Rank tests for clustered survival data when dependent subunits are randomized

2005 ◽  
Vol 25 (3) ◽  
pp. 361-373 ◽  
Author(s):  
Jong-Hyeon Jeong ◽  
Sin-Ho Jung
Keyword(s):  
Survival Data ◽  
Rank Tests ◽  
Clustered Survival Data

Flexible parametric copula modeling approaches for clustered survival data

2021 ◽  
Author(s):  
Sookhee Kwon ◽  
Il Do Ha ◽  
Jia‐Han Shih ◽  
Takeshi Emura
Keyword(s):  
Survival Data ◽  
Modeling Approaches ◽  
Copula Modeling ◽  
Clustered Survival Data ◽  
Parametric Copula

scholarly journals Additive Hazard Regression for the Analysis of Clustered Survival Data from Case-Cohort Studies

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
June Liu ◽  
Yi Zhang
Keyword(s):  
Cohort Studies ◽  
Survival Data ◽  
Failure Time ◽  
Weight Functions ◽  
Disease Rate ◽  
Failure Time Data ◽  
Finite Sample ◽  
Time Data ◽  
Cohort Design ◽  
Clustered Survival Data

The case-cohort design is an effective and economical method in large cohort studies, especially when the disease rate is low. Case-cohort design in most of the existing literature is mainly used to analyze the univariate failure time data. But in practice, multivariate failure time data are commonly encountered in biomedical research. In this paper, we will propose methods based on estimating equation method for case-cohort designs for clustered survival data. By introducing the event failure rate, three different weight functions are constructed. Then, three estimating equations and parameter estimators are presented. Furthermore, consistency and asymptotic normality of the proposed estimators are established. Finally, the simulation results show that the proposed estimation procedure has reasonable finite sample behaviors.

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