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
2014 ◽  
Vol 33 (26) ◽  
pp. 4590-4604 ◽  
Author(s):  
Il Do Ha ◽  
Minjung Lee ◽  
Seungyoung Oh ◽  
Jong-Hyeon Jeong ◽  
Richard Sylvester ◽  
...  

2016 ◽  
Vol 9 (2) ◽  
pp. 379-405 ◽  
Author(s):  
Zhixuan Fu ◽  
Shuangge Ma ◽  
Haiqun Lin ◽  
Chirag R. Parikh ◽  
Bingqing Zhou

2017 ◽  
Vol 24 (3) ◽  
pp. 407-424 ◽  
Author(s):  
Kwang Woo Ahn ◽  
Anjishnu Banerjee ◽  
Natasha Sahr ◽  
Soyoung Kim

Author(s):  
Xiaolin Chen ◽  
Chenguang Li ◽  
Tao Zhang ◽  
Zhenlong Gao

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

2013 ◽  
Vol 20 (4) ◽  
pp. 514-537 ◽  
Author(s):  
Laura L. Taylor ◽  
Edsel A. Peña

Author(s):  
Thomas H. Scheike ◽  
Klaus Kähler Holst

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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