• 응용통계연구
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• 제20권1호
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• pp.39-52
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• 2007

협심증 치료의 신약에 대한 교차계획 임상시험(crossover clinical trials)에서 신약의 효능을 알아보는 운동테스트(treadmill exercise test) 결과는 중도절단 생존시간(censored survival times)으로 측정된다. 이 논문에서는 교차계획에서 수집된 중도절단 생존자료의 여러 가지 분석법에 대해 설명한다. 중도절단을 감안한 비모수적 방법들과 층화 Cox 비례위험모형 (stratified Cox proportional hazards model)에 근거한 분석법이 제시되었다. 한편, 교차계획의 두 시기에 걸쳐 수집된 생존시간의 차(difference)로부터 구간절단자료(interval censored data)가 생성되며 이에 근거한 분석법으로서 이 논문에서는 구간절단자료에 대한 Cox 비례위험모형 (proportional hazards model)의 가능성을 알아보며, 예제 자료로써 여러 방법들의 결과를 비교해 본다.

• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.31-38
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• 2002

In the case of multiple survival times which might be censored at each covariate vector, we study the regression quantile estimations in this paper. The estimations are based on the empirical distribution functions of the censored times and the sample quantiles of the observed survival times at each covariate vector and the weighted least square method is applied for the estimation of the regression quantile. The estimators are shown to be asymptotically normally distributed under some regularity conditions.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.1017-1024
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• 2003

An estimation problem of treatment effect for bivariate censored survival data is considered under location shift model between two sample. The proposed estimator is very intuitive and can be obtained in a closed form. Asymptotic results of the proposed estimator are discussed and simulation studies are performed to show the strength of the proposed estimator.

• 응용통계연구
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• 제31권6호
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• pp.761-769
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• 2018

통계학 연구에 모의실험이 중요하게 쓰이며 중도절단자료를 다루는 생존분석에서도 마찬가지다. 생존분석에서 Cox 모형이 널리 쓰이는데, Cox 모형을 따르는 중도절단자료를 생성하는 방법에 대해 살펴보았다. Bender 등 (Statistics in Medicine, 24, 1713-1723, 2005)은 생존시간을 생성하는 모수적 방법을 제시하였으나 생존시간뿐만 아니라 중도절단시간도 생성해야 중도절단자료를 얻게 된다. 중도절단자료를 생성하기 위한 모수적 방법과 함께 비모수적 방법도 제시하였으며 실제 자료에도 적용해 보았다.

• Journal of the Korean Statistical Society
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• 제30권1호
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• pp.127-138
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• 2001

In order to combine parametric and nonparametric approaches together for survival analysis with censored observations, a new class of priors called mixtures of the beta processes is introduced. It is shown that mixtures of beta processes priors generalized the well known priors - mixtures of Dirichlet processes, and they are conjugate with right censored observations. Formulas for computing the posterior distribution are derived. Finally, a real data set is analyzed for illustrational purpose.

• Journal of the Korean Data and Information Science Society
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• 제7권1호
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• pp.59-68
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• 1996

In this paper, we consider how to estimate New Better Than Used (NBU) survival curves from randomly right censored data. We propose several possible NBU estimators and study their properties. Numerical studies indicate that the proposed estimators are appropriate in practical use. Some useful examples are presented.

• Communications for Statistical Applications and Methods
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• 제30권4호
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• pp.389-402
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• 2023

Multivariate or clustered failure time data often occur in many medical, epidemiological, and socio-economic studies when survival data are collected from several research centers. If the data are periodically observed as in a longitudinal study, survival times are often subject to various types of interval-censoring, creating multivariate interval-censored data. Then, the event times of interest may be correlated among individuals who come from the same cluster. In this article, we propose a unified linear regression method for analyzing multivariate interval-censored data. We consider a semiparametric multivariate accelerated failure time model as a statistical analysis tool and develop a generalized Buckley-James method to make inferences by imputing interval-censored observations with their conditional mean values. Since the study population consists of several heterogeneous clusters, where the subjects in the same cluster may be related, we propose a generalized estimating equations approach to accommodate potential dependence in clusters. Our simulation results confirm that the proposed estimator is robust to misspecification of working covariance matrix and statistical efficiency can increase when the working covariance structure is close to the truth. The proposed method is applied to the dataset from a diabetic retinopathy study.

• Journal of the Korean Statistical Society
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• 제32권1호
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• pp.25-32
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• 2003

In this paper, we define the mean life process for the right censored data and show the asymptotic equivalence between two kinds of the mean life processes. We use the Kaplan-Meier and Susarla-Van Ryzin estimates as the estimates of survival function for the construction of the mean life processes. Also we show the asymptotic equivalence between two mean residual life processes as an application and finally discuss some difficulties caused by the censoring mechanism.

• International Journal of Reliability and Applications
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• 제2권1호
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• pp.49-56
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• 2001

In this paper we consider empirical Bayes estimation of the hazard rate and survival probabilities with right censored data under the assumption that the hazard function is constant over the period of observation and the prior distribution is gamma. We provide an estimator of the first derivative of the prior moment generating function that converges at each point to the true value in $L_2$ and use it to obtain, easy to compute, asymptotically optimal estimators under the squared error loss function.

• 한국디지털정책학회:학술대회논문집
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• 한국디지털정책학회 2006년도 추계학술대회
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• pp.327-336
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• 2006

The odds ratio is used for assessing the disease-exposure association, because epidemiological data for case-control of cohort studies are often summarized into 2 ${\times}$ 2 tables. In this paper we define the odds ratio function(ORF) that extends odds ratio used on discrete survival event data to continuous survival time data and propose estimation procedures with censored data. The first one is a nonparametric estimator based on the Nelson-Aalen estimator of comulative hazard function, and the others are obtained using the concept of empirical odds ratio. Asymptotic properties such as consistency and weak convergence results are also provided. The ORF provides a simple interpretation and is comparable to survival function or comulative hazard function in comparing two groups. The mean square errors are investigated via Monte Carlo simulation. The result are finally illustrated using the Melanoma data.