Cure Rate Models

2001 ◽  
pp. 155-207 ◽  
Author(s):  
Joseph G. Ibrahim ◽  
Ming-Hui Chen ◽  
Debajyoti Sinha
Keyword(s):  
Cure Rate ◽  
Cure Rate Models

Relaxed Poisson cure rate models

2015 ◽  
Vol 58 (2) ◽  
pp. 397-415 ◽  
Author(s):  
Josemar Rodrigues ◽  
Gauss M. Cordeiro ◽  
Vicente G. Cancho ◽  
N. Balakrishnan
Keyword(s):  
Cure Rate ◽  
Cure Rate Models

Bayesian Approaches to Cure Rate Models

2005 ◽  
Author(s):  
Joseph G. Ibrahim ◽  
Ming-Hui Chen ◽  
Debajyoti Sinha
Keyword(s):  
Cure Rate ◽  
Bayesian Approaches ◽  
Cure Rate Models

scholarly journals Gradient and Likelihood Ratio Tests in Cure Rate Models

2016 ◽  
Vol 5 (4) ◽  
pp. 9 ◽  
Author(s):  
Hérica P. A. Carneiro ◽  
Dione M. Valença
Keyword(s):  
Likelihood Ratio ◽  
Asymptotic Properties ◽  
Information Matrix ◽  
Survival Models ◽  
Finite Sample ◽  
Cure Rate ◽  
Time Model ◽  
Cure Rate Models ◽  
Large Sample ◽  
Finite Samples

In some survival studies part of the population may be no longer subject to the event of interest. The called cure rate models take this fact into account. They have been extensively studied for several authors who have proposed extensions and applications in real lifetime data. Classic large sample tests are usually considered in these applications, especially the likelihood ratio. Recently  a new test called \textit{gradient test} has been proposed. The gradient statistic shares the same asymptotic properties with the classic likelihood ratio and does not involve knowledge of the information matrix, which can be an advantage in survival models. Some simulation studies have been carried out to explore the behavior of the gradient test in finite samples and compare it with the classic tests in different models. However little is known about the properties of these large sample tests in finite sample for cure rate models. In this work we  performed a simulation study based on the promotion time model with Weibull distribution, to assess the performance of likelihood ratio and gradient tests in finite samples. An application is presented to illustrate the results.

On a class of non‐linear transformation cure rate models

2020 ◽  
Vol 62 (5) ◽  
pp. 1208-1222 ◽  
Author(s):  
Narayanaswamy Balakrishnan ◽  
Fotios S. Milienos
Keyword(s):  
Linear Transformation ◽  
Cure Rate ◽  
Cure Rate Models ◽  
Non Linear

Efficient semiparametric mixture inferences on cure rate models for competing risks

2015 ◽  
Vol 43 (3) ◽  
pp. 420-435 ◽  
Author(s):  
Sangbum Choi ◽  
Xuelin Huang ◽  
Janice N. Cormier
Keyword(s):  
Competing Risks ◽  
Cure Rate ◽  
Cure Rate Models ◽  
Semiparametric Mixture

scholarly journals Analyzing the Long-Term Survival of Patients with Colorectal Cancer: A Study Using Parametric Non-Mixture Cure Rate Models

2018 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Mehdi Azizmohammad Looha ◽  
Elaheh Zarean ◽  
Mohamad Amin Pourhoseingholi ◽  
Seyyed Vahid Hosseini ◽  
Tara Azimi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cure Rate ◽  
Cure Rate Models ◽  
Term Survival ◽  
Long Term Survival

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.

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