title: Statistical methods in epidemiology. IX. Survival (failure-time) models.
abstract: OBJECTIVE
  This article introduces readers to survival (failure-time) models, with a focus on Kaplan-Meier curves, Cox regression and sample size estimation.
  
  
  METHODS
  An example is used to show readers how to calculate a Kaplan-Meier curve from first principles.
  
  
  RESULTS
  What makes survival data unique is censoring. Readers should understand censoring before undertaking an analysis of survival data.
  
  
  CONCLUSIONS
  The Cox model continues to set the standard for survival models, and will continue well into the future.
authors.affiliation: Academic Cardiology, University of Hull, and Hull York Medical School, Kingston-upon-Hull, UK. asr1960@hotmail.com
authors.forename: Alan S
authors.initials: AS
authors.lastname: Rigby
authors.forename: Jufen
authors.initials: J
authors.lastname: Zhang
authors.forename: Kevin M
authors.initials: KM
authors.lastname: Goode
mesh_terms: D004812:Epidemiologic Methods; D006801:Humans; D015233:Models, Statistical; D016016:Proportional Hazards Models; D018401:Sample Size; D016019:Survival Analysis