A 3-parameter Gompertz distribution for survival data with competing risks, with an application to breast cancer data

2016 ◽  
Vol 43 (12) ◽  
pp. 2239-2253 ◽  
Author(s):  
S. R. Haile ◽  
J.-H. Jeong ◽  
X. Chen ◽  
Y. Cheng
Keyword(s):  
Breast Cancer ◽  
Competing Risks ◽  
Survival Data ◽  
Breast Cancer Data ◽  
Cancer Data ◽  
Gompertz Distribution

scholarly journals A BAYESIAN WEIBULL ANALYSIS OF BREAST CANCER DATA WITH LONG-TERM SURVIVORS IN PARANA STATE, BRAZIL

2021 ◽  
Vol 39 (2) ◽  
pp. 293-310
Author(s):  
Talita Evelin Nabarrete Tristão de MORAES ◽  
Isolde PREVIDELLI ◽  
Giovani Loiola da SILVA
Keyword(s):  
Breast Cancer ◽  
Survival Data ◽  
White Women ◽  
Age Groups ◽  
Population Based ◽  
Breast Cancer Data ◽  
Weibull Regression ◽  
Cancer Data ◽  
Long Term Survivors

Breast cancer is one of the most common diseases among women worldwide with about 25% of new cases each year. In Brazil, 59,700 new cases of breast cancer were expected in 2019, according to the Brazilian National Cancer Institute (INCA). Survival analysis has been an useful tool for the identifying the risk and prognostic factors for cancer patients. This work aims to characterize the prognostic value of demographic, clinical and pathological variables in relation to the survival time of 2,092 patients diagnosed with breast cancer in Parana State, Brazil, from 2004 to 2016. In this sense, we propose a Bayesian analysis of survival data with long-term survivors by using Weibull regression models through integrated nested Laplace approximations (INLA). The results point to a proportion of long-term survivors around 57:6% in the population under study. In regard to potential risk factors, we namely concluded that 40-50 year age group has superior survival than younger and older age groups, white women have higher breast cancer risk than other races, and marital status decreases that risk. Caution on the general use of these results is nevertheless advised, since we have analyzed population-based breast cancer data without proper monitoring by a healthprofessional.

Statistical Applications of Survival Data Analysis for Breast Cancer Data

2015 ◽  
Vol 4 (2) ◽  
pp. 30-36
Author(s):  
Ahammad Basha Shaik ◽  
◽  
Venkataramanaiah. M ◽  
Thasleema . ◽  
◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Analysis ◽  
Survival Data ◽  
Breast Cancer Data ◽  
Cancer Data

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

AGE FEATURES OF MOLECULAR-BIOLOGICAL SUBTYPES OF BREAST CANCER IN THE REPUBLIC OF UZBEKISTAN

2018 ◽  
Vol 64 (2) ◽  
pp. 196-199
Author(s):  
Gulya Miryusupova ◽  
G. Khakimov ◽  
N. Shayusupov
Keyword(s):  
Breast Cancer ◽  
Cancer Care ◽  
Indigenous Women ◽  
Morbidity And Mortality ◽  
Breast Cancer Data ◽  
Female Population ◽  
Cancer Data ◽  
Age Related ◽  
The Republic ◽  
Age Features

According to the results of breast cancer data in the Republic of Uzbekistan in addition to the increase in morbidity and mortality from breast cancer among women the presence of age specific features among indigenous women in the direction of “rejuvenating” of the disease with all molecular-biological (phenotypic) subtypes of breast cancer were marked. Within the framework of age-related features the prevalence of the least favorable phenotypes of breast cancer was found among indigenous women: Her2/neu hyperexpressive and three times negative subtype of breast cancer. The data obtained made it possible to build a so-called population “portrait” of breast cancer on the territory of the Republic, which in turn would contribute to further improvement of cancer care for the female population of the country.

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