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.

The strong prognostic value of KELIM, a model-based parameter from CA 125 kinetics in ovarian cancer: Data from CALYPSO trial (a GINECO-GCIG study)

2013 ◽  
Vol 130 (2) ◽  
pp. 289-294 ◽  
Author(s):  
Benoit You ◽  
Olivier Colomban ◽  
Mark Heywood ◽  
Chee Lee ◽  
Margaret Davy ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Prognostic Value ◽  
Ca 125 ◽  
Cancer Data ◽  
Model Based ◽  
Ovarian Cancer Data

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 Method of Contraception and Risk of Ovarian Cancer Data

Data in Brief ◽  
2021 ◽  
pp. 107469
Author(s):  
Jacqueline Chesang ◽  
Ann Richardson ◽  
John Potter ◽  
Mary Sneyd ◽  
Pat Coope
Keyword(s):  
Ovarian Cancer ◽  
Cancer Data ◽  
Ovarian Cancer Data

Method of Contraception and Risk of Ovarian Cancer Data

2021 ◽  
Author(s):  
Jacqueline Chesang ◽  
Ann Richardson ◽  
John Potter ◽  
Mary Sneyd ◽  
Pat Coope
Keyword(s):  
Ovarian Cancer ◽  
Cancer Data ◽  
Ovarian Cancer Data

scholarly journals Building a Lung and Ovarian Cancer Data Warehouse

2020 ◽  
Vol 26 (4) ◽  
pp. 303-310
Author(s):  
Canan Eren Atay ◽  
Georgia Garani
Keyword(s):  
Ovarian Cancer ◽  
Data Warehouse ◽  
Cancer Data ◽  
Ovarian Cancer Data
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