Survival data arise when we are interested in the time of occurrence of an event, and the survival function describes the cumulative probability of surviving beyond a given time point in a given group of individuals. Methods to estimate the survival function can be classified in nonparametric and model-based methods and the main ones are described here. Among the nonparametric ones, the product limit, the life table, the Fleming–Harrington, and Bayesian methods are considered. Assumptions on right censoring and left truncation are discussed, with practical advices on how to present and read the survival curve. Aspects that are special to estimation of survival in the presence of time-dependent variables and competing risks are mentioned. Among the model-based methods, the use of estimators derived by the Cox model and parametric models is shown. Finally, relative survival and period analysis estimators are presented as useful tools in describing the survival experience in disease (cancer) registries, and survival estimators that account for special features in (nonrandom) designs are also briefly mentioned.

Rebora, P., Valsecchi, M. (2016). Survival Function. In Wiley StatsRef: Statistics Reference Online (pp. 1-10). Wiley [10.1002/9781118445112.stat07882].

Survival Function

REBORA, PAOLA
Primo
;
VALSECCHI, MARIA GRAZIA
Ultimo
2016

Abstract

Survival data arise when we are interested in the time of occurrence of an event, and the survival function describes the cumulative probability of surviving beyond a given time point in a given group of individuals. Methods to estimate the survival function can be classified in nonparametric and model-based methods and the main ones are described here. Among the nonparametric ones, the product limit, the life table, the Fleming–Harrington, and Bayesian methods are considered. Assumptions on right censoring and left truncation are discussed, with practical advices on how to present and read the survival curve. Aspects that are special to estimation of survival in the presence of time-dependent variables and competing risks are mentioned. Among the model-based methods, the use of estimators derived by the Cox model and parametric models is shown. Finally, relative survival and period analysis estimators are presented as useful tools in describing the survival experience in disease (cancer) registries, and survival estimators that account for special features in (nonrandom) designs are also briefly mentioned.
Voce in dizionario o enciclopedia
time-to-event analysis, censored survival data, product limit estimator, life table, hazard function
English
Wiley StatsRef: Statistics Reference Online
9781118445112
Rebora, P., Valsecchi, M. (2016). Survival Function. In Wiley StatsRef: Statistics Reference Online (pp. 1-10). Wiley [10.1002/9781118445112.stat07882].
Rebora, P; Valsecchi, M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/137346
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