Semiparametric random censorship models for survival data with long-term survivors

2018 ◽  
Vol 49 (11) ◽  
pp. 2876-2896
Author(s):  
Yan Feng ◽  
Xiaobing Zhao ◽  
Xian Zhou
Keyword(s):  
Survival Data ◽  
Random Censorship ◽  
Long Term Survivors

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.

scholarly journals Nadarajah-Haghighi Model for Survival Data With Long Term Survivors in the Presence of Right Censored Data

2021 ◽  
pp. 695-709
Author(s):  
Umar Usman ◽  
Shamsuddeen Suleiman ◽  
Bello Magaji Arkilla ◽  
Yakubu Aliyu
Keyword(s):  
Survival Data ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Right Censoring ◽  
Right Censored Data ◽  
Term Survival ◽  
Proposed Model ◽  
Cure Fraction ◽  
Long Term Survivors

In this paper, a new long term survival model called Nadarajah-Haghighi model for survival data with long term survivors was proposed. The model is used in fitting data where the population of interest is a mixture of individuals that are susceptible to the event of interest and individuals that are not susceptible to the event of interest. The statistical properties of the proposed model including quantile function, moments, mean and variance were provided. Maximum likelihood estimation procedure was used to estimate the parameters of the model assuming right censoring. Furthermore, Bayesian method of estimation was also employed in estimating the parameters of the model assuming right censoring. Simulations study was performed in order to ascertain the performances of the MLE estimators. Random samples of different sample sizes were generated from the model with some arbitrary values for the parameters for 5%, 1:3% and 1:5% cure fraction values. Bias, standard error and mean square error were used as discrimination criteria. Additionally, we compared the performance of the proposed model with some competing models. The results of the applications indicates that the proposed model is more efficient than the models compared with. Finally, we fitted some models considering type of treatment as a covariate. It was observed that the covariate  have effect on the shape parameter of the proposed model.

A new parametric model for survival data with long-term survivors

2004 ◽  
Vol 23 (22) ◽  
pp. 3525-3543 ◽  
Author(s):  
Quanxi Shao ◽  
Xian Zhou
Keyword(s):  
Survival Data ◽  
Parametric Model ◽  
Long Term Survivors

scholarly journals Power laws in the Roman Empire: a survival analysis

2021 ◽  
Vol 8 (7) ◽  
pp. 210850
Author(s):  
P. L. Ramos ◽  
L. F. Costa ◽  
F. Louzada ◽  
F. A. Rodrigues
Keyword(s):  
Roman Empire ◽  
Power Law ◽  
Political Institutions ◽  
Survival Data ◽  
Power Laws ◽  
Data Types ◽  
Ancient Civilizations ◽  
Long Term Survivors ◽  
Power Law Distributions

The Roman Empire shaped western civilization, and many Roman principles are embodied in modern institutions. Although its political institutions proved both resilient and adaptable, allowing it to incorporate diverse populations, the Empire suffered from many conflicts. Indeed, most emperors died violently, from assassination, suicide or in battle. These conflicts produced patterns in the length of time that can be identified by statistical analysis. In this paper, we study the underlying patterns associated with the reign of the Roman emperors by using statistical tools of survival data analysis. We consider all the 175 Roman emperors and propose a new power-law model with change points to predict the time-to-violent-death of the Roman emperors. This model encompasses data in the presence of censoring and long-term survivors, providing more accurate predictions than previous models. Our results show that power-law distributions can also occur in survival data, as verified in other data types from natural and artificial systems, reinforcing the ubiquity of power-law distributions. The generality of our approach paves the way to further related investigations not only in other ancient civilizations but also in applications in engineering and medicine.

Computer extracted measurements of intra-tumoral heterogeneity on H&E stained tissue images to distinguish short term and long term survivors in patients with non-small cell lung carcinoma.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e20052-e20052
Author(s):  
Cheng Lu ◽  
Monica Khunger ◽  
Rajat Thawani ◽  
Vamsidhar Velcheti ◽  
Anant Madabhushi
Keyword(s):  
Survival Data ◽  
Early Stage ◽  
Image Features ◽  
Short Term ◽  
Term Survival ◽  
Cell Lung Carcinoma ◽  
Early Stage Nsclc ◽  
Nsclc Patients ◽  
Long Term Survivors

e20052 Background: Molecular and morphologic heterogeneity is an important characteristic of cancer. This spatial and temporal tumor heterogeneity has important implications on tumor behavior and response to therapies. This study aims to evaluate the role of computer extracted features of intra-tumoral heterogeneity (ITH) from digitized whole slide H&E stained images of early stage NSCLC patients treated with surgery as a prognostic marker for survival. Methods: A cohort of 89 early stage NSCLC patients treated with surgery with long term survival data were identified. 28 patients had OS> 3 years from the date of definitive surgery and were defined as long term survivors and 61 patients had OS < 3 years, and were defined as short term survivors. Corresponding H&E stained whole mounted lung tissue images was digitally scanned and a thoracic pathologist marked the primary tumor margins on these images. Our computational approach involved determining the variance in measurements relating to nuclear size, shape, and texture across the tissue section; Each feature was then assigned a morphologic diversity score (MDS) based off the variance; the top predictive MDS features were identified via Wilcoxon Rank Sum Test and then evaluated via a quadratic classifier using 3-fold cross validation. Kaplan-Meier (KM) survival analysis was performed for the ITH features, as well as T- and N-stage. Results: The top ranked MDS features yielded a mean area under the receiver operating characteristic curve (AUC) of 0.66 in discriminating short term from long term survivors. A p=0.00657 (see Table) was obtained for KM-analysis of the ITH features. Conclusions: Computer extracted image features of ITH enabled differentiation of NSCLC patients with short and longer term survival. Large scale multi-site validation will need to be done to establish ITH measurements as a prognostic biomarker for NSCLC patients. [Table: see text]

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