title: [An introduction to competing risks analysis].
abstract: The need to develop treatments and/or programs specific to a disease requires the analysis of outcomes to be specific to that disease. Such endpoints as heart failure, death due to a specific disease, or control of local disease in cancer may become impossible to observe due to a prior occurrence of a different type of event (such as death from another cause). The event which hinders or changes the possibility of observing the event of interest is called a competing risk. The usual techniques for time-to-event analysis applied in the presence of competing risks give biased or uninterpretable results. The estimation of the probability of the event therefore needs to be calculated using specific techniques such as the cumulative incidence function introduced by Kalbfleisch and Prentice. The model introduced by Fine and Gray can be applied to test a covariate when competing risks are present. Using specific techniques for the analysis of competing risks will ensure that the results are unbiased and can be correctly interpreted. Full English text available from: www.revespcardiol.org.
authors.affiliation: Department of Biostatistics, Ontario Cancer Institute/Princess Margaret Hospital, University Health Network, Dalla Lana School of Public Health, University of Toronto, Ontario, Canada. Pintilie@uhnres.utoronto.ca
authors.forename: Melania
authors.initials: M
authors.lastname: Pintilie
mesh_terms: D000465:Algorithms; D023381:Endpoint Determination; D006331:Heart Diseases / Q000175:diagnosis / Q000453:epidemiology; D006801:Humans; D053208:Kaplan-Meier Estimate; D015233:Models, Statistical; D011336:Probability; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000706:statistics & numerical data*; D012984:Software; D016896:Treatment Outcome