• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.279-291
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• 2012

When fitting a Cox proportional hazards model with missing covariates, it is inefficient to exclude observations with missing values in the analysis. Furthermore, if the missing-data mechanism is not Missing Completely At Random(MCAR), it may lead to biased parameter estimation. Many approaches have been suggested to handle the Cox proportional hazards model when covariates are sometimes missing, but they are based on the selection model. This paper suggest an approach to handle Cox proportional hazards model with missing covariates by using the pattern-mixture model (Little, 1993). The pattern-mixture model is expressed by the joint distribution of survival time and the missing-data mechanism. In the pattern-mixture model, many models can be considered by setting up various restrictions, and different results under various restrictions indicate the sensitivity of the model due to missing covariates. A simulation study was conducted to show the sensitivity of parameter estimation under different restrictions in a pattern-mixture model. The proposed approach was also applied to mouse leukemia data.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.349-359
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• 2017

In this paper, we conducted survival analyses by fitting the Cox proportional hazards model to stage III proximal colon cancer data obtained from the Surveillance, Epidemiology, and End Results program of the National Cancer Institute. We investigated the effect of covariates on the hazard function for death from proximal colon cancer in stage III with surgery performed and estimated the survival probability for a patient with specific covariates. We showed that the proportional hazards assumption is satisfied for covariates that were used to analyses, using a test based on the Schoenfeld residuals and plots of the Schoenfeld residuals and $log[-log\{{\hat{S}}(t)\}]$. We evaluated the model calibration and discriminatory accuracy by calibration plot and time-dependent area under the ROC curve, which were calculated using 10-fold cross validation.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.39-52
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• 2007

Crossover trials of new drugs in the treatment of angina pectoris, which frequently use treadmill exercise test for the assessment of its efficacy, produce censored survival times. In this paper we consider analysis approaches for censored survival times from crossover trials. Previously, a stratified Cox model for paired observation and nonparametric methods have been presented as possible analysis methods. On the other hand, the differences of two survival times would produce interval-censored survival times and we propose a Cox model for interval-censored data as n alternative analysis method. Example data is analyzed in order to compare these different methods.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.85-104
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• 1997

Proportional hazards model has been widely used for analyzing survival data. This article reviews some well-known goodness-of-fit tests for proportional hazards model. Simulation studies also provide some insights into the properties of these test statistics across several types of survival distributions and degerees of censorship.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.957-968
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• 2021

Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.

• The Korean Journal of Financial Management
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• v.18 no.2
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• pp.99-123
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• 2001

예금보험제도는 금융기관에 대한 예금자의 신뢰성을 보장해 주어 금융제도의 안정성을 유지시킨다는 면에서 유용한 제도이나 보험의 일반적인 속성인 도덕적 해이를 유발할 수 있다는 단점이 있으며, 이를 방지하기 위한 대안으로 예금보험료율을 금융기관의 위험에 따라 차등화 하는 위험예금 보험료율 제도가 검토되어 왔고 일부 국가에서는 이를 시행하고 있다. 위험예금보험료율을 결정하는 방안으로 그간 주가자료를 이용한 옵션가격결정모형이 주로 연구되어 왔는데, 본 연구에서는 Cox의 위험비례모형을 이용하여 은행의 위험을 측정하고 보험료율을 결정하는 방안에 관하여 연구하였다. 위험비례모형은 옵션가격결정모형과 달리 재무지표를 사용하므로 감독당국에서 실무상 적용하기가 용이하다는 등의 장점이 있다. 위험비례모형을 국내은행에 적용하여 실증분석한 연구결과 현행의 고정 예금보험료율은 국내은행의 위험에 비해 상당히 낮은 수준으로 나타났으며, 보험료율 결정에 가장 큰 영향을 미치는 변수인 은행 실패시 예금보험기구가 부담하는 손실비율을 최소화하기 위해서는 부실은행은 조기 퇴출시켜야 한다는 논리적 타당성도 도출하였다.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.921-932
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• 2015

This study analyzed military personnel survivability in regards to offensive operations according to the scientific military training data of a reinforced infantry battalion. Scientific battle training was conducted at the Korea Combat Training Center (KCTC) training facility and utilized scientific military training equipment that included MILES and the main exercise control system. The training audience freely engaged an OPFOR who is an expert at tactics and weapon systems. It provides a statistical analysis of data in regards to state-of-the-art military training because the scientific battle training system saves and utilizes all training zone data for analysis and after action review as well as offers training control during the training period. The methodologies used the Cox PH modeling (which does not require parametric distribution assumptions) and decision tree modeling for survival data such as CART, GUIDE, and CTREE for richer and easier interpretation. The variables that violate the PH assumption were stratified and analyzed. Since the Cox PH model result was not easy to interpret the period of service, additional interpretation was attempted through univariate local regression. CART, GUIDE, and CTREE formed different tree models which allow for various interpretations.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.723-736
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• 2013

The importance of lapse rate is highly increasing due to the introduction of Cash Flow Pricing system, non-refund-of-reserve insurance policy, and IFRS (International Financial Reporting System) to the Korean insurance market. Researches on lapse rate have mainly focused on simple data analysis and regression analysis, etc. However, lapse rate can be analyzed by survival analysis and can be well explained in terms of several covariates with Cox proportional hazard model. Guaranteed minimum benefits embedded in variable annuities require more elegant statistical analysis of lapse rate. Hence, this paper analyzes data of policyholders with variable annuities by using Cox proportional hazard model. The key variables of policy holder that influences the lapse rate are payment method, premium, lapse insured to term insured, reserve-GMXB ratio, and age.

• Industry Promotion Research
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• v.5 no.1
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• pp.1-11
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• 2020

The purpose of this study is to analyze the industrial accidents data with survival model. EDA approach is used to explore the relationship between two variables and among three variables for the past 10 years of industrial accidents data. Survival models are also tried. Survival curve drops more rapidly for the business with fewer employees as time goes by. Industrial accidents occur more often as the total number of industrial accidents gets larger and as the number of employees gets smaller. Agriculture, fishing and forestry have a higher level of industrial accidents than construction while service industry and 'transportation·storage and telecommunication' have a fewer number of industrial accidents than construction. Korea Safety and Health Agency's and Ministry of Employment and Labor's involvement were not effective but Civilian's was. Recurrent event data analysis reveals all most the same result as for non-recurrent data analysis.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.233-250
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• 2002

Cox's proportional hazard model is highly-used for the regression analysis of survival data in various fields. Regression diagnostics for the proportional hazards model, however, is not as well-known as the diagnostics for the classical linear models and so these diagnostic methods are not used widely in our practical data analyses. For this reason, we review the residuals proposed by several authors, and investigate how to use them in assessing the model. We also provide the results and interpretation with the analysis of PBC data using S-plus 2000 program.