scholarly journals Bayesian spatial homogeneity pursuit for survival data with an application to the SEER respiratory cancer data

Biometrics ◽  
2021 ◽  
Author(s):  
Lijiang Geng ◽  
Guanyu Hu
Keyword(s):  
Survival Data ◽  
Spatial Homogeneity ◽  
Cancer Data ◽  
Respiratory Cancer

Regression Analysis of Grouped Survival Data with Application to Breast Cancer Data

Biometrics ◽  
1978 ◽  
Vol 34 (1) ◽  
pp. 57 ◽  
Author(s):  
R. L. Prentice ◽  
L. A. Gloeckler
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Survival Data ◽  
Breast Cancer Data ◽  
Cancer Data

Clinical characteristics and predictors of outcomes in patients with fibrolamellar carcinoma: An eleven-year analysis of the National Cancer Database (NCDB).

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 413-413
Author(s):  
Hussein Assi ◽  
Hassan Hatoum ◽  
Sarbajit Mukherjee ◽  
Michael Machiorlatti ◽  
Sara Vesely ◽  
...  
Keyword(s):  
Overall Survival ◽  
Survival Data ◽  
Liver Tumors ◽  
Multivariable Analysis ◽  
Case Series ◽  
Cox Proportional Hazards Model ◽  
Treatment Modalities ◽  
Cancer Data ◽  
Regional Lymph Node Dissection ◽  
Fibrolamellar Carcinoma

413 Background: Fibrolamellar carcinoma (FLC) is a very rare liver tumor, comprising only 1% of all primary liver tumors in the United Sates. There is no standard of care for unresectable disease. Current practices are based on small retrospective studies and case series. We aim to analyze the clinicopathologic factors and treatment modalities affecting overall survival (OS) in FLC. Methods: Using the National Cancer Data Base (NCDB), we identified 496 patients diagnosed with FLC between 2004 and 2015. Simple descriptive statistics were created for all covariates. Survival data was available on 461 patients. Kaplan Meier Survival analysis was used for unadjusted results, and Cox proportional hazards model was used for multivariable analysis. The objective of the study is to identify predictors of survival in FLC. Results: The median age at diagnosis was 32 (range 18-90) years. Fifty-six percent were males. Stage distribution included 114 (31.2%), 43 (11.8%), 89 (24.3%) and 120 (32.8%) patients for stages I, II, III and IV, respectively. Median follow-up was 24 months. Surgery of the primary site was performed on 282 (56.9%) of patients, 146 (51.2%) of which had regional lymph node dissection. Seventy (47.9%) patients had pN+ disease. Among patients with available serum alpha fetoprotein (AFP) data, 146 (42.5%) had abnormal AFP levels (> 20 ng/mL). Median OS by stage were 78.5, 87.2, 18.6, and 10.6 months for stages I, II, III, and IV, respectively. Multivariate analysis showed that age (HR 1.01, p < 0.0001), pN+ (HR 2.31, p = 0.0003), and abnormal AFP (HR 1.69, p = 0.0003) were negative predictors of survival. Among metastatic patients, 57 (11.4%) had metastatectomy. Metastatectomy improved overall survival in stage IV FLC, HR 0.51 (95% CI 0.29-0.89). Conclusions: Independent predictors of decreased OS in patients with FLC include age, pN+ and abnormal AFP. Metastatectomy improved OS. FLC is a rare disease entity that warrants further investigations to better delineate optimal treatment approaches.

scholarly journals Two Artificial Neural Networks for Modeling Discrete Survival Time of Censored Data

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Taysseer Sharaf ◽  
Chris P. Tsokos
Keyword(s):  
Survival Time ◽  
Survival Data ◽  
Good Alternative ◽  
Cancer Data ◽  
Melanoma Cancer ◽  
Cox Proportional Hazard ◽  
Comprehensive Comparison ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Nonlinear Behaviors

Artificial neural network (ANN) theory is emerging as an alternative to conventional statistical methods in modeling nonlinear functions. The popular Cox proportional hazard model falls short in modeling survival data with nonlinear behaviors. ANN is a good alternative to the Cox PH as the proportionality of the hazard assumption and model relaxations are not required. In addition, ANN possesses a powerful capability of handling complex nonlinear relations within the risk factors associated with survival time. In this study, we present a comprehensive comparison of two different approaches of utilizing ANN in modeling smooth conditional hazard probability function. We use real melanoma cancer data to illustrate the usefulness of the proposed ANN methods. We report some significant results in comparing the survival time of male and female melanoma patients.

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 Application of Modified Generalized–Gamma Mixture Cure Model in the Analysis of Ovarian Cancer Data

2021 ◽  
Vol 2123 (1) ◽  
pp. 012041
Author(s):  
Serifat A. Folorunso ◽  
Timothy A.O. Oluwasola ◽  
Angela U. Chukwu ◽  
Akintunde A. Odukogbe
Keyword(s):  
Ovarian Cancer ◽  
Survival Data ◽  
Information Criterion ◽  
Cure Model ◽  
Modeling And Analysis ◽  
Cancer Data ◽  
Mixture Cure Model ◽  
Generalized Gamma ◽  
Ovarian Cancer Data ◽  
Cure Models

Abstract The modeling and analysis of lifetime for terminal diseases such as cancer is a significant aspect of statistical work. This study considered data from thirty-seven women diagnosed with Ovarian Cancer and hospitalized for care at theDepartment of Obstetrics and Gynecology, University of Ibadan, Nigeria. Focus was on the application of a parametric mixture cure model that can handle skewness associated with survival data – a modified generalized-gamma mixture cure model (MGGMCM). The effectiveness of MGGMCM was compared with existing parametric mixture cure models using Akaike Information Criterion, median time-to-cure and variance of the cure rate. It was observed that the MGGMCM is an improved parametric model for the mixture cure model.

scholarly journals Gastric cancer in East Azerbaijan, Iran: Five-year survival analysis of population-based cancer registry results

2020 ◽  
Vol 7 (11) ◽  
pp. 4114-4121
Author(s):  
Pooneh Jabbaripour ◽  
Mohammad Hossein Somi ◽  
Hossein Mashhadi Abdolahi ◽  
Roya Dolatkhah
Keyword(s):  
Gastric Cancer ◽  
Survival Analysis ◽  
Cancer Registry ◽  
Survival Data ◽  
Cox Regression ◽  
Survival Function ◽  
Population Based ◽  
Rank Test ◽  
Cancer Data ◽  
East Azerbaijan

Introduction: Gastric cancer is the most common cancer with significant increasing trends during the last decade in Iran. The aim of this study was to evaluate the epidemiologic profile of gastric cancer along with gastric cancer-specific survival analysis. Methods: This was an analytical cross-sectional study in which all gastric cancer data were analyzed using the database of the East Azerbaijan Population-Based Cancer Registry (EA-PBCR). The incidents of definitive gastric cancer diagnosis were between the period of March 20th, 2015 to March 19th, 2017 ( = 3 Iranian solar years). The survival analysis was performed using the Kaplan-Meier method and life tables for 1- to 5-year survival data. The Log-rank test and Cox regression were computed to test the equality of survival function and mortality hazard. Results: Overall, 2,631 newly diagnosed gastric cancer cases were registered for 3 years. Gastric cancer was 2.35 times more common in men than women. The most common age group was the 7th decade- with 531 (31.2%) gastric cancer cases. Most of the gastric cancer cases were non-cardia (n = 2,244, 85.29%) cancer, and the proportion of non-cardia to cardia gastric cancer was 5.8:1. Overall survival was 60.1%, and 1- to 5-year survival proportions were 91.61%, 64.21%, 58.53%, 30.14% and 24.77%, respectively. Cardia cancers had a worse survival rate than non-cardia cancers, and the hazard of mortality was 1.33 times higher in cardia than non-cardia cancers (hazard ratio or HR = 1.33; 95% CI: 1.05 - 1.68; P = 0.017). Conclusion: Non-cardia gastric cancer is still the most dominant subsite in East Azerbaijan, Iran. There was a higher 1- to 5- year survival proportion in East Azerbaijan, with lower overall mortality rates, compared to other regions of Iran.

scholarly journals Joint modelling of longitudinal and survival data in the presence of competing risks with applications to prostate cancer data

2020 ◽  
pp. 1471082X2094462
Author(s):  
Md. Tuhin Sheikh ◽  
Joseph G. Ibrahim ◽  
Jonathan A. Gelfond ◽  
Wei Sun ◽  
Ming-Hui Chen
Keyword(s):  
Prostate Cancer ◽  
Competing Risks ◽  
Survival Data ◽  
Monte Carlo Sampling ◽  
Low Grade ◽  
High Grade ◽  
Cancer Data ◽  
Empirical Performance ◽  
Competing Causes ◽  
Causes Of Failure

This research is motivated from the data from a large Selenium and Vitamin E Cancer Prevention Trial (SELECT). The prostate specific antigens (PSAs) were collected longitudinally, and the survival endpoint was the time to low-grade cancer or the time to high-grade cancer (competing risks). In this article, the goal is to model the longitudinal PSA data and the time-to-prostate cancer (PC) due to low- or high-grade. We consider the low-grade and high-grade as two competing causes of developing PC. A joint model for simultaneously analysing longitudinal and time-to-event data in the presence of multiple causes of failure (or competing risk) is proposed within the Bayesian framework. The proposed model allows for handling the missing causes of failure in the SELECT data and implementing an efficient Markov chain Monte Carlo sampling algorithm to sample from the posterior distribution via a novel reparameterization technique. Bayesian criteria, [Formula: see text]DIC[Formula: see text], and [Formula: see text]WAIC[Formula: see text], are introduced to quantify the gain in fit in the survival sub-model due to the inclusion of longitudinal data. A simulation study is conducted to examine the empirical performance of the posterior estimates as well as [Formula: see text]DIC[Formula: see text] and [Formula: see text]WAIC[Formula: see text] and a detailed analysis of the SELECT data is also carried out to further demonstrate the proposed methodology.

Impact of Hispanic ethnicity on the clinical presentation and survival of esophageal and gastric cancer in south Florida.

2014 ◽  
Vol 32 (3_suppl) ◽  
pp. 125-125
Author(s):  
Sofia Palacio ◽  
Daniel A. Sussman ◽  
Bach Ardalan ◽  
Caio Max S. Rocha Lima ◽  
Peter Joel Hosein
Keyword(s):  
Gastric Cancer ◽  
African Americans ◽  
Surgical Resection ◽  
Survival Data ◽  
Race And Ethnicity ◽  
Clinical Presentation ◽  
Private Insurance ◽  
South Florida ◽  
Cox Proportional Hazards Model ◽  
Cancer Data

125 Background: Race and ethnicity are associated with differences in survival among patients with esophageal and gastric cancer (EGC); outcomes are better in Asian patients but worse for African-Americans compared to Caucasians and Asians. Limited data exist for Hispanics (Hisp) compared to non-Hispanic whites (NHW) or African-Americans (AA). Because of the large Hisp population in South Florida, we compared the clinical presentation and survival of patients with EGC by race and ethnicity. Methods: Using a cross-sectional study design, this IRB-approved analysis of the Florida Cancer Data System database identified all patients diagnosed at the University of Miami and Jackson Memorial Hospital between January 2000 and December 2012 with squamous cell carcinoma (SCC) or adenocarcinoma (AC) of the esophagus, and adenocarcinomas of the gastro-esophageal junction (GEJ) or stomach (STO). Demographic, treatment and survival data were extracted from the registry. Survival was analyzed using the Kaplan-Meier method and variables associated with survival were analyzed using a Cox proportional hazards model. Results: Data from 2,170 patients were available; 44% were Hisp, 19% AA and 38% NHW. Compared to NHW's and AA's, Hisp's were more likely to have the following features: male gender, advanced age at cancer diagnosis, esophageal site of malignancy, adenocarcinoma histology, earlier stage at presentation, history of smoking and alcohol use, private insurance, surgical resection and receipt of chemotherapy (p < 0.001 in each case). Hisp were less likely to have STO (p<0.001). In a multivariate model, race and ethnicity were not independently associated with survival but age, stage, surgical resection and chemotherapy administration were all independently associated with survival (p < 0.01 in each case). Country of birth did not influence results among Hispanic patients. Conclusions: Race and ethnicity were not independently associated with survival in this large registry study. However, significant differences in the tumor location, histology and stage of presentation exist, and further studies to elucidate the biological or environmental reasons for these disparities are warranted.

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