Nomogram for survival analysis in the presence of competing risks
Zhang, Zhongheng ; Geskus, Ronald B ; Kattan, Michael W ; Zhang, Haoyang LU
and Liu, Tongyu
(2017)
In Annals of Translational Medicine
5(20).
p.1-6
- Abstract
Clinical research usually involves time-to-event survival analysis, in which the presence of a competing event is prevalent. It is acceptable to use the conventional Cox proportional hazard regression to model cause-specific hazard. However, this cause-specific hazard cannot directly translate to the cumulative incidence function, and the latter is usually clinically relevant. The subdistribution hazard regression directly quantifies the impact of covariates on the cumulative incidence. When estimating the subdistribution hazard, subjects experiencing competing event continue to contribute to the risk set, and censoring weights are assigned to them after the competing event time. The weights are the conditional probability that a... (More)
Clinical research usually involves time-to-event survival analysis, in which the presence of a competing event is prevalent. It is acceptable to use the conventional Cox proportional hazard regression to model cause-specific hazard. However, this cause-specific hazard cannot directly translate to the cumulative incidence function, and the latter is usually clinically relevant. The subdistribution hazard regression directly quantifies the impact of covariates on the cumulative incidence. When estimating the subdistribution hazard, subjects experiencing competing event continue to contribute to the risk set, and censoring weights are assigned to them after the competing event time. The weights are the conditional probability that a subject remains uncensored, and can be modelled to depend on the covariates of a subject. The first option to perform regression on the subdistribution hazard was the crr() function in the cmprsk package. However, it is not straightforward to draw a nomogram, which is a user-friendly tool for risk prediction, with the crr() function. To overcome this problem, we show an alternative method to use a nomogram function based on result of subdistribution hazard modeling.
(Less)
- author
- Zhang, Zhongheng
; Geskus, Ronald B
; Kattan, Michael W
; Zhang, Haoyang
LU
and Liu, Tongyu
- publishing date
- 2017-10
- type
- Contribution to journal
- publication status
- published
- in
- Annals of Translational Medicine
- volume
- 5
- issue
- 20
- article number
- 403
- pages
- 1 - 6
- publisher
- AME Publishing Company
- external identifiers
-
- scopus:85031897490
- pmid:29152503
- ISSN
- 2305-5839
- DOI
- 10.21037/atm.2017.07.27
- language
- English
- LU publication?
- no
- id
- 8d901fb0-6f18-402e-84cc-e66cdce52cb4
- date added to LUP
- 2024-02-05 16:18:01
- date last changed
- 2024-10-09 04:26:02
@article{8d901fb0-6f18-402e-84cc-e66cdce52cb4,
abstract = {{<p>Clinical research usually involves time-to-event survival analysis, in which the presence of a competing event is prevalent. It is acceptable to use the conventional Cox proportional hazard regression to model cause-specific hazard. However, this cause-specific hazard cannot directly translate to the cumulative incidence function, and the latter is usually clinically relevant. The subdistribution hazard regression directly quantifies the impact of covariates on the cumulative incidence. When estimating the subdistribution hazard, subjects experiencing competing event continue to contribute to the risk set, and censoring weights are assigned to them after the competing event time. The weights are the conditional probability that a subject remains uncensored, and can be modelled to depend on the covariates of a subject. The first option to perform regression on the subdistribution hazard was the crr() function in the cmprsk package. However, it is not straightforward to draw a nomogram, which is a user-friendly tool for risk prediction, with the crr() function. To overcome this problem, we show an alternative method to use a nomogram function based on result of subdistribution hazard modeling.</p>}},
author = {{Zhang, Zhongheng and Geskus, Ronald B and Kattan, Michael W and Zhang, Haoyang and Liu, Tongyu}},
issn = {{2305-5839}},
language = {{eng}},
number = {{20}},
pages = {{1--6}},
publisher = {{AME Publishing Company}},
series = {{Annals of Translational Medicine}},
title = {{Nomogram for survival analysis in the presence of competing risks}},
url = {{http://dx.doi.org/10.21037/atm.2017.07.27}},
doi = {{10.21037/atm.2017.07.27}},
volume = {{5}},
year = {{2017}},
}