• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5853-5858
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• 2015

Background: The survival rate reflecting prognosis of breast cancer patients is usually estimated based on crude survival methods such as observed and cause-specific. In situations where data are based on population-cancer registries, this method may produce biased estimations. This study therefore aimed to estimate the net survival of breast cancer based on relative survival. Materials and Methods: Data for 622 breast cancer patients diagnosed at the Iran Cancer Institute during 1990-95 and tracked till the end of 2000 were analyzed. For estimation of relative survival, Ederer's second method and SAS (9.1) and STATA (11) software were used. Results: Threeyear relative survivals of 85%, 90%, 80% and 67% were observed for age groups 15-44, 55-59, 60-74, and 75+years-old, respectively. A relative survival of approximately one was observed for two subsequent years for age-group 45-59 years-old. A value greater than one for two subsequent years of follow-up was observed in the age-group 60-74 years-old. Conclusions: Tracking the diagnosis of breast cancer, the relative survival decreases as we go to higher age-groups. It is also perceived that through follow-up, relative survival first decreased and then increased a little. The statistical cure point is acceptable for age group 45-59 years-old while for age-groups 15-44 and 60-74 years old is a sign of low quality data for some follow-up intervals.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1459-1473
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• 2016

Both longitudinal data and survival data are collected simultaneously in longitudinal data which are observed throughout the passage of time. In this case, the effect of the independent variable becomes biased (provided that sole use of longitudinal data analysis does not consider the relation between both data used) if the missing that occurred in the longitudinal data is non-ignorable because it is caused by a correlation with the survival data. A joint model of longitudinal data and survival data was studied as a solution for such problem in order to obtain an unbiased result by considering the survival model for the cause of missing. In this paper, a joint model of the longitudinal zero-inflated count data and survival data is studied by replacing the longitudinal part with zero-inflated count data. A hurdle model and proportional hazards model were used for each longitudinal zero inflated count data and survival data; in addition, both sub-models were linked based on the assumption that the random effect of sub-models follow the multivariate normal distribution. We used the EM algorithm for the maximum likelihood estimator of parameters and estimated standard errors of parameters were calculated using the profile likelihood method. In simulation, we observed a better performance of the joint model in bias and coverage probability compared to the separate model.

• Journal of Sport and Applied Science
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• v.6 no.3
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• pp.1-7
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• 2022

Purpose: The purpose of this study is to provide basic data for developing an educational program that can be practically applied in the field of survival swimming education. Research design, data, and methodology: This study reviewed prior literature including governmental reports, journal articles related to survival swimming programs at home and abroad. Based on the basic data collected through literature, domestic and foreign educational institutions and national educational programs were cataloged and analyzed. Results: The study found that among the goals of swimming education, the prevention of water accidents and the cultivation of water safety skills along with improving swimming ability are very important educational goals. Currently, domestic survival swimming education programs are divided into classes and training sessions, so it is necessary to develop an educational program according to each individual's swimming ability and a unified and systematic program through education for each level of survival swimming learners. Conclusions: It is thought that the reinforcement of the leader's capacity for quality improvement will have a positive effect for the development of survival swimming. Further implications were discussed.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.1
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• pp.159-163
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• 2016

Background: Colorectal cancer (CRC) is the third most common cancer in the world, and the fourth in Iran in both genders. The aim of this study was to find predictive factors for CRC survival. Materials and Methods: Medical records of 570 patients referred to the radiotherapy oncology department of Shiraz Namazi hospital from 2005 to 2010 were retrospectively analysed. Data were collected by reviewing medical records, and by telephone interviews with patients. Survival analysis was performed using the Cox's regression model with survival probability estimated with Kaplan-Meier curve. The log-rank test was used to compare survival between strata. Data was analyzed with Stata 12. Results: The five-year survival rate and the mean survival time after cancer diagnosis were 58.5% and $67{\pm}4months$. On multivariate analysis, age of diagnosis, disease stage and primary tumor site, lymphovascular invasion and type of treatment (in colon cancer) were significant factors for survival. Conclusions: Age of diagnosis and type of treatment (adjuvant therapy in patients with colon cancer) were two modifiable factors related to survival of CRC patients. Therefore earlier diagnosis might help increase survival.

• Journal of Gastric Cancer
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• v.14 no.4
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• pp.259-265
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• 2014

Purpose: Survival analysis of gastric cancer patients requires knowledge about factors that affect survival time. This paper attempted to analyze the survival of patients with incomplete registered data by using imputation methods. Materials and Methods: Three missing data imputation methods, including regression, expectation maximization algorithm, and multiple imputation (MI) using Monte Carlo Markov Chain methods, were applied to the data of cancer patients referred to the cancer institute at Imam Khomeini Hospital in Tehran in 2003 to 2008. The data included demographic variables, survival times, and censored variable of 471 patients with gastric cancer. After using imputation methods to account for missing covariate data, the data were analyzed using a Cox regression model and the results were compared. Results: The mean patient survival time after diagnosis was $49.1{\pm}4.4$ months. In the complete case analysis, which used information from 100 of the 471 patients, very wide and uninformative confidence intervals were obtained for the chemotherapy and surgery hazard ratios (HRs). However, after imputation, the maximum confidence interval widths for the chemotherapy and surgery HRs were 8.470 and 0.806, respectively. The minimum width corresponded with MI. Furthermore, the minimum Bayesian and Akaike information criteria values correlated with MI (-821.236 and -827.866, respectively). Conclusions: Missing value imputation increased the estimate precision and accuracy. In addition, MI yielded better results when compared with the expectation maximization algorithm and regression simple imputation methods.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.871-881
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• 2011

In this paper we propose a logistic regression method to estimate the survival function and the median survival time in interval-censored data. The proposed method is motivated by the data augmentation technique with no sacrifice in augmenting data. In addition, we develop a cross validation criterion to determine the size of data augmentation. We compare the proposed estimator with other existing methods such as the parametric method, the single point imputation method, and the nonparametric maximum likelihood estimator through extensive numerical studies to show that the proposed estimator performs better than others in the sense of the mean squared error. An illustrative example based on a real data set is given.

• Journal of the Korean Statistical Society
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• v.26 no.2
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• pp.277-288
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• 1997

For the analysis of survival data including covariates whose effects vary in time, the multiprocess discount survival model is proposed. The parameter vector modeling the time-varying effects of covariates is to vary between time intervals and its evolution between time intervals depends on the perturbation of the next time interval. The recursive estimation of the parameter vector can be obtained at the end of each time interval. The retrospective estimation of the survival function and the forecasting of the survival function of individuals of the specific covariates also can be obtained based on the information gathered until the end of the time interval.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.200-201
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• 2005

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4387-4391
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• 2012

Background: Lung carcinoma is the leading cause of cancer mortality worldwide. Although the 5-year survival rate nearly tripled from 5-15% over the last 25 years, the estimated number of deaths still exceeds 1.3 million annually. The overall 5-year survival of lung cancer is only 10% in Europe and 15% in the United States. The aim of the current study was to determine the long-term survival and the effect of certain prognostic factors on survival of patients with lung cancer in Yazd city, Iran. Methods: In this cross-sectional descriptive study, we retrospectively reviewed hospital records and follow-up data of 148 patients with histological proven lung cancer using the cancer data registered between 1998 and 2005 in the pathology department of Shahid Sadoughi educational hospital, Yazd, Iran. Data were extracted from patient documents that included sex, age, clinical manifestations, histopathological report of the tumor and type of treatment given. Results: Overall survival time in all patients was 8.5 months after diagnosis and there was no significant difference in survival according to sex (p=0.958). Histological analysis revealed that squamous cell carcinoma was the most common histologic type (35%). Kaplan-Meier statistical methods estimated the average survival time for SCC to be better (22.6 months) in comparison with the other types of histology (all of them below 10 months). There was a trend towards significance between type of histology and duration of survival (p=0.08). Conclusion: It is reasonable to expect that early lung cancer detection, and appropriated treatment, may improve surgical morbidity and mortality. Low survival of lung cancer in our center patients show our shortages in screening programs for early diagnosis. Designing studies with larger sample size that take some other variables like staging of patients is now necessary.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.747-756
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• 2010

With the recent recession, studies on the economy are actively being conducted throughout the industry. Based on the Small Business data registered in the Credit Guarantee Fund, we estimated the survival probability in the context of the survival analysis. We also analyzed the survival time for the construction and the other industries which are distinguished depending on the types of business and assets in the Small Business. The survival probability was estimated by using the life-table and the difference between the survival probabilities for the different types of business was described via the method of the Log-rank test and the Wilcoxon test. We found that the small business with over one billion asset has the highest survival probability and that with less than 1000 million asset showed the similar survival probability. In terms of types of business Wholesale and Retail trade industry and Services were relatively high in the survival probability than Light, Heavy, and the construction industries. Especially the construction industry showed the lowest survival probability. Most of the Small Business tend to increase in the hazard rate over time.