Incorporation of frailties into a cure rate regression model and its diagnostics and application to melanoma data

2018 ◽  
Vol 37 (29) ◽  
pp. 4421-4440 ◽  
Author(s):  
Jeremias Leão ◽  
Víctor Leiva ◽  
Helton Saulo ◽  
Vera Tomazella
Keyword(s):  
Regression Model ◽  
Cure Rate

Hazard regression model and cure rate model in colon cancer relative survival trends: are they telling the same story?

2008 ◽  
Vol 23 (4) ◽  
pp. 251-259 ◽  
Author(s):  
Theodora Bejan-Angoulvant ◽  
Anne-Marie Bouvier ◽  
Nadine Bossard ◽  
Aurelien Belot ◽  
Valérie Jooste ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Regression Model ◽  
Relative Survival ◽  
Rate Model ◽  
Cure Rate ◽  
Hazard Regression ◽  
Cure Rate Model

scholarly journals Predicting the cure rate of breast cancer using a new regression model with four regression structures

2017 ◽  
Vol 27 (11) ◽  
pp. 3207-3223 ◽  
Author(s):  
Thiago G Ramires ◽  
Gauss M Cordeiro ◽  
Michael W Kattan ◽  
Niel Hens ◽  
Edwin MM Ortega
Keyword(s):  
Breast Carcinoma ◽  
Regression Model ◽  
Additive Model ◽  
Real Data ◽  
Cure Rate ◽  
Time Model ◽  
Data Set ◽  
Explanatory Variables ◽  
Diagnostic Measures ◽  
Cure Fraction

Cure fraction models are useful to model lifetime data with long-term survivors. We propose a flexible four-parameter cure rate survival model called the log-sinh Cauchy promotion time model for predicting breast carcinoma survival in women who underwent mastectomy. The model can estimate simultaneously the effects of the explanatory variables on the timing acceleration/deceleration of a given event, the surviving fraction, the heterogeneity, and the possible existence of bimodality in the data. In order to examine the performance of the proposed model, simulations are presented to verify the robust aspects of this flexible class against outlying and influential observations. Furthermore, we determine some diagnostic measures and the one-step approximations of the estimates in the case-deletion model. The new model was implemented in the generalized additive model for location, scale and shape package of the R software, which is presented throughout the paper by way of a brief tutorial on its use. The potential of the new regression model to accurately predict breast carcinoma mortality is illustrated using a real data set.

scholarly journals Negative Binomial Kumaraswamy-G Cure Rate Regression Model

2018 ◽  
Vol 11 (1) ◽  
pp. 6 ◽  
Author(s):  
Amanda D’Andrea ◽  
Ricardo Rocha ◽  
Vera Tomazella ◽  
Francisco Louzada
Keyword(s):  
Regression Model ◽  
Negative Binomial ◽  
Cure Rate

scholarly journals Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 639
Author(s):  
Rolando de la Cruz ◽  
Oslando Padilla ◽  
Mauricio A. Valle ◽  
Gonzalo A. Ruz
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cox Regression ◽  
Bayesian Regression ◽  
Rate Model ◽  
Cure Rate ◽  
Cox Regression Model ◽  
Cure Rate Model ◽  
Random Survival Forest

This study aims to analyze and explore criminal recidivism with different modeling strategies: one based on an explanation of the phenomenon and another based on a prediction task. We compared three common statistical approaches for modeling recidivism: the logistic regression model, the Cox regression model, and the cure rate model. The parameters of these models were estimated from a Bayesian point of view. Additionally, for prediction purposes, we compared the Cox proportional model, a random survival forest, and a deep neural network. To conduct this study, we used a real dataset that corresponds to a cohort of individuals which consisted of men convicted of sexual crimes against women in 1973 in England and Wales. The results show that the logistic regression model tends to give more precise estimations of the probabilities of recidivism both globally and with the subgroups considered, but at the expense of running a model for each moment of the time that is of interest. The cure rate model with a relatively simple distribution, such as Weibull, provides acceptable estimations, and these tend to be better with longer follow-up periods. The Cox regression model can provide the most biased estimations with certain subgroups. The prediction results show the deep neural network’s superiority compared to the Cox proportional model and the random survival forest.

scholarly journals The Geometric Power Half-Normal Regression Model with Cure Rate

2016 ◽  
Vol 46 (138) ◽  
pp. 1-1
Author(s):  
Yolanda M. Gomez ◽  
Heleno Bolfarine
Keyword(s):  
Regression Model ◽  
Cure Rate ◽  
Normal Regression

The Geometric Birnbaum–Saunders regression model with cure rate

2012 ◽  
Vol 142 (4) ◽  
pp. 993-1000 ◽  
Author(s):  
Vicente G. Cancho ◽  
Francisco Louzada ◽  
Gladys D.C. Barriga
Keyword(s):  
Regression Model ◽  
Cure Rate

The Negative Binomial Beta Prime Regression Model with Cure Rate: Application with a Melanoma Dataset

2021 ◽  
Vol 15 (3) ◽  
Author(s):  
Jeremias Leão ◽  
Marcelo Bourguignon ◽  
Helton Saulo ◽  
Manoel Santos-Neto ◽  
Vinícius Calsavara
Keyword(s):  
Regression Model ◽  
Negative Binomial ◽  
Cure Rate
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