• Genomics & Informatics
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• v.17 no.4
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• pp.41.1-41.12
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• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

• Journal of Information Technology Services
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• v.18 no.4
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• pp.151-170
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• 2019

This study conducted an empirical analysis through the Kaplan-Meier method, which is mainly used for clinical experiment analysis, on the survival rate and the survival duration of small and medium-sized enterprises using B2B e-commerce guarantee provided by credit guarantee institutions for activating B2B e-commerce transactions. The variables presented in this study are analyzed by the subdivision of the survival characteristics of enterprises using B2B e-commerce guaranteee by year, enterprises attribute, representative attribute, and guarantee use amount based on the variables tested through previous studies. According to the empirical analysis, SMEs using B2B e-commerce guarantees have a higher survival rate compared to general enterprises Simply by year and have a variety of survival characteristics, and most of the variables have a significant effect except for some variables. The implication of this study is that the researches conducted on enterprises participating in B2B e-commerce for a long period of time to support the establishment of stable business environment of SMEs and the results of empirical analysis on the survival characteristics are useful information to the stakeholders of B2B e-commerce And it can contribute to enhance the survival rate of related enterprises.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5655-5661
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• 2015

Purpose: To assess the role of two adjuvant chemotherapy regimens, anthracycline-based and CMF on disease free survival and overall survival breast cancer patients by meta-analysis approach in Eastern Mediterranean and Asian countries to determine which is more effective and evaluate the appropriateness and efficiency of two different proposed statistical models. Materials and Methods: Survival curves were digitized and the survival proportions and times were extracted and modeled to appropriate covariates by two multivariate mixed effects models. Studies which reported disease free survival and overall survival curves for anthracycline-based or CMF as adjuvant chemotherapy that were published in English in the Eastern Mediterranean region and Asia were included in this systematic review. The two transformations of survival probabilities (Ln (-Ln(S)) and Ln(S/ (1-S))) as dependent variables were modeled by a multivariate mixed model to same covariates in order to have precise estimations with high power and appropriate interpretation of covariate effects. The analysis was carried out with SAS Proc MIXED and STATA software. Results: A total of 32 studies from the published literature were analysed, covering 4,092 patients who received anthracycline-based and 2,501 treated with CMF for the disease free survival and in order to analyze the overall survival, 13 studies reported the overall survival curves in which 2,050 cases were treated with anthracycline-based and 1,282 with CMF regimens. Conclusions: The findings illustrated that the model with dependent variable Ln (-Ln(S)) had more precise estimations of the covariate effects and showed significant difference between the effects of two adjuvant chemotherapy regimens. Anthracycline-based treatment gave better disease free survival and overall survival. As an IPD meta-analysis in the Italy the results of Angelo et al in 2011 also confirmed that anthracycline-based regimens were more effective for survival of breast cancer patients. The findings of Zare et al 2012 on disease free survival curves in Asia also provided similar evidence.

• Knowledge Management Research
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• v.11 no.2
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• pp.95-109
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• 2010

The objectives of this study are to identify the survival function (hazard function) of small and medium enterprises by using technology rating data for the companies guaranteed by Korea Technology Finance Corporation (KOTEC), and to figure out the factors that affects their survival. To serve the purposes, this study uses Kaplan-Meier Analysis as a non-parametric method and Cox proportional hazards model as a semi-parametric one. The 17,396 guaranteed companies that assessed from July 1st in 2005 to December 31st in 2009 are selected as samples (16,504 censored data and 829 accident data). The survival time is computed with random censoring (Type III) from July in 2005 as a starting point. The results of the analysis show that Kaplan-Meier Analysis and Cox proportional hazards model are able to readily estimate survival and hazard function and to perform comparative study among group variables such as industry and technology rating level. In particular, Cox proportional hazards model is recognized that it is useful to understand which technology rating items are meaningful to company's survival and how much they affect it. It is considered that these results will provide valuable knowledge for practitioners to find and manage the significant items for survival of the guaranteed companies through future technology rating.

• 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.17 no.7
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• pp.3575-3581
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• 2016

Background: Cancer survival analysis is an essential indicator for effective early detection and improvements in cancer treatment. This study was undertaken to document colorectal cancer survival and associated prognostic factors in Malaysians. Materials and Methods: All data were retrieved from the National Cancer Patient Registry-Colorectal Cancer. Only cases with confirmed diagnosis through histology between the year 2008 and 2009 were included. Retrieved data include socio-demographic information, pathological features and treatment received. Survival curves were plotted using the Kaplan-Meier method. Univariate analysis of all variables was then made using the Log-rank test. All significant factors that influenced survival of patients were further analysed in a multivariate analysis using Cox' regression. Results: Total of 1,214 patients were included in the study. The overall 3- and 5-year survival rates were 59.1% and 48.7%, respectively. Patients with localized tumours had better prognosis compared to those with advanced stage cancer. In univariate analysis, staging at diagnosis (p<0.001), primary tumour size (p<0.001), involvement of lymph nodes (p<0.001) and treatment modalities (p=0.001) were found to be predictors of survival. None of the socio-demographic characteristics were found to exert any influence. In Cox regression analysis, staging at diagnosis (p<0.001), primary tumour size (p<0.001), involvement of lymph nodes (p<0.001) and treatment modalities (p<0.001) were determined as independent prognostic factors of survival after adjusted for age, gender and ethnicity. Conclusions: The overall survival rate for colorectal cancer patients in Malaysia is similar to those in other Asian countries, with staging at diagnosis, primary tumor size, involvement of lymph node and treatment modalities having significant effects. More efforts are needed to improve national survival rates in future.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.7
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• pp.3357-3360
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• 2012

Objective: This study was to present the survival of advanced cancer patients and explore the influence of various factors on survival time as well as survival rate. The results provide guidelines for clinical practice of cancer treatment. Methods: Follow-up of 674 advanced cancer patients was performed in a hospice. The median survival time and survival rate were calculated, and survival analysis was carried out. Results: The median survival time of all patients dying from cancer was 12.0 months and the average survival time was 25.1 months. The 1-year cumulative survival rate was $0.518{\pm}0.020$ and the 5-year cumulative survival rate was $0.088{\pm}0.012$. The following factors showed significant impacts on survival rate: gender, age, primary diagnosis, surgery and the time when pain appeared. Conclusions: The survival time of patients with advanced cancer was relatively short. Major approaches to extend the survival time include early detection, early diagnosis, effective surgical treatment, pain control, reasonable supply of nutrients and multiple interventions.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.2013-2017
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• 2013

Background: To compare the effects of two adjuvant chemotherapy regimens, anthracycline-based and cyclophosphamide, methotrexate, fluorourical (CMF) on disease free survival for breast cancer patients in the Eastern Mediterranean region and Asia. Methods: In a systematic review with a multivariate mixed model meta-analysis, the reported survival proportion at multiple time points in different studies were combined. Our data sources were studies linking the two chemotherapy regimens on an adjuvant basis with disease free survival published in English and Persian in the Eastern Mediterranean region and Asia. All survival curves were generated with Graphdigitizer software. Results: 14 retrospective cohort studies were located from electronic databases. We analyzed data for 1,086 patients who received anthracycline-based treatment and 1,109 given CMF treatment. For determination of survival proportions and time we usesb the transformation Ln (-Ln(S)) and Ln (time) to make precise estimations and then fit the model. All analyses were carried out with STATA software. Conclusions: Our findings showed a significant efficacy of anthracycline-based adjuvant therapy regarding disease free survival of breast cancer. As a limitation in this meta-analysis we used studies with different types of anthracycline-based regimens.

• Annals of dermatology
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• v.30 no.6
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• pp.668-675
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• 2018

Background: Drug survival, defined as the time until discontinuation, is a parameter reflecting real-world therapeutic effectiveness. Few studies have examined the influence of economic factors on the drug survival of biologic agents for psoriasis, particularly in Asian countries. Objective: To determine the drug survival for ustekinumab in real-life settings and investigate the factors affecting drug survival for psoriasis patients in Korea. Methods: We evaluated 98 psoriasis patients who were treated with ustekinumab at a single center. We analyzed the efficacy and drug survival of ustekinumab. Cox proportional hazard analysis and competing risk regression analysis were performed to reveal the factors affecting the drug survival of ustekinumab. Results: The overall mean drug survival was 1,596 days (95% confidence interval [CI], 904~2,288). Among the 39 cessations of ustekinumab treatment, 9 (23.1%) patients discontinued treatment after experiencing satisfactory results. Multivariate Cox proportional hazard analysis revealed that paying on patients' own expense was the major predictor for the discontinuation of ustekinumab (hazard ratio [HR], 9.696; 95% CI, 4.088~22.998). Competing risk regression analysis modeling of discontinuation because of factors other than satisfaction of an event also revealed that ustekinumab treatment at the patient's expense (HR, 4.138; 95% CI, 1.684~10.168) was a predictor of discontinuation rather than satisfaction. Conclusion: The results of our study revealed that the cost of biologics treatment affects the drug survival of ustekinumab and suggested that economic factors affect the drug survival of ustekinumab treatment in Korea.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.11
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• pp.4691-4698
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• 2015

Background: The tumor suppressor phosphatase and tensin homolog (PTEN) is an important negative regulator of cell-survival signaling. However, available results for the prognostic value of PTEN expression in patients with cancer remain controversial. Therefore, a meta-analysis of published studies investigating this issue was performed. Materials and Methods: A literature search via PubMed and EMBASE databases was conducted. Statistical analysis was performed by using the STATA 12.0 (STATA Corp., College, TX). Data from eligible studies were extracted and included into the meta-analysis using a random effects model. Results: A total of 3,810 patients from 27 studies were included in the meta-analysis, 22 investigating the relationship between PTEN expression and overall survival (OS) using univariate analysis, and nine with multivariate analysis. The pooled hazard ratio (HR) for OS was 1.64 (95% confidence interval (CI): 1.32-2.05) by univariate analysis and 1.56 (95% CI: 1.20-2.03) by multivariate analysis. In addition, eight papers including two disease-free-survival analyses (DFSs), four relapse-free-survival analyses (RFSs), three progression-free-survival analyses (PFSs) and one metastasis-free-survival analysis (MFS) reported the effect of PTEN on survival. The results showed that loss of PTEN expression was significant correlated with poor prognosis, with a combined HR of 1.74 (95% CI: 1.24-2.44). Furthermore, in the stratified analysis by the year of publication, ethnicity, cancer type, method, cut-off value, median follow-up time and neoadjuvant therapy in which the study was conducted, we found that the ethnicity, cancer type, method, median follow-up time and neoadjuvant therapy are associated with prognosis. Conclusions: Our study shows that negative or loss of expression of PTEN is associated with worse prognosis in patients with cancer. However, adequately designed prospective studies need to be performed for confirmation.