title: The analysis of survival data: the Kaplan-Meier method.
abstract: What is this patient's prognosis regarding graft rejection? Do patients using a particular drug live longer than those not using it? How does this co-morbidity affect access to transplantation? To answer this type of questions one needs to perform survival analysis. This paper focuses on the Kaplan-Meier method, the most popular method used for survival analysis. It makes it possible to calculate the incidence rate of events like recovery of renal function, myocardial infarction or death by using information from all subjects at risk for these events. It explains how the method works, how survival probabilities are calculated, survival data can be summarized and survival in groups can be compared using the logrank test for hypothesis testing. In addition, it provides some guidance regarding the presentation of survival plots. Finally, it discusses the limitations of the Kaplan-Meier method and refers to other methods that better serve additional purposes.
authors.affiliation: ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. k.j.jager@amc.uva.nl
authors.forename: Kitty J
authors.initials: KJ
authors.lastname: Jager
authors.forename: Paul C
authors.initials: PC
authors.lastname: van Dijk
authors.forename: Carmine
authors.initials: C
authors.lastname: Zoccali
authors.forename: Friedo W
authors.initials: FW
authors.lastname: Dekker
mesh_terms: D001699:Biometry; D003928:Diabetic Nephropathies / Q000401:mortality / Q000628:therapy; D006801:Humans; D053208:Kaplan-Meier Estimate; D007674:Kidney Diseases / Q000150:complications / Q000401:mortality / Q000628:therapy; D007676:Kidney Failure, Chronic / Q000209:etiology / Q000401:mortality / Q000628:therapy; D009398:Nephrology / Q000706:statistics & numerical data*; D011379:Prognosis; D017582:Renal Replacement Therapy / Q000706:statistics & numerical data