We develop a simulation procedure to simulate the semicompeting risk survival data. In addition, we introduce an EM algorithm and a B–spline based estimation procedure to evaluate and implement Xu et al. (2010)’s nonparametric likelihood es-timation approach. The simulation procedure provides a route to simulate samples from the likelihood introduced in Xu et al. (2010)’s. Further, the EM algorithm and the B–spline methods stabilize the estimation and gives accurate estimation results. We illustrate the simulation and the estimation procedure with simluation examples and real data analysis
Extensions of the Cox proportional hazards model for survival data are studied where allowance is ma...
The Cox regression, a semi-parametric method of survival analysis, is extremely popular in biomedica...
Semi-competing risks refers to the survival analysis setting where the occurrence of a non-terminal ...
We develop a simulation procedure to simulate the semicompeting risk survival data. In addition, we ...
For analyzing multiple events data, the illness death model is often used to investigate the covaria...
Frailty mixture survival models are statistical models which allow for a cured fraction and frailty....
Generating survival data with a clustered and multi-state structure is useful to study multi-state m...
Generating survival data with a clustered and multi-state structure is useful to study finite sample...
BACKGROUND In survival analysis a large literature using frailty models, or models with unobserved h...
A marginal likelihood approach is proposed for estimating the parameters in a frailty model using cl...
Proportional hazards models are among the most popular regression models in survival analysis. Multi...
Semicompeting risks data are a mixture of competing risks data and progressive state data. This type...
In many instances, a subject can experience both a nonterminal and terminal event where the terminal...
The shared frailty models allow for unobserved heterogeneity or for statistical dependence between o...
Semi-competing risks are a variation of competing risks where a terminal event censors a non-termina...
Extensions of the Cox proportional hazards model for survival data are studied where allowance is ma...
The Cox regression, a semi-parametric method of survival analysis, is extremely popular in biomedica...
Semi-competing risks refers to the survival analysis setting where the occurrence of a non-terminal ...
We develop a simulation procedure to simulate the semicompeting risk survival data. In addition, we ...
For analyzing multiple events data, the illness death model is often used to investigate the covaria...
Frailty mixture survival models are statistical models which allow for a cured fraction and frailty....
Generating survival data with a clustered and multi-state structure is useful to study multi-state m...
Generating survival data with a clustered and multi-state structure is useful to study finite sample...
BACKGROUND In survival analysis a large literature using frailty models, or models with unobserved h...
A marginal likelihood approach is proposed for estimating the parameters in a frailty model using cl...
Proportional hazards models are among the most popular regression models in survival analysis. Multi...
Semicompeting risks data are a mixture of competing risks data and progressive state data. This type...
In many instances, a subject can experience both a nonterminal and terminal event where the terminal...
The shared frailty models allow for unobserved heterogeneity or for statistical dependence between o...
Semi-competing risks are a variation of competing risks where a terminal event censors a non-termina...
Extensions of the Cox proportional hazards model for survival data are studied where allowance is ma...
The Cox regression, a semi-parametric method of survival analysis, is extremely popular in biomedica...
Semi-competing risks refers to the survival analysis setting where the occurrence of a non-terminal ...