A semiparametric marginal mixture cure model for clustered survival data

2012 ◽  
Vol 32 (14) ◽  
pp. 2364-2373 ◽  
Author(s):  
Yi Niu ◽  
Yingwei Peng
Keyword(s):  
Survival Data ◽  
Cure Model ◽  
Mixture Cure Model ◽  
Clustered Survival Data

scholarly journals Application of Modified Generalized–Gamma Mixture Cure Model in the Analysis of Ovarian Cancer Data

2021 ◽  
Vol 2123 (1) ◽  
pp. 012041
Author(s):  
Serifat A. Folorunso ◽  
Timothy A.O. Oluwasola ◽  
Angela U. Chukwu ◽  
Akintunde A. Odukogbe
Keyword(s):  
Ovarian Cancer ◽  
Survival Data ◽  
Information Criterion ◽  
Cure Model ◽  
Modeling And Analysis ◽  
Cancer Data ◽  
Mixture Cure Model ◽  
Generalized Gamma ◽  
Ovarian Cancer Data ◽  
Cure Models

Abstract The modeling and analysis of lifetime for terminal diseases such as cancer is a significant aspect of statistical work. This study considered data from thirty-seven women diagnosed with Ovarian Cancer and hospitalized for care at theDepartment of Obstetrics and Gynecology, University of Ibadan, Nigeria. Focus was on the application of a parametric mixture cure model that can handle skewness associated with survival data – a modified generalized-gamma mixture cure model (MGGMCM). The effectiveness of MGGMCM was compared with existing parametric mixture cure models using Akaike Information Criterion, median time-to-cure and variance of the cure rate. It was observed that the MGGMCM is an improved parametric model for the mixture cure model.

scholarly journals Cure Models with Exponentiated Weibull Exponential Distribution for the Analysis of Melanoma Patients

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1926
Author(s):  
Mohamed Elamin Abdallah Mohamed Elamin Omer ◽  
Mohd Rizam Abu Bakar ◽  
Mohd Bakri Adam ◽  
Mohd Shafie Mustafa
Keyword(s):  
Survival Data ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Cure Rate ◽  
Cure Rate Models ◽  
Cure Model ◽  
Mixture Cure Model ◽  
Cure Models ◽  
Exponentiated Weibull ◽  
Melanoma Patients

In the survival data analysis, commonly, it is presumed that all study subjects will eventually have the event of concern. Nonetheless, it tends to be unequivocally expected that a fraction of these subjects will never expose to the event of interest. The cure rate models are usually used to model this type of data. In this paper, we introduced a maximum likelihood estimates analysis for the four-parameter exponentiated Weibull exponential (EWE) distribution in the existence of cured subjects, censored observations, and predictors. Aiming to include the fraction of unsusceptible (cured) individuals in the analysis, a mixture cure model, and two non-mixture cure models—bounded cumulative hazard model, and geometric non-mixture model with EWE distribution—are proposed. The mixture cure model provides a better fit to real data from a Melanoma clinical trial compared to the other two non-mixture cure models.

Change point detection in Cox proportional hazards mixture cure model

2020 ◽  
pp. 096228022095911
Author(s):  
Bing Wang ◽  
Jialiang Li ◽  
Xiaoguang Wang
Keyword(s):  
Survival Data ◽  
Change Point ◽  
Proportional Hazards ◽  
Test Procedure ◽  
Cox Proportional Hazards ◽  
Change Point Detection ◽  
Parameter Estimates ◽  
Finite Sample ◽  
Cure Model ◽  
Mixture Cure Model

The mixture cure model has been widely applied to survival data in which a fraction of the observations never experience the event of interest, despite long-term follow-up. In this paper, we study the Cox proportional hazards mixture cure model where the covariate effects on the distribution of uncured subjects’ failure time may jump when a covariate exceeds a change point. The nonparametric maximum likelihood estimation is used to obtain the semiparametric estimates. We employ a two-step computational procedure involving the Expectation-Maximization algorithm to implement the estimation. The consistency, convergence rate and asymptotic distributions of the estimators are carefully established under technical conditions and we show that the change point estimator is n consistency. The m out of n bootstrap and the Louis algorithm are used to obtain the standard errors of the estimated change point and other regression parameter estimates, respectively. We also contribute a test procedure to check the existence of the change point. The finite sample performance of the proposed method is demonstrated via simulation studies and real data examples.

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.

Sign in / Sign up

Export Citation Format

Share Document