Cure fraction estimation from the mixture cure models for grouped survival data

2004 ◽  
Vol 23 (11) ◽  
pp. 1733-1747 ◽  
Author(s):  
Binbing Yu ◽  
Ram C. Tiwari ◽  
Kathleen A. Cronin ◽  
Eric J. Feuer
Keyword(s):  
Survival Data ◽  
Cure Fraction ◽  
Cure Models

On a Class of Transmuted Distributions to Model Survival Data with a Cure Fraction

2021 ◽  
Vol 73 (2) ◽  
pp. 106-126
Author(s):  
G. Asha ◽  
C. S. Soorya
Keyword(s):  
Survival Data ◽  
Likelihood Function ◽  
Real Life ◽  
Estimation Of Parameters ◽  
Cure Rate ◽  
Data Set ◽  
Cure Rate Models ◽  
Subject Classifications ◽  
Cure Fraction ◽  
Cure Models

Modelling time to event data, when there is always a proportion of the individuals, commonly referred to as immunes who do not experience the event of interest, is of importance in many biomedical studies. Improper distributions are used to model these situations and they are generally referred to as cure rate models. In the literature, two main families of cure rate models have been proposed, namely the mixture cure models and promotion time cure models. Here we propose a new model by extending the mixture model via a generating function by considering a shifted Bernoulli distribution. This gives rise to a new class of popular distributions called the transmuted class of distributions to model survival data with a cure fraction. The properties of the proposed model are investigated and parameters estimated. The Bayesian approach to the estimation of parameters is also adopted. The complexity of the likelihood function is handled through the Metropolis-Hasting algorithm. The proposed method is illustrated with few examples using different baseline distributions. A real life data set is also analysed. AMS subject classifications: 62N02, 62F15

Joint longitudinal and time-to-event cure models for the assessment of being cured

2019 ◽  
Vol 29 (4) ◽  
pp. 1256-1270
Author(s):  
Antoine Barbieri ◽  
Catherine Legrand
Keyword(s):  
Survival Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Medical Intervention ◽  
Time To Event ◽  
Mixture Cure Model ◽  
Longitudinal Measurements ◽  
Cure Fraction ◽  
Cure Models ◽  
Latent Structures

Medical time-to-event studies frequently include two groups of patients: those who will not experience the event of interest and are said to be “cured” and those who will develop the event and are said to be “susceptible”. However, the cure status is unobserved in (right-)censored patients. While most of the work on cure models focuses on the time-to-event for the uncured patients (latency) or on the baseline probability of being cured or not (incidence), we focus in this research on the conditional probability of being cured after a medical intervention given survival until a certain time. Assuming the availability of longitudinal measurements collected over time and being informative on the risk to develop the event, we consider joint models for longitudinal and survival data given a cure fraction. These models include a linear mixed model to fit the trajectory of longitudinal measurements and a mixture cure model. In simulation studies, different shared latent structures linking both submodels are compared in order to assess their predictive performance. Finally, an illustration on HIV patient data completes the comparison.

scholarly journals A new threshold regression model for survival data with a cure fraction

2010 ◽  
Vol 17 (1) ◽  
pp. 101-122 ◽  
Author(s):  
Sungduk Kim ◽  
Ming-Hui Chen ◽  
Dipak K. Dey
Keyword(s):  
Regression Model ◽  
Survival Data ◽  
Threshold Regression ◽  
Cure Fraction ◽  
Threshold Regression Model

scholarly journals A new joint model for longitudinal and survival data with a cure fraction

2004 ◽  
Vol 91 (1) ◽  
pp. 18-34 ◽  
Author(s):  
Ming-Hui Chen ◽  
Joseph G Ibrahim ◽  
Debajyoti Sinha
Keyword(s):  
Survival Data ◽  
Joint Model ◽  
Cure Fraction ◽  
Longitudinal And Survival Data

Bayesian dynamic models for survival data with a cure fraction

2006 ◽  
Vol 13 (1) ◽  
pp. 17-35 ◽  
Author(s):  
Sungduk Kim ◽  
Ming-Hui Chen ◽  
Dipak K. Dey ◽  
Dani Gamerman
Keyword(s):  
Survival Data ◽  
Dynamic Models ◽  
Cure Fraction

Fully semiparametric Bayesian approach for modeling survival data with cure fraction

2013 ◽  
Vol 56 (2) ◽  
pp. 198-218 ◽  
Author(s):  
Fabio N. Demarqui ◽  
Dipak K. Dey ◽  
Rosangela H. Loschi ◽  
Enrico A. Colosimo
Keyword(s):  
Bayesian Approach ◽  
Survival Data ◽  
Cure Fraction

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 Nadarajah-Haghighi Model for Survival Data With Long Term Survivors in the Presence of Right Censored Data

2021 ◽  
pp. 695-709
Author(s):  
Umar Usman ◽  
Shamsuddeen Suleiman ◽  
Bello Magaji Arkilla ◽  
Yakubu Aliyu
Keyword(s):  
Survival Data ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Right Censoring ◽  
Right Censored Data ◽  
Term Survival ◽  
Proposed Model ◽  
Cure Fraction ◽  
Long Term Survivors

In this paper, a new long term survival model called Nadarajah-Haghighi model for survival data with long term survivors was proposed. The model is used in fitting data where the population of interest is a mixture of individuals that are susceptible to the event of interest and individuals that are not susceptible to the event of interest. The statistical properties of the proposed model including quantile function, moments, mean and variance were provided. Maximum likelihood estimation procedure was used to estimate the parameters of the model assuming right censoring. Furthermore, Bayesian method of estimation was also employed in estimating the parameters of the model assuming right censoring. Simulations study was performed in order to ascertain the performances of the MLE estimators. Random samples of different sample sizes were generated from the model with some arbitrary values for the parameters for 5%, 1:3% and 1:5% cure fraction values. Bias, standard error and mean square error were used as discrimination criteria. Additionally, we compared the performance of the proposed model with some competing models. The results of the applications indicates that the proposed model is more efficient than the models compared with. Finally, we fitted some models considering type of treatment as a covariate. It was observed that the covariate  have effect on the shape parameter of the proposed model.

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