• Journal of the Korean Statistical Society
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• 제23권2호
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• pp.379-402
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• 1994

Graphical and numerical methods for checking the assumption of proportional hazards of Cox model for censored survival data are discussed. The strenths and weaknessess of several goodness of fit tests for the propotional hazards for the two-sample problem are evaluated with Monte Carlo simulations, and the tests of Schoenfeld (1980), Andersen (1982), Wei (1984), and Gill and Schumacher (1987) are considered. The goodness of fit methods are illustrated with the survival data of patients who had chronic liver disease and had been treated with the endoscopy injection sclerotheraphy. Two other examples of data known to have nonpropotional hazards are also used in the illustration.

• 지식경영연구
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• 제11권2호
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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 Korean Data and Information Science Society
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• 제28권2호
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• pp.349-359
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• 2017

본 논문에서는 미국 국립암연구소의 SEER 프로그램에서 제공하는 우측 대장암 3기 자료에 Cox 비례위험모형을 적합하여 생존분석을 하였다. 우측 대장암 3기 환자의 사망률에 유의한 영향을 미치는 공변량들을 파악하고, 관심있는 공변량들을 가진 환자의 생존율을 추정하였다. Schoenfeld 잔차를 기반한 검정과 Schoenfeld 잔차 도표, $log[-log\{{\hat{S}}(t)\}]$ 도표를 이용하여 분석에 사용된 공변량들이 비례위험 가정을 만족함을 확인하였다. 적합된 Cox 비례위험모형의 타당성을 검증하기 위해 10-fold 교차 검증을 이용하여 calibration 도표와 시간에 의존하는 ROC 곡선 아래 면적을 계산하였다. 이를 통해 적합된 Cox 비례위험모형의 타당성을 확인하였다.

• 응용통계연구
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• 제15권2호
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• pp.233-250
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• 2002

Cox의 비례위험모형(proportional hazards model)은 생존자료(survival data)에 대한 회귀모형으로 경제학 및 의·공학을 비롯한 여러 응용 분야에서 가장 널리 쓰이고 있는 모형 중 하나이다. 그러나, 이 모형은 일반선헝모형에 비해 잔차 분석을 통한 회귀 진단의 연구가 널리 알려져 있지 않아, 국내의 실제 자료 분석에서는 잔차 분석에 대한 활용이 거의 이루어지지 않고 있는 실정이다. 이에 본 논문에서는 그 동안 제안된 여러 잔차들을 비교 분석하고, S-plus 프로그램을 이용한 PBC(primary biliary cirrhosis) 자료분석을 통해 각 잔차들의 의미를 고찰하고자 한다.

• Communications for Statistical Applications and Methods
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• 제27권6호
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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.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2001년도 추계학술대회
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• pp.42-42
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• 2001

• Asian Pacific Journal of Cancer Prevention
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• 제16권12호
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• pp.5081-5084
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• 2015

Background: Breast cancer is a malignant tumor that starts from cells of the breast and is seen mainly in women. It's the most common cancer in women worldwide and is a major threat to health. The purpose of this study was to fit a Cox proportional hazards model for prediction and determination of years of survival in Iranian patients. Materials and Methods: A total of 366 patients with breast cancer in the Cancer Research Center were included in the study. A Cox proportional hazard model was used with variables such as tumor grade, number of removed positive lymph nodes, human epidermal growth factor receptor 2 (HER2) expression and several other variables. Kaplan-Meier curves were plotted and multi-years of survival were evaluated. Results: The mean age of patients was 48.1 years. Consumption of fatty foods (p=0.033), recurrence (p<0.001), tumor grade (p=0.046) and age (p=0.017) were significant variables. The overall 1- year, 3-year and 5-year survival rates were found to be 93%, 75% and 52%. Conclusions: Use of covariates and the Cox proportional hazard model are effective in predicting the survival of individuals and this model distinguished 4 effective factors in the survival of patients.

• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2005년도 학술발표대회 논문집
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• pp.265-269
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• 2005

Cox's proportional hazards model (PHM) has been widely applied in the analysis of lifetime data, and it can be characterized by the baseline hazard function and covariates influencing systems' lifetime, where the covariates describe operating environments (e.g. temperature, pressure, humidity). In this article, we consider the constant baseline hazard function and a discrete random variable of a covariate. The estimation procedure is developed in a parametric framework when there are not only complete data but also incomplete one. The Expectation-Maximization (EM) algorithm is employed to handle the incomplete data problem. Simulation results are presented to illustrate the accuracy and some properties of the estimation results.

• 응용통계연구
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• 제25권6호
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• pp.959-975
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• 2012

Each year research institutions such as the Korea Employment Information Service(KEIS), a government institution established for the advancement of employment support services, and Job Korea, a popular Korean job website, announce first job waiting times after college graduation. This provides useful information understand and resolve youth unemployment problems. However, previous reports deal with the time as a completely observed one and are not appropriate. This paper proposes a new study on first job waiting times after college graduation set to 4 months prior to graduation. In Korea, most college students hunt for jobs before college graduation in addition, the full-fledged job markets also open before graduation. In this case the exact waiting time of college graduates can be right-censored. We apply a Cox proportional hazards model to evaluate the associations between first job waiting times and risk factors. A real example is based on the 2008 Graduates Occupational Mobility Survey(GOMS).

• 한국통계학회:학술대회논문집
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• 한국통계학회 2002년도 추계 학술발표회 논문집
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• pp.197-201
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• 2002

The healthy worker effect is an important issue in occupational epidemiology. We proposed a new statistical method to test the relationship between exposure and time to death in the presence of the healthy worker effect. In this study, we considered the healthy worker hire effect to operate as a confounder and the healthy worker survival effect to operate as a confounder and an intermediate variable. The basic idea of the proposed method reflects the length bias-sampling caused by changing one's employment status. Simulation studies were also carried out to compare the proposed method with the Cox proportional hazards models. According to our simulation studies, both the proposed test and the test based on the Cox model having the change of the employment status as a time-dependent covariate seem to be satisfactory at an upper 5% significance level. The Cox models, however, are inadequate with the change, if any, of the employment status as time-independent covariate. The proposed test is superior in power to the test based on the Cox model including the time-dependent employment status.