TY - JOUR
T1 - Applying competing risks regression models
T2 - An overview
AU - Haller, Bernhard
AU - Schmidt, Georg
AU - Ulm, Kurt
N1 - Funding Information:
Acknowledgments The cohort study conducted in the Klinikum rechts der Isar and in the German Heart Centre Munich was supported by the Bundesministerium für Bildung, Wissenschaft, Forschung und Tech-nologie (13N/7073/7), the Kommission für Klinische Forschung, and the Deutsche Forschungsgemeinschaft (SFB 386).
PY - 2012/12
Y1 - 2012/12
N2 - In many clinical research applications the time to occurrence of one event of interest, that may be obscured by another-so called competing-event, is investigated. Specific interventions can only have an effect on the endpoint they address or research questions might focus on risk factors for a certain outcome. Different approaches for the analysis of time-to-event data in the presence of competing risks were introduced in the last decades including some new methodologies, which are not yet frequently used in the analysis of competing risks data. Cause-specific hazard regression, subdistribution hazard regression, mixture models, vertical modelling and the analysis of time-to-event data based on pseudo-observations are described in this article and are applied to a dataset of a cohort study intended to establish risk stratification for cardiac death after myocardial infarction. Data analysts are encouraged to use the appropriate methods for their specific research questions by comparing different regression approaches in the competing risks setting regarding assumptions, methodology and interpretation of the results. Notes on application of the mentioned methods using the statistical software R are presented and extensions to the presented standard methods proposed in statistical literature are mentioned.
AB - In many clinical research applications the time to occurrence of one event of interest, that may be obscured by another-so called competing-event, is investigated. Specific interventions can only have an effect on the endpoint they address or research questions might focus on risk factors for a certain outcome. Different approaches for the analysis of time-to-event data in the presence of competing risks were introduced in the last decades including some new methodologies, which are not yet frequently used in the analysis of competing risks data. Cause-specific hazard regression, subdistribution hazard regression, mixture models, vertical modelling and the analysis of time-to-event data based on pseudo-observations are described in this article and are applied to a dataset of a cohort study intended to establish risk stratification for cardiac death after myocardial infarction. Data analysts are encouraged to use the appropriate methods for their specific research questions by comparing different regression approaches in the competing risks setting regarding assumptions, methodology and interpretation of the results. Notes on application of the mentioned methods using the statistical software R are presented and extensions to the presented standard methods proposed in statistical literature are mentioned.
KW - Cause-specific hazard
KW - Competing risks
KW - Mixture model
KW - Pseudo-observation approach
KW - Subdistribution hazard
KW - Vertical modelling
UR - http://www.scopus.com/inward/record.url?scp=84871350344&partnerID=8YFLogxK
U2 - 10.1007/s10985-012-9230-8
DO - 10.1007/s10985-012-9230-8
M3 - Article
C2 - 23010807
AN - SCOPUS:84871350344
SN - 1380-7870
VL - 19
SP - 33
EP - 58
JO - Lifetime Data Analysis
JF - Lifetime Data Analysis
IS - 1
ER -