title: [Competing risks in clinical research].
abstract: In clinical research, study populations are often followed up until the occurrence of a certain outcome, such as breast cancer or death. Survival analysis, used to analyse the time-to-event data, is usually focused on one particular event; however, study participants may experience other events that prevent the event of interest from occurring. This is a situation of competing risks. An example is the risk of death in a study investigating breast cancer recurrence, as women who have died without having recurrent disease are no longer at risk of this recurrent disease. Competing risks can lead to biased results if not taken into account. Competing risks are important in studies including patients with a high risk of varying clinical outcomes, and in those studies with a long follow-up period. If competing risks are present, the competing outcomes can be combined into one outcome measure or should be taken into account in adapted survival analysis.
authors.affiliation: Universitair Medisch Centrum Utrecht, Afd. Vasculaire Geneeskunde, Utrecht, the Netherlands.
authors.forename: Rob C M
authors.initials: RC
authors.lastname: van Kruijsdijk
authors.forename: Marinus J C
authors.initials: MJ
authors.lastname: Eijkemans
authors.forename: Frank L J
authors.initials: FL
authors.lastname: Visseren
mesh_terms: D035843:Biomedical Research / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D018572:Disease-Free Survival; D005500:Follow-Up Studies; D006801:Humans; D015233:Models, Statistical; D011336:Probability; D012008:Recurrence; D012044:Regression Analysis; D012306:Risk; D012307:Risk Factors; D016019:Survival Analysis; D013997:Time Factors; D016896:Treatment Outcome