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Yena Jeon 1 Article
Competing Risk Model in Survival Analysis
Yena Jeon, Won Kee Lee
Cardiovasc Prev Pharmacother. 2020;2(3):77-84.   Published online July 31, 2020
DOI: https://doi.org/10.36011/cpp.2020.2.e11
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Abstract PDF
Survival analysis is primarily used to identify the time-to-event for events of interest. However, there subjects may undergo several outcomes; competing risks occur when other events may affect the incidence rate of the event of interest. In the presence of competing risks, traditional survival analysis such as the Kaplan-Meier method or the Cox proportional hazard regression introduces biases into the estimation of survival probability. In this review, we discuss several methods that can be used to consider competing risks in survival analysis: the cumulative incidence function, the cause-specific hazard function, and Fine and Gray's Subdistribution hazard function. We also provide a guide for conducting competing risk analysis using SAS with the bone marrow transplantation dataset presented by Klein and Moeschberger (1997).

Citations

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  • Standard Survival Analysis Can Overestimate Incidence and Risk Factors of Event of Interest in a Prospective Cohort Study with Considerable Attrition: The Case of a Suicide High-Risk Cohort
    Min Ji Kim, Maengseok Noh, Jieun Yoo, Seung Yeon Jeon, Jungjoon Moon, Seong Jin Cho, Sang Yeol Lee, Se-Hoon Shim, Shin Gyeom Kim, Won Sub Kang, Min-Hyuk Kim, Christopher Hyung Keun Park, Daun Shin, Sang Jin Rhee, Jeong Hun Yang, Yong-Min Ahn, Weon-Young L
    SSRN Electronic Journal .2022;[Epub]     CrossRef

CPP : Cardiovascular Prevention and Pharmacotherapy