scholarly journals A simplified stochastic EM algorithm for cure rate model with negative binomial competing risks: An application to breast cancer data

2021 ◽  
Author(s):  
Suvra Pal
Keyword(s):  
Breast Cancer ◽  
Em Algorithm ◽  
Competing Risks ◽  
Negative Binomial ◽  
Rate Model ◽  
Breast Cancer Data ◽  
Cure Rate ◽  
Cancer Data ◽  
Stochastic Em Algorithm ◽  
Stochastic Em

scholarly journals Fitting Cure Rate Model to Breast Cancer Data of Cancer Research Center

2015 ◽  
Vol 16 (17) ◽  
pp. 7923-7927 ◽  
Author(s):  
Ahmad Reza Baghestani ◽  
Farid Zayeri ◽  
Mohammad Esmaeil Akbari ◽  
Leyla Shojaee ◽  
Naghmeh Khadembashi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Research ◽  
Research Center ◽  
Rate Model ◽  
Breast Cancer Data ◽  
Cure Rate ◽  
Cancer Data ◽  
Cure Rate Model

scholarly journals Estimation of survival and hazard curves of mixture Mirra cure rate model: Application to gastric and breast cancer data

2020 ◽  
Vol 9 (4) ◽  
pp. 132-137
Author(s):  
Marcos Vinicius de Oliveira Peres ◽  
Franchesco Sanches dos Santos ◽  
Ricado Puziol de Oliveira
Keyword(s):  
Breast Cancer ◽  
Rate Model ◽  
Breast Cancer Data ◽  
Cure Rate ◽  
Model Application ◽  
Cancer Data ◽  
Cure Rate Model ◽  
Hazard Curves

A note on the EM algorithm for estimation in the destructive negative binomial cure rate model

2017 ◽  
Vol 87 (12) ◽  
pp. 2291-2297
Author(s):  
Diego I. Gallardo ◽  
Yolanda M. Gómez ◽  
Mário de Castro
Keyword(s):  
Em Algorithm ◽  
Negative Binomial ◽  
Rate Model ◽  
Cure Rate ◽  
Cure Rate Model ◽  
The Em Algorithm

Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data

2021 ◽  
pp. 096228022110370
Author(s):  
Mengjiao Peng ◽  
Liming Xiang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Overall Survival ◽  
Competing Risks ◽  
Distant Metastasis ◽  
Finite Sample ◽  
Breast Cancer Data ◽  
Free Survival ◽  
Cancer Data ◽  
Distant Metastasis Free Survival

Ultrahigh-dimensional gene features are often collected in modern cancer studies in which the number of gene features [Formula: see text] is extremely larger than sample size [Formula: see text]. While gene expression patterns have been shown to be related to patients’ survival in microarray-based gene expression studies, one has to deal with the challenges of ultrahigh-dimensional genetic predictors for survival predicting and genetic understanding of the disease in precision medicine. The problem becomes more complicated when two types of survival endpoints, distant metastasis-free survival and overall survival, are of interest in the study and outcome data can be subject to semi-competing risks due to the fact that distant metastasis-free survival is possibly censored by overall survival but not vice versa. Our focus in this paper is to extract important features, which have great impacts on both distant metastasis-free survival and overall survival jointly, from massive gene expression data in the semi-competing risks setting. We propose a model-free screening method based on the ranking of the correlation between gene features and the joint survival function of two endpoints. The method accounts for the relationship between two endpoints in a simply defined utility measure that is easy to understand and calculate. We show its favorable theoretical properties such as the sure screening and ranking consistency, and evaluate its finite sample performance through extensive simulation studies. Finally, an application to classifying breast cancer data clearly demonstrates the utility of the proposed method in practice.

Bayesian Frailty Model for Time to Event Breast Cancer Data

2011 ◽  
Vol 4 (2) ◽  
pp. 8-12
Author(s):  
Leo Alexander T Leo Alexander T ◽  
◽  
Pari Dayal L Pari Dayal L ◽  
Valarmathi S Valarmathi S ◽  
Ponnuraja C Ponnuraja C ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Frailty Model ◽  
Breast Cancer Data ◽  
Time To Event ◽  
Cancer Data
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