• Tuberculosis and Respiratory Diseases
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• v.67 no.2
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• pp.121-126
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• 2009

Background: Pemetrexed has been prescribed newly as a second line chemotherapy in advanced non-small cell lung carcinoma (NSCLC). The aim of study was to determine the efficacy and toxicity of pemetrexed in advanced NSCLC. Methods: Patients with histologically or cytologically confirmed NSCLC were evaluated from June 2006 to December 2008. The patients had relapsed or progressed after prior chemotherapy treatment. They were treated with intravenous pemetrexed $500mg/m^2$ for 10 min on Day 1 of each 21-day cycle. Results: A total of 89 patients were eligible for analysis. The response rate and disease control rate were 11% and 66%. Non-squamous cell carcinoma histology was significantly associated with a superior response rate (p=0.035) and disease control rate (p=0.009) than squamous cell carcinoma histology. The median survival time was 13 months and the median progression free survival time was 2.3 months. The median survival time of patients with ECOG PS 0~1 was 13.2 months, whereas median survival time was 11.6 months for patients with PS 2 (p=0.002). The median progression free survival time of patients with PS 0~1 were 3.8 months, but 2.1 months for patients with PS 2 (p=0.016). The median progression free survival time of smokers with non-squamous cell carcinoma was 3.4 months, which was significant (p=0.014). Grade 3~4 neutropenia were seen in 7.9% patients. Conclusion: Pemetrexed has efficacy in patients who had prior chemotherapy with advanced NSCLC and less hematologic toxicity.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.35-47
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• 2022

The receiver operating characteristic (ROC) curve was developed to quantify the classification ability of marker values (covariates) on the response variable and has been extended to survival data with diverse missing data structure. When survival data is understood as binary data (status of being alive or dead) at each time point, the ROC curve expressed at every time point results in time-dependent ROC curve and time-dependent area under curve (AUC). In particular, a follow-up study brings the change of cohort and incomplete data structures such as censoring and competing risk. In this paper, we review time-dependent ROC estimators under several contexts and perform simulation to check the performance of each estimators. We analyzed a dementia dataset to compare the prognostic power of markers.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup3
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• pp.311-316
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• 2016

Breast cancer is one of the most common causes of cancer mortality in Iran. Social determinants of health are among the key factors affecting the pathogenesis of diseases. This cross-sectional study aimed to determine the social determinants of breast cancer survival time with parametric and semi-parametric regression models. It was conducted on male and female patients diagnosed with breast cancer presenting to the Cancer Research Center of Shohada-E-Tajrish Hospital from 2006 to 2010. The Cox proportional hazard model and parametric models including the Weibull, log normal and log-logistic models were applied to determine the social determinants of survival time of breast cancer patients. The Akaike information criterion (AIC) was used to assess the best fit. Statistical analysis was performed with STATA (version 11) software. This study was performed on 797 breast cancer patients, aged 25-93 years with a mean age of 54.7 (${\pm}11.9$) years. In both semi-parametric and parametric models, the three-year survival was related to level of education and municipal district of residence (P<0.05). The AIC suggested that log normal distribution was the best fit for the three-year survival time of breast cancer patients. Social determinants of health such as level of education and municipal district of residence affect the survival of breast cancer cases. Future studies must focus on the effect of childhood social class on the survival times of cancers, which have hitherto only been paid limited attention.

• Journal of Chest Surgery
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• v.33 no.4
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• pp.301-305
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• 2000

Background: The surgical indications of stage IV non-small cell lung cancer(NSCLC) are extremely limited with its controversial results. We analyzed the surgical results and survival in selected patients with resectable stage IV NSCLC. Material and Method: We reviewed the medical records of 21 patients who underwent operation for stage IV NSCLC from Jan. 1992 to Sep. 1999. Result: The mean age of patients was 55.6 years(range: 35 to 78). Sixteen were men and 5 were women. Tissue types were squamous cell carcinoma in 10(45.5%), adenocarcinoma in 9(40.9%), large cell carcinoma in 1 and carcinosarcoma in 1. Distant metastatic lesions were ipsilateral other lobe of lung in 18, brain in 2 and adrenal gland in 1. Pneumonectomy was performed in 16 patients, bilobectomy in 3, and lobectomy in 2 who underwent previous operatin for brain metastasis. Mean follow-up duration was 21.2$\pm$17.7 months. During follow-up period, 13 patients died. Three-and 5-year survival of patients were 38.0% and 19.0%, the median survival time was 19.1$\pm$7.8 months. In the group with ipsilateral pulonary metastasis(PM, n=18), 3- and 5-year survival of patients with N0 and N1(n=9) disease were 64.8% and 32.4%, median survival time was 55.3$\pm$27.2 months. Three-year survival of patients with N2(n=9) disease was 11.1%, median survival time was 10.6$\pm$0.3 months. The survival of N0 and N1 disease group was significantly better than that of N2 disease group(p=0.042). Also the disease free survival of N0 and N1 was significantly better than that of N2 disease in overall group(53.3 months vs 12.1 months, p=0.036) and ipsilateral PM group(63.4 months vs 8.8 months, p=0.001). Conclusion: We suggest that surgical treatment is worthful modality in well selected patients with stage IV NHSCLC especially with ipsilateral PM and N0 or N1 disease,. Nevertheless our study indicate questions that will need to be experienced further in larger studies.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.1
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• pp.94-106
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• 2024

In this study, we conducted a survival analysis on battalion-level commanders(leaders), focusing on infantry battalion defensive operations using the big data of brigade-level KCTC(Korea Combat Training Center) training results. Unlike previous studies, we utilized the brigade-level KCTC training results data for the first time to conduct a survival analysis, and the research subjects were battalion-level commanders(leaders), which can affect the battle. At this time, the battle results were defined, and through cluster analysis, infantry battalions were divided into excellent, average, and insufficient units, and the difference in the survival rate of the commanders was analyzed through the Kaplan-Meier survival analysis. This provided an opportunity to objectively compare the differences between excellent and insufficient units. Subsequently, factors affecting the survival of commanders were derived using the Cox proportional hazard model, and it was possible to confirm the influencing factors from various angles by also using the survival tree model. Significance and limitations confirmed in the research process were presented as policy suggestions and future research directions.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1623-1627
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• 2013

Cholangiocarcinoma (CCA) is a major health problem and cause of death among people in Northeastern Thailand. In this prospective study 171 patients newly diagnosed with CCA by physicians in 5 tertiary hospitals in four provinces of northeastern of Thailand between February and July 2011 were followed up to January 2012. The outcome was survival time from diagnosis to death. A total of 758.4 person-months of follow-up were available. The mortality rate was 16.9 per 100 person-months (95%CI: 14.1-20.1). The median survival time among CCA patients was 4.3 months (95%CI: 3.3-5.1). Cox's proportional hazard model was used to study the independent effects of factors affecting survival time among patients. Statistically significant factors included advanced stage at diagnosis (HR: 2.5, 95%CI: 1.7-3.8), presentation with jaundice (HR: 1.7, 95%CI: 1.1-2.4) or ascites (HR: 2.8, 95%CI: 1.8-4.4), and positive serum carcinoembryonic antigen (HR: 2.3, 95%CI: 1.2-4.3). Patients who had received standard treatment had a better prognosis that those who did not (HR: 0.5, 95%CI: 0.3-0.7).

• Journal of Korean Neurosurgical Society
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• v.60 no.6
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• pp.661-666
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• 2017

Objective : Atypical meningioma is rare tumor and there is no accurate guide line for optimal treatment. This retrospective study analyzed the prognostic factors, the effect of different methods of treatments and the behavior of atypical meningioma. Methods : Thirty six patients were diagnosed as atypical meningioma, among 273 patients who were given a diagnosis of meningioma in the period of 2002 to 2015. Age, gender, tumor location, Ki 67, Simpson grade and treatment received were analyzed. We studied the correlation between these factors with recurrence, overall survival rate and progression free survival. Results : Median overall survival time and progression free survival time are 60 and 53 (months). Better survival rate was observed for patients less than 50 years old but with no statistical significance (p=0.322). And patients with total resection compared with subtotal resection also showed better survival rate but no statistical significance (p=0.744). Patients with a tumor located in skull base compared with patients with a tumor located in brain convexity and parasagittal showed better progression free survival (p=0.048). Total resection is associated with longer progression-free survival than incomplete resection (p=0.018). Conclusion : We confirmed that Simpson grade was significant factor for statistically affect to progression free survival in univariate analysis. In case of skull base atypical tumor, it is analyzed that it has more recurrence than tumor located elsewhere. Overall survival was not affected statistically by patient age, gender, tumor location, Ki 67, Simpson grade and treatment received in this study.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.7
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• pp.3067-3072
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• 2015

Cervical cancer is the most common malignant cancer of the female reproductive organs worldwide. Currently, cervical cancer can be prevented by vaccination and detected at an early stage via various screening methods. Malaysia, as a developing country faces a heavy disease burden of cervical cancer as it is the second most common cancer among Malaysian women. This population based study was carried out to fulfil the primary aim of determining the survival rates of Malaysian women with cervical cancer and associated factors. Data were obtained from two different sources namely, the Malaysian National Cancer Registry (MNCR) and National Health Informatics Centre (NHIC) from 1st January 2000 to 31st December 2005. Kaplan Meier analyses were conducted to identify the overall survival rates and median survival time. Differences in survival among different ethnic and age group were compared using the log-rank test. A total of 5,859 patients were included. The median survival time for cervical cancer in this study was 65.8 months and the 5-year survival rate was 71.1%. The overall observed survival rates at 1, 3 and 5 years were 94.1%, 79.3% and 71.1% respectively. The log-rank test finding also showed that there were significant differences in the 5-year survival rate among different ethnic groups. Malays had the lowest survival rate of 59.2% followed by Indians (69.5%) and Chinese (73.8%). The overall 5-year survival rate among patients with cervical cancer in Malaysia is relatively good. Age and ethnic groups remain as significant determining factors for cervical cancer survival rate.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5571-5575
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• 2014

Background: The ability to predict the survival time of breast cancer patients is important because of the potential high morbidity and mortality associated with the disease. To develop a predictive inference for determining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we propose the development of a databased statistical probability model and application of the Bayesian method to predict future survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009. Materials and Methods: A stratified random sample of White Hispanic female patient survival data was selected from the Surveillance Epidemiology and End Results (SEER) database to derive statistical probability models. Four were considered to identify the best-fit model. We used three standard model-building criteria, which included Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. Furthermore, the Bayesian method was used to derive future survival inferences for survival times. Results: The highest number of White Hispanic female breast cancer patients in this sample was from New Mexico and the lowest from Hawaii. The mean (SD) age at diagnosis (years) was 58.2 (14.2). The mean (SD) of survival time (months) for White Hispanic females was 72.7 (32.2). We found that the exponentiated Weibull model best fit the survival times compared to other widely known statistical probability models. The predictive inference for future survival times is presented using the Bayesian method. Conclusions: The findings are significant for treatment planning and health-care cost allocation. They should also contribute to further research on breast cancer survival issues.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.4049-4054
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• 2014

Background: Race and ethnicity are significant factors in predicting survival time of breast cancer patients. In this study, we applied advanced statistical methods to predict the survival of White non-Hispanic female breast cancer patients, who were diagnosed between the years 1973 and 2009 in the United States (U.S.). Materials and Methods: Demographic data from the Surveillance Epidemiology and End Results (SEER) database were used for the purpose of this study. Nine states were randomly selected from 12 U.S. cancer registries. A stratified random sampling method was used to select 2,000 female breast cancer patients from these nine states. We compared four types of advanced statistical probability models to identify the best-fit model for the White non-Hispanic female breast cancer survival data. Three model building criterion were used to measure and compare goodness of fit of the models. These include Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC). In addition, we used a novel Bayesian method and the Markov Chain Monte Carlo technique to determine the posterior density function of the parameters. After evaluating the model parameters, we selected the model having the lowest DIC value. Using this Bayesian method, we derived the predictive survival density for future survival time and its related inferences. Results: The analytical sample of White non-Hispanic women included 2,000 breast cancer cases from the SEER database (1973-2009). The majority of cases were married (55.2%), the mean age of diagnosis was 63.61 years (SD = 14.24) and the mean survival time was 84 months (SD = 35.01). After comparing the four statistical models, results suggested that the exponentiated Weibull model (DIC= 19818.220) was a better fit for White non-Hispanic females' breast cancer survival data. This model predicted the survival times (in months) for White non-Hispanic women after implementation of precise estimates of the model parameters. Conclusions: By using modern model building criteria, we determined that the data best fit the exponentiated Weibull model. We incorporated precise estimates of the parameter into the predictive model and evaluated the survival inference for the White non-Hispanic female population. This method of analysis will assist researchers in making scientific and clinical conclusions when assessing survival time of breast cancer patients.