scholarly journals A Note on Including Time-dependent Covariate in Regression Model for Competing Risks Data

2005 ◽  
Vol 47 (6) ◽  
pp. 807-814 ◽  
Author(s):  
A. Latouche ◽  
R. Porcher ◽  
S. Chevret
Keyword(s):  
Regression Model ◽  
Competing Risks ◽  
Time Dependent ◽  
Competing Risks Data

Analysis of masked competing risks data with cause and time dependent masking mechanism

2020 ◽  
Vol 90 (12) ◽  
pp. 2256-2266
Author(s):  
Hasan Misaei ◽  
Samaneh Eftekhari Mahabadi ◽  
Firoozeh Haghighi
Keyword(s):  
Competing Risks ◽  
Time Dependent ◽  
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

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 Primary tumor surgery improves survival in non-metastatic primary urethral carcinoma patients: a large population-based investigation

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jie Wu ◽  
Yu-Chen Wang ◽  
Wen-Jie Luo ◽  
Bo-Dai ◽  
Ding-Wei Ye ◽  
...  
Keyword(s):  
Regression Model ◽  
Competing Risks ◽  
Primary Tumor ◽  
Cox Regression ◽  
Radical Surgery ◽  
Large Population ◽  
Local Therapy ◽  
Primary Site ◽  
Urethral Carcinoma ◽  
The Impact

Abstract Background Primary urethral carcinoma (PUC) is a rare genitourinary malignancy with a relatively poor prognosis. The aim of this study was to examine the impact of surgery on survival of patients diagnosed with PUC. Methods A total of 1544 PUC patients diagnosed between 2004 and 2016 were identified based on the SEER database. The Kaplan-Meier estimate and the Fine and Gray competing risks analysis were performed to assess overall survival (OS) and cancer-specific mortality (CSM). The multivariate Cox regression model and competing risks regression model were used to identify independent risk factors of OS and cancer-specific survival (CSS). Results The 5-yr OS was significantly better in patients who received either local therapy (39.8%) or radical surgery (44.7%) compared to patients receiving no surgery of the primary site (21.5%) (p < 0.001). Both local therapy and radical surgery were each independently associated with decreased CSM, with predicted 5-yr cumulative incidence of 45.4 and 43.3%, respectively, compared to 64.7% for patients receiving no surgery of the primary site (p < 0.001). Multivariate analyses demonstrated that primary site surgery was independently associated with better OS (local therapy, p = 0.037; radical surgery, p < 0.001) and decreased CSM (p = 0.003). Similar results were noted regardless of age, sex, T stage, N stage, and AJCC prognostic groups based on subgroup analysis. However, patients with M1 disease who underwent primary site surgery did not exhibit any survival benefit. Conclusion Surgery for the primary tumor conferred a survival advantage in non-metastatic PUC patients.

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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