No abstract available.
Survival Analysis*

The appropriate plot effectively conveys the author's conclusions to readers. Journal of Korean Medical Science will provide a series of special articles to show you how to make consistent and excellent plots easier. In the first of this series of special articles, I will cover Kaplan-Meier curve (or Kaplan-Meier plot) and the ease tools. This plot, generated as a result of the Survival Analysis, provides a visualization of the ‘Kaplan-Meier Survival Probability Estimate’ for each group.
Survival Analysis

No abstract available.
Ovarian Neoplasms* ; Survival Analysis*

No abstract available.
Humans ; Microcomputers* ; Survival Analysis*

Humans ; Survival Analysis

The survival data and the survival analysis are the data and analysis methods used to study the probability of survival. The survival data consist of a period from the juncture of a start event to the juncture of the end event (occurrence event). The period is called the survival period or survival time. In this way, the method of analysing the survival time of subjects and appropriately summarizing the degree of survival is called survival analysis. To understand and analyse survival analysis methods, researchers must be aware of some concepts. Concepts to be aware of in the survival analysis include events, censored data, survival period, survival function, survival curve and so on. This review focuses on the terms and concepts used in the survival analysis. It will also cover the types of survival data that should be collected and prepared when using actual survival analysis method and how to prepare them.
Methods ; Survival Analysis

OBJECTIVE: To date, few attempts have been made at clinical features and prognostic factors of primary peritoneal carcinoma (PPC) because of low prevalence. The aim of this study is to evaluate the clinical characteristcs and determine the prognosis factors of PPC. METHODS: From March 1996 to March 2004, a total of 23 women newly diagnosed with PPC were recruited into the study. Overall survival and prognostic factors were evaluated using Kaplan-Meier method and Cox regression model. RESULTS: The mean age of patients was 58.7+/-7.6 years and the FIGO stage was advanced disease; stage IIIc (73%) and IV (27%). The mean survival time for patients enrolled was 26.0 months. By univariate analysis, tumor state (p=0.028), performance status (p=0.045), the presence of initial debulking operation (p=0.035), and normalization of CA125 at 3 months of treatment (p=0.003) were significantly correlated with survival. On multivariate analysis, only the normalization of CA125 at 3 months of treatment remained as the independent factor for survival (Odds ratio, 6.896; 95% Confidence interval, 1.504-31.623; p=0.013). CONCLUSION: The mean survival time for patients with PPC was 26.0 months, and the normalization of CA125 at 3 months of treatment was identified as the independent prognostic factor. From this study, we analysis the clinical characteristics of PPC and provide more precise understanding of this disease.
Female ; Humans ; Multivariate Analysis ; Prevalence ; Prognosis ; Survival Analysis* ; Survival Rate

No abstract available.
Proportional Hazards Models* ; Survival Analysis*

Follow-Up Studies ; Survival Analysis

No abstract available.
Humans ; Renal Dialysis* ; Survival Analysis*