• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.179-185
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• 1994

To test the equality of survival distributions in the presence of arbitrary right censorship, the choice of weights which are functions of the number of individuals at risk at the time of each death is very important in increasing the power of the test. In this paper a weight by probit scale is derived and the efficiencies relative to the other weight's are also investigated.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.933-939
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• 2009

The bivariate data in clinical research fields often has two types of failure times, which are mark variable for the first failure time and the final failure time. This paper showed how to generate bootstrap data to get Bayesian estimation for the joint distribution of bivariate survival times. The observed data was generated by Frank's family and the fake date is simulated with the Gamma prior of survival time. The bootstrap data was obtained by combining the mimic data with the observed data and the simulated fake data from the observed data.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.283-290
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• 2014

In the flat panel display industry, to meet production target quantities and the deadline of production, the scheduler and dispatching systems are major production management systems which control the order of facility production and the distribution of WIP (Work In Process). Especially the delivery time is a key factor of the dispatching system for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors of the delivery time and to build the delivery time forecasting model. To select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the accelerated failure time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the mean square error (MSE) criteria, the AFT model decreased by 33.8% compared to the statistics prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing the delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.2
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• pp.825-828
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• 2013

Objective: The objective of this study was to determine the five-year survival among patients with cervical cancer treated in Hospital Universiti Sains Malaysia. Methods: One hundred and twenty cervical cancer patients diagnosed between $1^{st}$ July 1995 and $30^{th}$ June 2007 were identified. Data were obtained from medical records. The survival probability was determined using the Kaplan-Meier method and the log-rank test was applied to compare the survival distribution between groups. Results: The overall five-year survival was 39.7% [95%CI (Confidence Interval): 30.7, 51.3] with a median survival time of 40.8 (95%CI: 34.0, 62.0) months. The log-rank test showed that there were survival differences between the groups for the following variables: stage at diagnosis (p=0.005); and primary treatment (p=0.0242). Patients who were diagnosed at the latest stage (III-IV) were found to have the lowest survival, 18.4% (95%CI: 6.75, 50.1), compared to stage I and II where the five-year survival was 54.7% (95%CI: 38.7, 77.2) and 40.8% (95%CI: 27.7, 60.3), respectively. The five-year survival was higher in patients who received surgery [52.6% (95%CI: 37.5, 73.6)] as a primary treatment compared to the non-surgical group [33.3% (95%CI: 22.9, 48.4)]. Conclusion: The five-year survival of cervical cancer patients in this study was low. The survival of those diagnosed at an advanced stage was low compared to early stages. In addition, those who underwent surgery had higher survival than those who had no surgery for primary treatment.

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.521-531
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• 2010

Interval-censored observations are common in medical and epidemiologic studies; however, limited studies exist due to the complexity and special structure of interval-censoring. This paper introduces the imputation method and the self consistency method in the interval-censored data. We propose a new method of generating random numbers under an interval-censoring set-up. Through simulation studies we compare two methods under various simulation schemes in the sense of the mean squared error for estimating the median survival time and the mean integrated squared error for estimating the survival function. Under a moderate censoring percentage, the mean imputation method showed a better performance than the self-consistency method in estimating the median survival time and the survival function.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.18
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• pp.7835-7838
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• 2014

Background: Neuroendocrine tumors have widespread and different clinical presentations and prognoses. This study was conducted to assess their survival time and prognostic factors in Iran. Materials and Methods: In a retrospective cohort study, 189 patients diagnosed of having neuroendocrine carcinoma were chosen. The tumor and clinical characteristics of the patients were modeled with a Cox proportional hazard approach. Survival was assessed using Kaplan-Meyer curves. Results: Crude median survival time was 30 months. Women survived longer than men (the median survival time for women was 40 and for men was 24 months). Age (<60 vs >60 years old with hazard ratio (HR) of 2.43, 95% CI 1.3-4.5), primary pathology report (carcinoid vs. others with HR 5.85 cm, 95% CI 2.4-14.3), tumor size cm (for 5-10, HR of 3.1, 95% CI 1.6 and for >10 HR of 8.2, 95% with 95% CI 3.1-21.9), and chemotherapy with single drug (taking vs. not taking with a HR 2.2, 95% CI 1.1-4.8) had significant effects on overall survival of patients. Conclusions: Survival time in patients with neuroendocrine carcinomas is related to demographics, clinical characteristics, tumor histology, and subtype specific treatment.

• Journal of Embryo Transfer
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• v.9 no.1
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• pp.65-71
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• 1994

This study were carried out to investigate the effective concentration of cryoprotectant agents and sucrose by one-step straw method, and to determine the optimum thawing temperature and equilibration time of frozen bovine embryos. The bovine embryos following dehydration by cryoprotective agents and a various concentration of sucrose were directly plunged into liquid nitrogen and thawed in 3O$^{\circ}C$ water. Survival rate was defined by FDA test. The results are summarized as follows : 1. The survival rate of bovine embryos thawed after rapid freezing in the freezing medium containing a various kinds of cryoprotective agents added 0.25M and O.50M sucrose were 28.6% and 25.0%, 35.1% and 31.6%, 32.4% and 24.4%, 34.2% and 28.2%, 18.9% and 17.6%, 14.7.% and 21.6%, respectively. 2. The survival rate of bovine embryos thawed after rapid freezing in the freezing medium containing a various concentration of sucrose added 1.5M and 2.OM glycerol, i.5M and 2.OM DMSO and 1.5M and 2.OM propanediol were 22.9~37.8%, 2O.7~31. 3%, 19.2~30.0% and 17.2~25.0%, respectively. 3. The temperature thawed at 2$0^{\circ}C$ after rapid freezing of bovine embryos resulted in a significantly higher embryos survival rate than did at 3$0^{\circ}C$ and 35$^{\circ}C$. 4. The equilibration time on the survival rate of bovine embryos was attained after short period of time(2.5~5 min.) in the freezing medium higher than long period of time (1O~20 min.).

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.117-126
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• 2010

Clinical researchers often analyze survival data as binary outcomes using the logistic regression method. This paper examines the information loss resulting from analyzing survival time as binary outcomes. We first demonstrate that, under the proportional hazard assumption, this binary discretization does result in a significant information loss. Second, when fitting a logistic model to survival time data, researchers inadvertently use the maximal statistic. We implement a numerical study to examine the properties of the reference distribution for this statistic, finally, we show that the logistic regression method can still be a useful tool for analyzing survival data in particular when the proportional hazard assumption is questionable.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.405-411
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• 2004

In the analysis of cancer data, it is important to make inferences of survival function and to assess the effects of covariates. Cox's proportional hazard model(PHM) and Beran's nonparametric method are generally used to estimate the survival function with covariates. We adjusted the incomplete survival time using the Buckley and James's(1979) pseudo random variables, and then proposed the estimator for the conditional survival function. Also, we carried out the simulation studies to compare the performances of the proposed method.

• Journal of Gastric Cancer
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• v.14 no.4
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• pp.266-270
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• 2014

Purpose: Palliative gastrectomy and chemotherapy are important options for peritoneal seeding of gastric cancer. The treatment stage IV gastric cancer patient who respond to induction chemotherapy, is converted to gastrectomy (conversion therapy or conversion surgery). This study explored the clinical outcomes of gastric cancer patients with peritoneal seeding who had undergone conversion therapy. Materials and Methods: Between 2003 and 2012, gastric cancer patients with peritoneal seeding, as determined by preoperative or intraoperative diagnosis were reviewed retrospectively. Clinicopathologic characteristics and clinical outcomes of patients with peritoneal seeding were analyzed. Results: Forty-three patients were enrolled. Eighteen patients had undergone conversion surgery and 25 patients continued conventional chemotherapy. Among the 18 conversion patients, 10 received clinically curative resection. The median follow-up period was 28.5 months (range 8 to 60 months) and the total 3-year survival rate was 16.3%. The median survival time of the patients who received clinically curative conversion therapy was 37 months, and the 3-year survival rate was 50%. The median follow-up for non-curative gastrectomy patients was 18 months. No patient treated using chemotherapy survived to 3 years; the median survival time was 8 months. The differences in survival time between the groups was statistically significant (P<0.001). Conclusions: In terms of survival benefits for gastric cancer patients with peritoneal seeding, clinically curative conversion therapy resulted in better clinical outcomes.