration Author Mantel, N Publication data 1966, Cancer ... Development of the logrank test for censored survival data ...
Predictive Analytics - Statistical Techniques - Survival or Duration Analysis
... Survival analysis is another name for time to event analysis ... as in engineering (reliability and failure time analysis) ... Censoring and non-normality, which are characteristic of survival data, generate difficulty when trying to analyze the data using conventional statistical models such as multiple linear regression ...

Famous quotes containing the words analysis and/or survival:

    Analysis as an instrument of enlightenment and civilization is good, in so far as it shatters absurd convictions, acts as a solvent upon natural prejudices, and undermines authority; good, in other words, in that it sets free, refines, humanizes, makes slaves ripe for freedom. But it is bad, very bad, in so far as it stands in the way of action, cannot shape the vital forces, maims life at its roots. Analysis can be a very unappetizing affair, as much so as death.
    Thomas Mann (1875–1955)

    However great a man’s fear of life, suicide remains the courageous act, the clear-headed act of a mathematician. The suicide has judged by the laws of chance—so many odds against one that to live will be more miserable than to die. His sense of mathematics is greater than his sense of survival. But think how a sense of survival must clamour to be heard at the last moment, what excuses it must present of a totally unscientific nature.
    Graham Greene (1904–1991)