Survival Data are measurements in time from a well defined origin until a particular event occurs. The event is usually death (e.g., lifetime from birth to death), but it could also be a change of state (e.g., occurrence of a disease or time to failure of an electrical component).
Of central importance to the study of risk is the probability that a system will perform and maintain its function (remain in a state) during a specified time interval (0, t). Let F(t) = P(T ≤ t) be the cumulative distribution function for the probability that a system fails before time t and conversely \(R(t) = 1 - F(t)\) be the survival function for the system. Data from survival studies are often censored (the system has not failed during the study) so that survival times are larger than censored survival times. For example, if the response variable is the lifetime of an individual (or component), then the censored data are represented as (y i , δ i ) where the indicator variable δis equal to 1 if the...
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References and Further Reading
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Melnick, E.L. (2011). Modeling Survival Data. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_370
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