• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7117-7120
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• 2013

Background: This study hypothesized living in a poor neighborhood decreased the cause specific survival in individuals suffering from carcinoid carcinomas. Surveillance, Epidemiology and End Results (SEER) carcinoid carcinoma data were used to identify potential socioeconomic disparities in outcome. Materials and Methods: This study analyzed socioeconomic, staging and treatment factors available in the SEER database for carcinoid carcinomas. The Kaplan-Meier method was used to analyze time to events and the Kolmogorov-Smirnov test to compare survival curves. The Cox proportional hazard method was employed for multivariate analysis. Areas under the receiver operating characteristic curves (ROCs) were computed to screen the predictors for further analysis. Results: There were 38,546 patients diagnosed from 1973 to 2009 included in this study. The mean follow up time (S.D.) was 68.1 (70.7) months. SEER stage was the most predictive factor of outcome (ROC area of 0.79). 16.4% of patients were un-staged. Race/ethnicity, rural urban residence and county level family income were significant predictors of cause specific survival on multivariate analysis, these accounting for about 5% of the difference in actuarial cause specific survival at 20 years of follow up. Conclusions: This study found poorer cause specific survival of carcinoid carcinomas of individuals living in poor and rural neighborhoods.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.39-52
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• 2007

Crossover trials of new drugs in the treatment of angina pectoris, which frequently use treadmill exercise test for the assessment of its efficacy, produce censored survival times. In this paper we consider analysis approaches for censored survival times from crossover trials. Previously, a stratified Cox model for paired observation and nonparametric methods have been presented as possible analysis methods. On the other hand, the differences of two survival times would produce interval-censored survival times and we propose a Cox model for interval-censored data as n alternative analysis method. Example data is analyzed in order to compare these different methods.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.10
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• pp.4615-4621
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• 2016

Background: There has not been a general estimation about survival rates of breast cancer cases in Iran. Therefore, the present study aimed to assess survival using a meta-analysis. Materials and Methods: International credible databases such as Scopus, Web of Science, PubMed, Science direct and Google Scholar and Iranian databases such as Magiran, Irandoc and SID, from 1997 to 2015 were searched. All articles covering survival rate of breast cancer were entered into the study without any limits. Quality assessment of the articles and data extraction were performed by two researchers using the modified STROBE checklist, which includes 12 questions. Articles with scores greater than 8 were included in the analysis. A limitation of this meta-analysis was different methods for presenting of results in the papers surveyed. Results: A total of 21 articles with a sample of 12,195 people were analyzed. The one-year, three-year, five-year and ten-year survival rates of breast cancer in Iran were estimated to be 95.8% (94.6-97.0), 82.4% (79.0-85.8), 69.5% (64.5-74.5), 58.1% (39.6-76.6), respectively. The most important factors affecting survival of breast cancer were age, number of lymph nodes involved, size of the tumor and the stage of the disease. Conclusion: The five- and ten- year survival rates in Iran are lower than in developed countries. Conducting breast cancer screening plan support (including regular clinical examination, mammography), public training and raising awareness should be helpful in facilitating early diagnosis and increasing survival rates for Iranian women.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.8
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• pp.3917-3921
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• 2016

Background: The association between calcium channel blockers (CCBs) and survival in cancer patients remains unclear and the results of related studies are conflicting. The objective of the study was to investigate the association between calcium channel blockers (CCBs) use and survival in cancer patients. Materials and Methods: We searched PubMed, EMBASE, Web of Science and Cochrane Library for studies published before January 2016 with the terms related to CCBs and survival in cancer patients. The information was reviewed and extracted by two evaluators independently. Data of publications was extracted and calculated into hazard ratios (HRs) for overall survival (OS). Statistical analysis was performed by using Review Manager 5.3. Results: There were 11 studies included in our meta-analysis. Analysis of all studies showed that CCBs use was not associated with survival in cancer patients (HR=1.07; 95% CI: 0.91-1.25; P=0.42). No association between CCBs use and overall survival in cancer patients was existed whether in Asian (HR=1.18, 95% CI: 0.72-1.93; P=0.52) or Caucasian population (HR=1.03, 95% CI: 0.89-1.20; P=0.66). Conclusions: There is no evidence that CCBs use is associated with a better or worse outcome of survival in cancer patients.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.1
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• pp.95-100
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• 2016

Background: Survival time of lymphoma patients can be estimated with the help of microarray technology. In this study, with the use of iterative Bayesian Model Averaging (BMA) method, survival time of Mantle Cell Lymphoma patients (MCL) was estimated and in reference to the findings, patients were divided into two high-risk and low-risk groups. Materials and Methods: In this study, gene expression data of MCL patients were used in order to select a subset of genes for survival analysis with microarray data, using the iterative BMA method. To evaluate the performance of the method, patients were divided into high-risk and low-risk based on their scores. Performance prediction was investigated using the log-rank test. The bioconductor package "iterativeBMAsurv" was applied with R statistical software for classification and survival analysis. Results: In this study, 25 genes associated with survival for MCL patients were identified across 132 selected models. The maximum likelihood estimate coefficients of the selected genes and the posterior probabilities of the selected models were obtained from training data. Using this method, patients could be separated into high-risk and low-risk groups with high significance (p<0.001). Conclusions: The iterative BMA algorithm has high precision and ability for survival analysis. This method is capable of identifying a few predictive variables associated with survival, among many variables in a set of microarray data. Therefore, it can be used as a low-cost diagnostic tool in clinical research.

• Journal of Gastric Cancer
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• v.17 no.4
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• pp.319-330
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• 2017

Introduction: The extent of lymphadenectomy in the surgical treatment of gastric cancer is a topic of controversy among surgeons. This study was conducted to analyze the American National Cancer Database (NCDB) and conclude the optimal extent of lymphadenectomy for gastric adenocarcinoma. Methods: The NCDB for gastric cancer was utilized. Patients who received at least a partial gastrectomy were included. Patients with metastatic disease, unknown TNM stages, R1/R2 resection, or treated with a palliative intent were excluded. Joinpoint regression was used to identify the extent of lymphadenectomy that reflects the optimal survival. Cox regression analysis and Bayesian information criterion were used to identify significant survival predictors. Kaplan-Meier was applied to study overall survival and stage migration. Results: 40,281 patients of 168,377 met the inclusion criteria. Joinpoint analysis showed that dissection of 29 nodes provides the optimal median survival for the overall population. Regression analysis reported the cutoff ${\geq}29$ to have a better fit in the prognostic model than that of ${\geq}15$. Dissection of ${\geq}29$ nodes in the higher stages provides a comparable overall survival to the immediately lower stage. Nonetheless, the retrieval of ${\geq}15$ nodes proved to be adequate for staging without a significant stage migration compared to ${\geq}29$ nodes. Conclusion: The extent of lymphadenectomy in gastric adenocarcinoma is a marker of improved resection which reflects in a longer overall survival. Our analysis concludes that the dissection of ${\geq}15$ nodes is adequate for staging. However, the dissection of 29 nodes might be needed to provide a significantly improved survival.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.51-69
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• 2014

Times to multiple events (TMEs) are a major data type in large-scale business and medical data. Despite its importance, the analysis of TME data has not been well studied because of the analysis difficulty from censoring of observation. To address this difficulty, we have developed a Bayesian-based multivariate survival analysis method, which can successfully estimate the joint probability density of survival times. In this work, we extended this method for the analysis of precedence, dependency and causality among multiple events. We applied this method to the electronic health records of 2,111 patients in a children's hospital in the US and the proposed analysis successfully shows the relation between times to two types of hospital visits for different medical issues. The overall result implies the usefulness of the multivariate survival analysis method in large-scale big data in a variety of areas including marketing, human resources, and e-commerce. Lastly, we suggest our future research directions based multivariate survival analysis method.

• Journal of the Korean Data and Information Science Society
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• v.7 no.2
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• pp.227-234
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• 1996

The discount survival model is proposed for the application of the Cox model on the analysis of survival data with time-varying effects of covariates. Algorithms for the recursive estimation of the parameter vector and the retrospective estimation of the survival function are suggested. Also the algorithm of forecasting of the survival function of individuals of specific covariates in the next time interval based on the information gathered until the end of a certain time interval is suggested.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.111-122
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• 2023

The purpose of this study is to verify the factors affecting survival time by estimating survival rate and survival time using non-financial information of social enterprises using credit guarantee in credit guarantee institutions, and provide information to stakeholders to improve survival rate and employ to contribute to maintaining and expanding the As a research method, survival analysis was performed using a non-parametric analysis method, Kaplan-Meier Analysis. As a sample, 621 companies (577 normal companies, 44 insolvent companies) established between 2009 and 2018 were selected as the target companies. As a result of examining the factors affecting survival time by classifying social enterprise representative information and corporate information, representative credit rating, representative home ownership, credit transaction period, and corporate credit rating were derived as significant variables affecting survival time. In the future, financial institutions will be able to induce corporate soundness by reflecting factors that affect survival when examining loans for social enterprises, contributing to job retention and reduction of social costs. Supporting organizations such as the government and private organizations will be able to use it in various ways, such as policy establishment and education and training for the growth and sustainability of social enterprises. With this study as an opportunity, I hope that research will continue with more interest in the factors influencing social enterprise performance as well as corporate insolvency.