title: Statistics for surgeons - understanding survival analysis.
abstract: Survival data stand out as a special statistical field. This paper tries to describe what survival data is and what makes it so special. Survival data concerns times to some events. A key point is the successive observation of time, which on the one hand leads to sometimes not being observed so that all that is known is that they exceed some given times (censoring), and on the other hand implies that predictions regarding the future course should be conditional on the present status (truncation). In the simplest case, this condition is that the individual is alive. The successive conditioning makes the hazard function, which describes the probability of an event happening during a short interval given that the individual is alive today, the most relevant concept. Here we discuss parametric as well as non-parametric methods. Examples are presented in a way that can be followed without the help of computers.
authors.affiliation: University of Lucknow, Lucknow, Uttar Pradesh India.
authors.forename: Girdhar Gopal
authors.initials: GG
authors.lastname: Agarwal
keywords: Censored data; Hazard function; Kaplan-Meier estimate; Logrank test; Survivor function