Mixture cure model with random effects for clustered interval-censored survival data

2011 ◽  
Vol 30 (9) ◽  
pp. 995-1006 ◽  
Author(s):  
Liming Xiang ◽  
Xiangmei Ma ◽  
Kelvin K. W. Yau
Keyword(s):  
Random Effects ◽  
Survival Data ◽  
Cure Model ◽  
Censored Survival Data ◽  
Mixture Cure Model ◽  
Interval Censored

scholarly journals Diagnostic checks in mixture cure models with interval-censoring

2016 ◽  
Vol 27 (7) ◽  
pp. 2114-2131 ◽  
Author(s):  
Sylvie Scolas ◽  
Catherine Legrand ◽  
Abderrahim Oulhaj ◽  
Anouar El Ghouch
Keyword(s):  
Survival Data ◽  
Goodness Of Fit ◽  
Real Data ◽  
Interval Censoring ◽  
Data Set ◽  
Survival Distribution ◽  
Cure Model ◽  
Censored Survival Data ◽  
Mixture Cure Model ◽  
Interval Censored

Models for interval-censored survival data presenting a fraction of “cure” or “immune” patients have recently been proposed in the literature, particularly extending the mixture cure model to interval-censored data. However, little is known about the goodness-of-fit of such models. In a mixture cure model, the survival distribution of the entire population is improper and expressed in terms of the survival distribution of uncured individuals, i.e. the latency part of the model, and the probability to experience the event of interest, i.e. the incidence part. To validate a mixture cure model, assumptions made on both parts need to be checked, i.e. the survival distribution of uncured individuals, the link function used in the latency and the linearity of the covariates used in the both parts of the model. In this work, we investigate the Cox-Snell and deviance residuals and show how they can be adapted and used to perform diagnostics checks when all subjects are right- or interval-censored and some subjects are cured with unknown cure status. A large simulation study investigates the ability of these residuals to detect a departure from the assumptions of the mixture model. Developed techniques are applied to a real data set about Alzheimer’s disease.

scholarly journals Variable selection in a flexible parametric mixture cure model with interval‐censored data

2015 ◽  
Vol 35 (7) ◽  
pp. 1210-1225 ◽  
Author(s):  
Sylvie Scolas ◽  
Anouar El Ghouch ◽  
Catherine Legrand ◽  
Abderrahim Oulhaj
Keyword(s):  
Variable Selection ◽  
Censored Data ◽  
Interval Censored Data ◽  
Cure Model ◽  
Mixture Cure Model ◽  
Interval Censored
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