A nonparametric vertical model : an application to discrete time competing risks data with missing failure causes

2020 ◽  
Vol 54 (2) ◽  
pp. 231-241
Keyword(s):  
Competing Risks ◽  
Discrete Time ◽  
Competing Risks Data

On the analysis of discrete time competing risks data

Biometrics ◽  
2018 ◽  
Vol 74 (4) ◽  
pp. 1468-1481 ◽  
Author(s):  
Minjung Lee ◽  
Eric J. Feuer ◽  
Jason P. Fine
Keyword(s):  
Competing Risks ◽  
Discrete Time ◽  
Competing Risks Data

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

On a progressively censored competing risks data from Gompertz distribution

2021 ◽  
pp. 1-22
Author(s):  
Chandrakant Lodhi ◽  
Yogesh Mani Tripathi ◽  
Ritwik Bhattacharya
Keyword(s):  
Competing Risks ◽  
Gompertz Distribution ◽  
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

Semiparametric copula-based regression modeling of semi-competing risks data

2021 ◽  
pp. 1-16
Author(s):  
Hong Zhu ◽  
Yu Lan ◽  
Jing Ning ◽  
Yu Shen
Keyword(s):  
Competing Risks ◽  
Regression Modeling ◽  
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.

Estimation for exponential distribution under multiply progressive censored competing risks data

2021 ◽  
Vol 32 (6) ◽  
pp. 1305-1315
Author(s):  
Kyeongjun Lee ◽  
Hanse Kang ◽  
Soyun Jeong ◽  
Junki Hong
Keyword(s):  
Exponential Distribution ◽  
Competing Risks ◽  
Competing Risks Data

Analysis of dependent left-truncated and right-censored competing risks data with partially observed failure causes

2021 ◽  
Author(s):  
Liang Wang ◽  
Yogesh Mani Tripathi ◽  
Sanku Dey ◽  
Chunfang Zhang ◽  
Ke Wu
Keyword(s):  
Competing Risks ◽  
Partially Observed ◽  
Competing Risks Data

Exact inference for joint Type-I hybrid censoring model with exponential competing risks data

2017 ◽  
Vol 33 (3) ◽  
pp. 645-658 ◽  
Author(s):  
Song Mao ◽  
Yi-min Shi ◽  
Xiao-lin Wang
Keyword(s):  
Competing Risks ◽  
Type I ◽  
Exact Inference ◽  
Hybrid Censoring ◽  
Competing Risks Data
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