%0 Journal Article %1 Wolbers:2014:Eur-Heart-J:24711436 %A Wolbers, M %A Koller, M T %A Stel, V S %A Schaer, B %A Jager, K J %A Leffondré, K %A Heinze, G %D 2014 %J Eur Heart J %K CompetingRisks SurvivalAnalysis cardiology statistics %N 42 %P 2936-2941 %R 10.1093/eurheartj/ehu131 %T Competing risks analyses: objectives and approaches %U https://www.ncbi.nlm.nih.gov/pubmed/24711436 %V 35 %X Studies in cardiology often record the time to multiple disease events such as death, myocardial infarction, or hospitalization. Competing risks methods allow for the analysis of the time to the first observed event and the type of the first event. They are also relevant if the time to a specific event is of primary interest but competing events may preclude its occurrence or greatly alter the chances to observe it. We give a non-technical overview of competing risks concepts for descriptive and regression analyses. For descriptive statistics, the cumulative incidence function is the most important tool. For regression modelling, we introduce regression models for the cumulative incidence function and the cause-specific hazard function, respectively. We stress the importance of choosing statistical methods that are appropriate if competing risks are present. We also clarify the role of competing risks for the analysis of composite endpoints.

@article{Wolbers:2014:Eur-Heart-J:24711436, abstract = {Studies in cardiology often record the time to multiple disease events such as death, myocardial infarction, or hospitalization. Competing risks methods allow for the analysis of the time to the first observed event and the type of the first event. They are also relevant if the time to a specific event is of primary interest but competing events may preclude its occurrence or greatly alter the chances to observe it. We give a non-technical overview of competing risks concepts for descriptive and regression analyses. For descriptive statistics, the cumulative incidence function is the most important tool. For regression modelling, we introduce regression models for the cumulative incidence function and the cause-specific hazard function, respectively. We stress the importance of choosing statistical methods that are appropriate if competing risks are present. We also clarify the role of competing risks for the analysis of composite endpoints. }, added-at = {2019-10-27T09:35:52.000+0100}, author = {Wolbers, M and Koller, M T and Stel, V S and Schaer, B and Jager, K J and Leffondr{\'e}, K and Heinze, G}, biburl = {https://www.bibsonomy.org/bibtex/229e8ba90435cab6ddf1e7b0635a046c2/jkd}, description = {Competing risks analyses: objectives and approaches. - PubMed - NCBI}, doi = {10.1093/eurheartj/ehu131}, interhash = {d7011b49504a3966eca3492a450352d1}, intrahash = {29e8ba90435cab6ddf1e7b0635a046c2}, journal = {Eur Heart J}, keywords = {CompetingRisks SurvivalAnalysis cardiology statistics}, month = nov, number = 42, pages = {2936-2941}, pmid = {24711436}, timestamp = {2019-10-27T09:35:52.000+0100}, title = {Competing risks analyses: objectives and approaches}, url = {https://www.ncbi.nlm.nih.gov/pubmed/24711436}, volume = 35, year = 2014 }