• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.15
• /
• pp.6029-6033
• /
• 2014

The burden and severity of a cancer can be reflected by patterns of survival. Breast cancer prognosis between two countries with a different socioeconomic status and cultural beliefs may exhibit wide variation. This study aimed to describe survival in patients with breast cancer in China and Thailand in relation to demographic and clinical prognostic information. Materials and Methods: We compared the survival of 1,504 Chinese women in Yunnan province and 929 Thai women in Songkhla with breast cancer from 2006 to 2010. Descriptive prognostic comparisons between the Chinese and Thai women were performed by relative survival analysis. A Cox regression model was used to calculate the hazard ratios of death, taking into account the age, disease stage, period of diagnosis and country. Results: The overall 5-year survival proportion for patients diagnosed with breast cancer for Yunnan province (0.72) appeared slightly better than Songkhla (0.70) without statistical significance. Thai women diagnosed with distant and regional breast cancer had poorer survival than Chinese women. Disease stage was the most important determinant of survival from the results of Cox regression model. Conclusions: Breast cancer patients in Kunming had slightly greater five-year survival rate than patients in Songkhla. Both Chinese and Thai women need improvement in prognosis, which could conceivably be attained through increased public education and awareness regarding early detection and compliance to treatment protocols.

• Journal of Biomedical Engineering Research
• /
• v.33 no.3
• /
• pp.148-154
• /
• 2012

Hemorrhagic shock is a primary cause of deaths resulting from injury in the world. Although many studies have tried to diagnose accurately hemorrhagic shock in the early stage, such attempts were not successful due to compensatory mechanisms of humans. The objective of this study was to construct a survival prediction model of rats in acute hemorrhagic shock using a random forest (RF) model. Heart rate (HR), mean arterial pressure (MAP), respiration rate (RR), lactate concentration (LC), and peripheral perfusion (PP) measured in rats were used as input variables for the RF model and its performance was compared with that of a logistic regression (LR) model. Before constructing the models, we performed 5-fold cross validation for RF variable selection, and forward stepwise variable selection for the LR model to examine which variables were important for the models. For the LR model, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (ROC-AUC) were 0.83, 0.95, 0.88, and 0.96, respectively. For the RF models, sensitivity, specificity, accuracy, and AUC were 0.97, 0.95, 0.96, and 0.99, respectively. In conclusion, the RF model was superior to the LR model for survival prediction in the rat model.

• Journal of the Korean Statistical Society
• /
• v.26 no.4
• /
• pp.429-443
• /
• 1997

We consider the problem of obtaining several types of simultaneous confidence bands for the survival curve under the additive risk model. The derivation uses the weak convergence of normalized cumulative hazard estimator to a mean zero Gaussian process whose distribution can be easily approxomated through simulation. The bands are illustrated by applying them from two well-known clinicla studies. Finally, simulation studies are carried outo to compare the performance of the proposed bands for the survival function under the additive risk model.

• Food Science and Biotechnology
• /
• v.18 no.1
• /
• pp.137-142
• /
• 2009

One of the most important aspects of conducting this microbial risk assessment (MRA) is determining the model in microbial behaviors in food systems. However, to fully these modeling, large expenditures or newly laboratory experiments will be spent to do it. To overcome these problems, it has to be considered to develop the new strategies that can be used data in the published literatures. This study is to show whether or not the data set from the published experimental data has more value for modeling for MRA. To illustrate this suggestion, as example of data set, 4 published Salmonella survival in Cheddar cheese reports were used. Finally, using the GInaFiT tool, survival was modeled by nonlinear polynomial regression model describing the effect of temperature on Weibull model parameters. This model used data in the literatures is useful in describing behavior of Salmonella during different time and temperature conditions of cheese ripening.

• International Journal of Reliability and Applications
• /
• v.2 no.1
• /
• pp.1-26
• /
• 2001

The purpose of this paper is to introduce a proportional reversed hazard rate model, in contrast to the celebrated proportional hazard model, and study some of its structural properties. Some criteria of ageing are presented and the inheritance of the ageing notions (of the base line distribution) by the proposed model are studied. Two important data sets are analyzed: one uncensored and the other having some censored observations. In both cases, the confidence bands for the failure rate and survival function are investigated. In one case the failure rate is bathtub shaped and in the other it is upside bath tub shaped and thus the failure rates are non-monotonic even though the baseline failure rate is monotonic. In addition, the estimates of the turning points of the failure rates are provided.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.sup3
• /
• pp.5-10
• /
• 2016

Breast cancer, the second cause of cancer-related death after lung cancer and the most common cancer in women after skin cancer, is curable if detected in early stages of clinical presentation. Knowledge as to any age cut-off points which might have significance for prognostic groups is important in screening and treatment planning. Therefore, determining a change-point could improve resource allocation. This study aimed to determine if a change point for survival might exist in the age of breast cancer diagnosis. This study included 568 cases of breast cancer that were registered in Breast Cancer Research Center, Tehran, Iran, during the period 1986-2006 and were followed up to 2012. In the presence of curable cases of breast cancer, a change point in the age of breast cancer diagnosis was estimated using a mixture survival cure model. The data were analyzed using SPSS (versions 20) and R (version 2.15.0) software. The results revealed that a change point in the age of breast cancer diagnosis was at 50 years age. Based on our estimation, 35% of the patients diagnosed with breast cancer at age less than or equal to 50 years of age were cured while the figure was 57% for those diagnosed after 50 years of age. Those in the older age group had better survival compared to their younger counterparts during 12 years of follow up. Our results suggest that it is better to estimate change points in age for cancers which are curable in early stages using survival cure models, and that the cure rate would increase with timely screening for breast cancer.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.297-299
• /
• 2022

Malignant glioma has a poor prognosis with the reported median survival of between 6 months to 14 months. Thus, it is crucial to predict the accurate survival of patients with malignant glioma. In this paper, we propose a convolutional neural network to predict the overall survival and age of the patients. A total of four MRI modalities, T1, T1-contrast enhanced, T2, and fluid-attenuated inversion recovery, which effectively capture spatial characteristics of malignant glioma, were used as input images. Age is an important factor impacting the overall survival, thus incorporating it into the model will thereby improve the performance of the proposed model. Our model successfully predicted overall survival and age of the patients with pearson correlation coefficients of 0.1748 and 0.3056, respectively.

• Genomics & Informatics
• /
• v.8 no.4
• /
• pp.212-218
• /
• 2010

An accurate system for predicting the survival of patients with oral squamous cell carcinoma (OSCC) will be useful for selecting appropriate therapies. A nomogram for predicting survival was constructed from 96 patients with primary OSCC who underwent surgical resection between January 1994 and June 2003 at the Yonsei Dental Hospital in Seoul, Korea. We performed univariate and multivariate Cox regression to identify survival prognostic factors. For the early stage patients group, the nomogram was able to predict the 5 and 10 year survival from OSCC with a concordance index of 0.72. The total point assigned by the nomogram was a significant factor for predicting survival. This nomogram was able to accurately predict the survival after treatment of an individual patient with OSCC and may have practical utility for deciding adjuvant treatment.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.19
• /
• pp.8371-8376
• /
• 2014

Background: The use of statistical methods has become an imperative tool in breast cancer survival data analysis. The purpose of this study was to develop the best statistical probability model using the Bayesian method to predict future survival times for the black non-Hispanic female breast cancer patients diagnosed during 1973-2009 in the U.S. Materials and Methods: We used a stratified random sample of black non-Hispanic female breast cancer patient data from the Surveillance Epidemiology and End Results (SEER) database. Survival analysis was performed using Kaplan-Meier and Cox proportional regression methods. Four advanced types of statistical models, Exponentiated Exponential (EE), Beta Generalized Exponential (BGE), Exponentiated Weibull (EW), and Beta Inverse Weibull (BIW) were utilized for data analysis. The statistical model building criteria, Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were used to measure the goodness of fit tests. Furthermore, we used the Bayesian approach to obtain the predictive survival inferences from the best-fit data based on the exponentiated Weibull model. Results: We identified the highest number of black non-Hispanic female breast cancer patients in Michigan and the lowest in Hawaii. The mean (SD), of age at diagnosis (years) was 58.3 (14.43). The mean (SD), of survival time (months) for black non-Hispanic females was 66.8 (30.20). Non-Hispanic blacks had a significantly increased risk of death compared to Black Hispanics (Hazard ratio: 1.96, 95%CI: 1.51-2.54). Compared to other statistical probability models, we found that the exponentiated Weibull model better fits for the survival times. By making use of the Bayesian method predictive inferences for future survival times were obtained. Conclusions: These findings will be of great significance in determining appropriate treatment plans and health-care cost allocation. Furthermore, the same approach should contribute to build future predictive models for any health related diseases.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.9
• /
• pp.3923-3926
• /
• 2015

Background: Lung cancer is a fatal malignancy with high mortality and short survival time. The aim of this study was to estimate survival rates of Iranian patients with lung cancer and its associate predictive factors. Materials and Methods: The study was conducted on 355 patients admitted to hospitals of West Azerbaijan in the year 2007. The patients were followed up by phone calls until the end of June 2014. The survival rate was estimated using the Kaplan-Meier method and log-rank test for comparison. The Cox's proportional hazard model was used to investigate the effect of various variables on patient survival time, including age, sex, Eastern Cooperative Oncology Group (ECOG) performance, smoking status, tumor type, tumor stage, treatment, metastasis, and blood hemoglobin concentration. Results: Of the 355 patients under study, 240 died and 115 were censored. The mean and median survival time of patients was 13 and 4.8 months, respectively. According to the results of Kaplan-Meier method, 1, 2, and 3 years survival rates were 39%, 18%, and 0.07%, respectively. Based on Cox regression analysis, the risk of death was associated with ECOG group V (1.83, 95% CI: 1 Conclusions: The survival time of the patients with lung cancer is very short. While early diagnosis may improve the life expectancy effective treatment is not available.