• Korean Journal of Radiology
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• v.23 no.8
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• pp.777-784
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• 2022

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1533-1537
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• 2012

In general, breast cancer is the most common malignancy among women in developed as well as some developing countries, often being the second leading cause of cancer mortality after lung cancer. Using a parametric log-logistic model to consider the effects of prognostic factors, the present study focused on the 5-year survival of women with the diagnosis of breast cancer in Southern Iran. A total of 1,148 women who were diagnosed with primary invasive breast cancer from January 2001 to January 2005 were included and divided into three prognosis groups: poor, medium, and good. The survival times as well as the hazard rates of the three different groups were compared. The log-logistic model was employed as the best parametric model which could explain survival times. The hazard rates of the poor and the medium prognosis groups were respectively 13 and 3 times greater than in the good prognosis group. Also, the difference between the overall survival rates of the poor and the medium prognosis groups was highly significant in comparison to the good prognosis group. Use of the parametric log-logistic model - also a proportional odds model - allowed assessment of the natural process of the disease based on hazard and identification of trends.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.237-253
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• 2005

In Korea, the volume of insurance industry has been increased rapidly with helping the economic growth, the increment of GNP and derive of public welfare policy. But the other side of the volume increment, the life insurers have some problems, such as the high rate of turnover, lapses and surrenders, in processing of acquiring more insurance contracts. The object of this paper is the analysis of the causes and properties of the high rate of turnover, lapses and surrenders using statistical survival model. Also we hope that the insurers will use the results of analysis to reduce the rates.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.675-688
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• 2020

In survival analysis of observational data, the inverse probability weighting method and the Cox proportional hazards model are widely used when estimating the causal effects of multiple-valued treatment. In this paper, the two kinds of weights have been examined in the inverse probability weighting method. We explain the reason why the stabilized weight is more appropriate when an inverse probability weighting method using the generalized propensity score is applied. We also emphasize that a marginal hazard ratio and the conditional hazard ratio should be distinguished when defining the hazard ratio as a treatment effect under the Cox proportional hazards model. A simulation study based on real data is conducted to provide concrete numerical evidence.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.189-201
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• 2016

In survival analysis, the Nelson-Aalen estimator and Peterson estimator are often used to estimate a cumulative hazard function in randomly right censored data. In this paper, we suggested the smoothing version of the cumulative hazard function estimators using a Bezier curve. We compare them with the existing estimators including a kernel smooth version of the Nelson-Aalen estimator and the Peterson estimator in the sense of mean integrated square error to show through numerical studies that the proposed estimators are better than existing ones. Further, we applied our method to the Cox regression where covariates are used as predictors and suggested a survival function estimation at a given covariate.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.747-756
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• 2010

With the recent recession, studies on the economy are actively being conducted throughout the industry. Based on the Small Business data registered in the Credit Guarantee Fund, we estimated the survival probability in the context of the survival analysis. We also analyzed the survival time for the construction and the other industries which are distinguished depending on the types of business and assets in the Small Business. The survival probability was estimated by using the life-table and the difference between the survival probabilities for the different types of business was described via the method of the Log-rank test and the Wilcoxon test. We found that the small business with over one billion asset has the highest survival probability and that with less than 1000 million asset showed the similar survival probability. In terms of types of business Wholesale and Retail trade industry and Services were relatively high in the survival probability than Light, Heavy, and the construction industries. Especially the construction industry showed the lowest survival probability. Most of the Small Business tend to increase in the hazard rate over time.

• Communications for Statistical Applications and Methods
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• v.21 no.4
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• pp.285-296
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• 2014

The quantile residual life function has been effectively used to interpret results from the analysis of the proportional hazards model for censored survival data; however, the quantile residual life function is not always estimable with currently available semi-parametric regression methods in the presence of heavy censoring. A parametric regression approach may circumvent the difficulty of heavy censoring, but parametric assumptions on a baseline hazard function can cause a potential bias. This article proposes a Bayesian semi-parametric regression approach for inference on an unknown baseline hazard function while adjusting for available covariates. We consider a model-based approach but the proposed method does not suffer from strong parametric assumptions, enjoying a closed-form specification of the parametric regression approach without sacrificing the flexibility of the semi-parametric regression approach. The proposed method is applied to simulated data and heavily censored survival data to estimate various quantile residual lifetimes and adjust for important prognostic factors.

• Journal of Hospice and Palliative Care
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• v.24 no.4
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• pp.254-260
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• 2021

Continuous deep sedation (CDS) is an extreme form of palliative sedation to relieve refractory symptoms at the end of life. In this study, we shared our experiences with CDS and examined the clinical characteristics associated with survival in patients with terminal cancer who received CDS. We conducted a chart audit of 106 consecutive patients with terminal cancer who received CDS at a single hospice care unit between January 2014 and December 2016. Survival was defined as the first day of admission to the date of death. The associations between clinical characteristics and survival were presented as hazard ratios and 95% confidence intervals using a Cox proportional hazard model. The mean age of participants was 65.2 years, and 33.0% (n=35) were women. Diazepam was the most commonly administered drug, and haloperidol or lorazepam were also used if needed. One sedative was enough for a majority of the patients. Stepwise multivariate analysis identified poor functioning, a high Palliative Prognostic Index score, hyperbilirubinemia, high serum ferritin levels, and a low number of sedatives as independent poor prognostic factors. Our experiences and findings are expected to be helpful for shared decision-making and further research on palliative sedation.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.21
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• pp.9453-9458
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• 2014

Background: Breast cancer is the second leading cause of cancer death for women in the United States. Differences in survival of breast cancer have been noted among racial and ethnic groups, but the reasons for these disparities remain unclear. This study presents the characteristics and the survival curve of two racial and ethnic groups and evaluates the effects of race on survival times by measuring the lifetime data-based half-normal model. Materials and Methods: The distributions among racial and ethnic groups are compared using female breast cancer patients from nine states in the country all taken from the National Cancer Institute's Surveillance, Epidemiology, and End Results cancer registry. The main end points observed are: age at diagnosis, survival time in months, and marital status. The right skewed half-normal statistical probability model is used to show the differences in the survival times between black Hispanic (BH) and black non-Hispanic (BNH) female breast cancer patients. The Kaplan-Meier and Cox proportional hazard ratio are used to estimate and compare the relative risk of death in two minority groups, BH and BNH. Results: A probability random sample method was used to select representative samples from BNH and BH female breast cancer patients, who were diagnosed during the years of 1973-2009 in the United States. The sample contained 1,000 BNH and 298 BH female breast cancer patients. The median age at diagnosis was 57.75 years among BNH and 54.11 years among BH. The results of the half-normal model showed that the survival times formed positive skewed models with higher variability in BNH compared with BH. The Kaplan-Meir estimate was used to plot the survival curves for cancer patients; this test was positively skewed. The Kaplan-Meier and Cox proportional hazard ratio for survival analysis showed that BNH had a significantly longer survival time as compared to BH which is consistent with the results of the half-normal model. Conclusions: The findings with the proposed model strategy will assist in the healthcare field to measure future outcomes for BH and BNH, given their past history and conditions. These findings may provide an enhanced and improved outlook for the diagnosis and treatment of breast cancer patients in the United States.

• Journal of Preventive Medicine and Public Health
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• v.56 no.1
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• pp.59-66
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• 2023

Objectives: The aim of this study was to evaluate the effect of body weight status and sleep duration on the discrete-time hazard of menarche in Korean schoolgirls using multiple-point prospective panel data. Methods: The study included 914 girls in the 2010 Korean Children and Youth Panel Study who were in the elementary first-grader panel from 2010 until 2016. We used a Gompertz regression model to estimate the effects of weight status based on age-specific and sex-specific body mass index (BMI) percentile and sleep duration on an early schoolchild's conditional probability of menarche during a given time interval using general health condition and annual household income as covariates. Results: Gompertz regression of time to menarche data collected from the Korean Children and Youth Panel Study 2010 suggested that being overweight or sleeping less than the recommended duration was related to an increased hazard of menarche compared to being average weight and sleeping 9 hours to 11 hours, by 1.63 times and 1.38 times, respectively, while other covariates were fixed. In contrast, being underweight was associated with a 66% lower discrete-time hazard of menarche. Conclusions: Weight status based on BMI percentiles and sleep duration in the early school years affect the hazard of menarche.