Linear and nonlinear variable selection in competing risks data

2018 ◽  
Vol 37 (13) ◽  
pp. 2134-2147
Author(s):  
Xiaowei Ren ◽  
Shanshan Li ◽  
Changyu Shen ◽  
Zhangsheng Yu
Keyword(s):  
Variable Selection ◽  
Competing Risks ◽  
Competing Risks Data ◽  
Linear And Nonlinear

scholarly journals Variable selection in subdistribution hazard frailty models with competing risks data

2014 ◽  
Vol 33 (26) ◽  
pp. 4590-4604 ◽  
Author(s):  
Il Do Ha ◽  
Minjung Lee ◽  
Seungyoung Oh ◽  
Jong-Hyeon Jeong ◽  
Richard Sylvester ◽  
...  
Keyword(s):  
Variable Selection ◽  
Competing Risks ◽  
Frailty Models ◽  
Subdistribution Hazard ◽  
Competing Risks Data

scholarly journals Penalized Variable Selection for Multi-center Competing Risks Data

2016 ◽  
Vol 9 (2) ◽  
pp. 379-405 ◽  
Author(s):  
Zhixuan Fu ◽  
Shuangge Ma ◽  
Haiqun Lin ◽  
Chirag R. Parikh ◽  
Bingqing Zhou
Keyword(s):  
Variable Selection ◽  
Competing Risks ◽  
Competing Risks Data ◽  
Selection For

scholarly journals Group and within-group variable selection for competing risks data

2017 ◽  
Vol 24 (3) ◽  
pp. 407-424 ◽  
Author(s):  
Kwang Woo Ahn ◽  
Anjishnu Banerjee ◽  
Natasha Sahr ◽  
Soyoung Kim
Keyword(s):  
Variable Selection ◽  
Competing Risks ◽  
Group Variable Selection ◽  
Competing Risks Data ◽  
Selection For ◽  
Group Variable

On correlation rank screening for ultra-high dimensional competing risks data

2021 ◽  
pp. 1-17
Author(s):  
Xiaolin Chen ◽  
Chenguang Li ◽  
Tao Zhang ◽  
Zhenlong Gao
Keyword(s):  
Competing Risks ◽  
High Dimensional ◽  
Competing Risks Data

scholarly journals Modeling semi‐competing risks data as a longitudinal bivariate process

Biometrics ◽  
2021 ◽  
Author(s):  
Daniel Nevo ◽  
Deborah Blacker ◽  
Eric B. Larson ◽  
Sebastien Haneuse
Keyword(s):  
Competing Risks ◽  
Competing Risks Data

scholarly journals Nonparametric estimation with recurrent competing risks data

2013 ◽  
Vol 20 (4) ◽  
pp. 514-537 ◽  
Author(s):  
Laura L. Taylor ◽  
Edsel A. Peña
Keyword(s):  
Competing Risks ◽  
Nonparametric Estimation ◽  
Competing Risks Data

A Practical Guide to Family Studies with Lifetime Data

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Thomas H. Scheike ◽  
Klaus Kähler Holst
Keyword(s):  
Competing Risks ◽  
Survival Data ◽  
Age Of Onset ◽  
Familial Aggregation ◽  
Threshold Model ◽  
Right Censoring ◽  
Annual Review ◽  
Publication Date ◽  
Lifetime Data ◽  
Competing Risks Data

Familial aggregation refers to the fact that a particular disease may be overrepresented in some families due to genetic or environmental factors. When studying such phenomena, it is clear that one important aspect is the age of onset of the disease in question, and in addition, the data will typically be right-censored. Therefore, one must apply lifetime data methods to quantify such dependence and to separate it into different sources using polygenic modeling. Another important point is that the occurrence of a particular disease can be prevented by death—that is, competing risks—and therefore, the familial aggregation should be studied in a model that allows for both death and the occurrence of the disease. We here demonstrate how polygenic modeling can be done for both survival data and competing risks data dealing with right-censoring. The competing risks modeling that we focus on is closely related to the liability threshold model. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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