• Journal of Gastric Cancer
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• 제14권4호
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• pp.259-265
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• 2014

Purpose: Survival analysis of gastric cancer patients requires knowledge about factors that affect survival time. This paper attempted to analyze the survival of patients with incomplete registered data by using imputation methods. Materials and Methods: Three missing data imputation methods, including regression, expectation maximization algorithm, and multiple imputation (MI) using Monte Carlo Markov Chain methods, were applied to the data of cancer patients referred to the cancer institute at Imam Khomeini Hospital in Tehran in 2003 to 2008. The data included demographic variables, survival times, and censored variable of 471 patients with gastric cancer. After using imputation methods to account for missing covariate data, the data were analyzed using a Cox regression model and the results were compared. Results: The mean patient survival time after diagnosis was $49.1{\pm}4.4$ months. In the complete case analysis, which used information from 100 of the 471 patients, very wide and uninformative confidence intervals were obtained for the chemotherapy and surgery hazard ratios (HRs). However, after imputation, the maximum confidence interval widths for the chemotherapy and surgery HRs were 8.470 and 0.806, respectively. The minimum width corresponded with MI. Furthermore, the minimum Bayesian and Akaike information criteria values correlated with MI (-821.236 and -827.866, respectively). Conclusions: Missing value imputation increased the estimate precision and accuracy. In addition, MI yielded better results when compared with the expectation maximization algorithm and regression simple imputation methods.

• 응용통계연구
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• 제20권2호
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• pp.313-322
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• 2007

오염이 있는 생존자료에서 여러 가지 회귀뎁스(regression depth)를 비교 연구하였다. 중도절단 자료에서 회귀뎁스에 대한 정의는 Park과 Hwang(2003)의 반공간회귀뎁스(halfspace regression depth)와 Park(2003)의 심플리셜 회귀뎁스(simplicial regression depth)가 있다. 본 논문은 Hubert 등(2001)이 제안한 사영회귀뎁스(projection regression depth)를 생존자료에서 사용하는 방법을 제시하고 이 방법과 기존의 뎁스기반 회귀모형과의 비교를 다양한 오염 상황에서 실시하였다.

• Communications for Statistical Applications and Methods
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• 제22권3호
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• pp.233-239
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• 2015

This study compares two generalized Lindley distributions and assesses consistency between theoretical and analytical results. Data (complete and censored) assumed to follow the Lindley distribution are generated and analyzed using two generalized Lindley distributions, and maximum likelihood estimates of parameters from the generalized distributions are obtained. Size and power of tests of hypotheses on the parameters are assessed drawing on asymptotic properties of the maximum likelihood estimates. Results suggest that whereas size of some of the tests of hypotheses based on the considered generalized distributions are essentially ${\alpha}$-level, some are possibly not; power of tests of hypotheses on the Lindley distribution parameter from the two distributions differs.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.357-360
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• 2005

In this paper we consider the well-known semiparametric proportional hazards (PH) models for survival analysis. These models are usually used with few covariates and many observations (subjects). But, for a typical setting of gene expression data from DNA microarray, we need to consider the case where the number of covariates p exceeds the number of samples n. For a given vector of response values which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the significant genes. This approach enable us to estimate the survival curve when n < < p. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional flexibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in effect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology to diffuse large B-cell lymphoma (DLBCL) complementary DNA(cDNA) data.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 춘계 학술발표회 논문집
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• pp.17-23
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• 2005

In this paper we consider the well-known semiparametric proportional hazards models for survival analysis. These models are usually used with few covariates and many observations (subjects). But, for a typical setting of gene expression data from DNA microarray, we need to consider the case where the number of covariates p exceeds the number of samples n. For a given vector of response values which are times to event (death or censored times) and p gene expressions(covariates), we address the issue of how to reduce the dimension by selecting the significant genes. This approach enables us to estimate the survival curve when n ${\ll}$p. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional flexibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in effect works as a penalty To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology to diffuse large B-cell lymphoma (DLBCL) complementary DNA (cDNA) data and Breast Carcinomas data.

• Journal of the Korean Data and Information Science Society
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• 제26권6호
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• pp.1225-1238
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• 2015

본 연구에서는 직장암 환자들의 수술 후 재발까지의 시간 데이터에 대해 집단 간 생존함수 양상에 차이가 있는지 로그 순위 검정 결과 유의수준 10%에서 포도당 단일수송체 (GLUT1)의 수준, 수술 전 병기 (cstage), 수술 후 병기 (ypstage)에 따른 차이가 유의하며, Cox 비례위험률 모형을 이용하여 검정한 결과 가장 유의한 공변량은 포도당 단일수송체와 수술 후 병기였다. 지수분포를 따른다고 가정할 경우, 우도함수를 기반한 여러 가지 위험률 변화점을 추정하였다.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 추계 학술발표회 논문집
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• pp.39-45
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• 2003

Ghosh and Ramamoorthi (1996) studied the posterior consistency for survival models and showed that the posterior was consistent, when the prior on the distribution of survival times was the Dirichlet process prior. In this paper, we study the posterior consistency of survival models with neutral to the right process priors which include Dirichlet process priors. A set of sufficient conditions for the posterior consistency with neutral to the right process priors are given. Interestingly, not all the neutral to the right process priors have consistent posteriors, but most of the popular priors such as Dirichlet processes, beta processes and gamma processes have consistent posteriors. For extended beta processes, a necessary and sufficient condition for the consistency is also established.

• 응용통계연구
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• 제29권7호
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• pp.1231-1246
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• 2016

경쟁위험에 대한 연구 중 주로 쓰이는 방법은 Cause-specific 위험 모형과 subdistribution을 이용한 비례 위험 모형 방법이다. 그 이후에도 많은 모형이 제시되었지만, 추정 방법 면에서 설명력이 부족하거나 알고리즘으로 구현하기 어려운 단점을 가지고 있어서 잘 활용되고 있지 않다. 이 논문에서는 Cause-specific 위험 모형, subdistribution을 이용한 비례 위험 모형과 비교적 최근에 제시된 이항 회귀 모형(direct binomial model), 절대 위험 회귀 모형(absolute risk regression model), Eriksson 등 (2015)의 비례 오즈 모형(proportional odds model)을 소개하고 추정 방법을 간단히 설명하고자 한다. 각 모형에 대하여 SAS와 R을 이용한 활용 방법을 제시하고, 두 가지 경쟁위험이 존재하는 데이터를 R을 이용하여 분석하였다.

• Journal of the Korean Data and Information Science Society
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• 제23권6호
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• pp.1045-1054
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• 2012

서포트벡터 기계는 분류 및 비선형 함수추정에서 유용하게 사용되고 있는 통계적 기법이다. 본 논문에서는 두 개의 입력변수와 회귀함수의 단조 관계를 이용하여 단조 서포트벡터기계를 제안하고, Kaplan-Meier의 방법에 의해서 생존함수의 추정값이 주어진 경우 제안된 방법을 이용하여 생존 함수를 평활하는 방법 또한 제안한다. 모의실험에서는 실제 생존함수를 이용하여 Kaplan-Meier의 방법에 의한 생존함수의 추정값과의 성능을 비교함으로써 제안된 방법의 우수성을 보이기로 한다.

• Communications for Statistical Applications and Methods
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• 제24권6호
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• pp.605-625
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• 2017

This paper presents proportional odds cure models to allow spatial correlations by including spatial frailty in the interval censored data setting. Parametric cure rate models with independent and dependent spatial frailties are proposed and compared. Our approach enables different underlying activation mechanisms that lead to the event of interest; in addition, the number of competing causes which may be responsible for the occurrence of the event of interest follows a Geometric distribution. Markov chain Monte Carlo method is used in a Bayesian framework for inferential purposes. For model comparison some Bayesian criteria were used. An influence diagnostic analysis was conducted to detect possible influential or extreme observations that may cause distortions on the results of the analysis. Finally, the proposed models are applied for the analysis of a real data set on smoking cessation. The results of the application show that the parametric cure model with frailties under the first activation scheme has better findings.