Mixed-effects varying-coefficient model with skewed distribution coupled with cause-specific varying-coefficient hazard model with random-effects for longitudinal-competing risks data analysis

2015 ◽  
Vol 26 (3) ◽  
pp. 519-533 ◽  
Author(s):  
Tao Lu ◽  
Min Wang ◽  
Guangying Liu ◽  
Guang-Hui Dong ◽  
Feng Qian
Keyword(s):  
Data Analysis ◽  
Competing Risks ◽  
Random Effects ◽  
Hazard Model ◽  
Mixed Effects ◽  
Skewed Distribution ◽  
Varying Coefficient Model ◽  
Varying Coefficient ◽  
Competing Risks Data

scholarly journals A multilevel mixed effects varying coefficient model with multilevel predictors and random effects for modeling hospitalization risk in patients on dialysis

Biometrics ◽  
2020 ◽  
Vol 76 (3) ◽  
pp. 924-938
Author(s):  
Yihao Li ◽  
Danh V. Nguyen ◽  
Esra Kürüm ◽  
Connie M. Rhee ◽  
Yanjun Chen ◽  
...  
Keyword(s):  
Random Effects ◽  
Mixed Effects ◽  
Varying Coefficient Model ◽  
Varying Coefficient

scholarly journals Comparison of the marginal hazard model and the sub-distribution hazard model for competing risks under an assumed copula

2019 ◽  
Vol 29 (8) ◽  
pp. 2307-2327 ◽  
Author(s):  
Takeshi Emura ◽  
Jia-Han Shih ◽  
Il Do Ha ◽  
Ralf A Wilke
Keyword(s):  
Competing Risks ◽  
Hazard Function ◽  
Cox Regression ◽  
Cox Model ◽  
Hazard Model ◽  
Numerical Comparison ◽  
Hazard Functions ◽  
Different Types ◽  
Competing Risks Data ◽  
The Relationship

For the analysis of competing risks data, three different types of hazard functions have been considered in the literature, namely the cause-specific hazard, the sub-distribution hazard, and the marginal hazard function. Accordingly, medical researchers can fit three different types of the Cox model to estimate the effect of covariates on each of the hazard function. While the relationship between the cause-specific hazard and the sub-distribution hazard has been extensively studied, the relationship to the marginal hazard function has not yet been analyzed due to the difficulties related to non-identifiability. In this paper, we adopt an assumed copula model to deal with the model identifiability issue, making it possible to establish a relationship between the sub-distribution hazard and the marginal hazard function. We then compare the two methods of fitting the Cox model to competing risks data. We also extend our comparative analysis to clustered competing risks data that are frequently used in medical studies. To facilitate the numerical comparison, we implement the computing algorithm for marginal Cox regression with clustered competing risks data in the R joint.Cox package and check its performance via simulations. For illustration, we analyze two survival datasets from lung cancer and bladder cancer patients.

scholarly journals Semi-Competing Risks Data Analysis

2016 ◽  
Vol 9 (3) ◽  
pp. 322-331 ◽  
Author(s):  
Sebastien Haneuse ◽  
Kyu Ha Lee
Keyword(s):  
Data Analysis ◽  
Competing Risks ◽  
Competing Risks Data
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