An overview on standard statistical methods for assessing exposure-outcome link in survival analysis (part II): The Kaplan-Meier analysis and the Cox regression method
Aging Clinical and Experimental Research , Volume 24 - Issue 3 p. 203- 206
The Kaplan-Meier and the Cox regression methods are the most used statistical techniques for performing "time to event analysis" in epidemiological and clinical research. The Kaplan-Meier analysis allows to build up one or more survival curves describing the occurrence of the outcome of interest over time according to the presence/absence of one or more exposures. The Cox regression method models the relationship between a specific exposure (either a continuous one like age, and systolic blood pressure or a categorical one like diabetes, degree of obesity, etc.) and the occurrence of a given outcome taking into account multiple confounders and/or predictors.
|Cox regression analysis, Kaplan-Meier analysis, Survival analysis|
|Aging Clinical and Experimental Research|
|Organisation||Department of Internal Medicine|
Elhafeez, S.A, Torino, C, D'Arrigo, G, Bolignano, D, Provenzano, F, Mattace Raso, F.U.S, … Tripepi, G.L. (2012). An overview on standard statistical methods for assessing exposure-outcome link in survival analysis (part II): The Kaplan-Meier analysis and the Cox regression method. Aging Clinical and Experimental Research (Vol. 24, pp. 203–206). Retrieved from http://hdl.handle.net/1765/91030