Semi-Parametric Cure Rate Proportional Odds Models with Spatial Frailties for Interval-Censored Data

2019 ◽  
Vol 11 (03n04) ◽  
pp. 1950005
Author(s):  
Yiqi Bao ◽  
Vicente G. Cancho ◽  
Francisco Louzada ◽  
Adriano K. Suzuki
Keyword(s):  
Censored Data ◽  
Real Data ◽  
Interval Censored Data ◽  
Spatial Correlations ◽  
Cure Rate ◽  
Proportional Odds ◽  
Data Set ◽  
Cure Rate Models ◽  
Cure Models ◽  
Interval Censored

In this work, we proposed the semi-parametric cure rate models with independent and dependent spatial frailties. These models extend the proportional odds cure models and allow for spatial correlations by including spatial frailty for the interval censored data setting. Moreover, since these cure models are obtained by considering the occurrence of an event of interest is caused by the presence of any nonobserved risks, we also study the complementary cure model, that is, the cure models are obtained by assuming the occurrence of an event of interest is caused when all of the nonobserved risks are activated. The MCMC method is used in a Bayesian approach for inferential purposes. We conduct an influence diagnostic through the diagnostic measures in order to detect possible influential or extreme observations that can cause distortions on the results of the analysis. Finally, the proposed models are applied to the analysis of a real data set.

scholarly journals Cure rate proportional odds models with spatial frailties for interval-censored data

2017 ◽  
Vol 24 (6) ◽  
pp. 605-625 ◽  
Author(s):  
Bao Yiqi ◽  
Vicente Garibay Cancho ◽  
Francisco Louzada ◽  
Adriano Kamimura Suzuki
Keyword(s):  
Censored Data ◽  
Interval Censored Data ◽  
Cure Rate ◽  
Proportional Odds ◽  
Interval Censored ◽  
Proportional Odds Models

scholarly journals Bayesian Inference of the Weibull Model Based on Interval-Censored Survival Data

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Chris Bambey Guure ◽  
Noor Akma Ibrahim ◽  
Mohd Bakri Adam
Keyword(s):  
Maximum Likelihood ◽  
Censored Data ◽  
Survival Data ◽  
Failure Time ◽  
Real Data ◽  
Weibull Model ◽  
Bayesian Estimator ◽  
Interval Censored Data ◽  
Weibull Parameters ◽  
Interval Censored

Interval-censored data consist of adjacent inspection times that surround an unknown failure time. We have in this paper reviewed the classical approach which is maximum likelihood in estimating the Weibull parameters with interval-censored data. We have also considered the Bayesian approach in estimating the Weibull parameters with interval-censored data under three loss functions. This study became necessary because of the limited discussion in the literature, if at all, with regard to estimating the Weibull parameters with interval-censored data using Bayesian. A simulation study is carried out to compare the performances of the methods. A real data application is also illustrated. It has been observed from the study that the Bayesian estimator is preferred to the classical maximum likelihood estimator for both the scale and shape parameters.

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.

Cure rate model with interval censored data

2007 ◽  
Vol 27 (1) ◽  
pp. 3-14 ◽  
Author(s):  
Yang-Jin Kim ◽  
Myoungshic Jhun
Keyword(s):  
Censored Data ◽  
Interval Censored Data ◽  
Rate Model ◽  
Cure Rate ◽  
Cure Rate Model ◽  
Interval Censored

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

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