Survival Analysis

Survival analysis is one of the oldest ¿ elds of statistics, going back to the 17th century. The ¿ rst life-table was presented by John Graunt in 1662 (Kreager, 1988). Life-tables are used extensively in analysing the mortality data obtained from toxicology studies, especially carcinogenicity and long-term repeated dose administration studies (Portier, 1988; FDA, 2007) and ecotoxicology studies (Gentile et al., 1982; Van Leeuwen et al., 1985; Bechmann, 1994). A major advancement in the survival analysis took place in 1958, when Kaplan and Meier proposed their ‘estimator of the survival curve’ (Kaplan and Meier, 1958). Since then, the ¿ eld of survival analysis progressed signi¿ cantly with the contributions from several statisticians (Mantel and Haenszel, 1959; Cox, 1972; Aalen, 1976; Aalen, 1980; Diggle, et al., 2007; Aalen et al., 2008). The term “survival” is a bit misleading. Originally the analysis was concerned with time from treatment until death, hence the name, “survival analysis”. Survival analysis is a collection of statistical procedures for data analysis for which the outcome variable of interest is time until an event occurs (Kleinbaum and Klein, 2005). According to Akritas (2004), survival analysis is a method for the analysis of data on an event observed over time and the study of factors associated with the occurrence rates of this event. The event could be the time until a generator’s bearing seizes, the time until a patient dies or the time until a person ¿ nds employment (Cleves et al., 2008). Survival analysis can be used inmany ¿ elds, such as medicine, biology, public healthand epidemiology (Kul, 2010). In pharmacology and toxicology survival analysis is used in analyzing the events like time to death, time to signs occurrence, disappearance and reoccurrence, time to recovery etc. of the experimental animals.