• 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.34 no.3
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• pp.411-425
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• 2021

Accelerated failure time (AFT) model represents a linear relationship between the log-survival time and covariates. We are interested in the inference of covariate's effect affecting the variation of survival times in the AFT model. Thus, we need to model the variance as well as the mean of survival times. We call the resulting model mean and variance AFT (MV-AFT) model. In this paper, we propose a variable selection procedure of regression parameters of mean and variance in MV-AFT model using penalized likelihood function. For the variable selection, we study four penalty functions, i.e. least absolute shrinkage and selection operator (LASSO), adaptive lasso (ALASSO), smoothly clipped absolute deviation (SCAD) and hierarchical likelihood (HL). With this procedure we can select important covariates and estimate the regression parameters at the same time. The performance of the proposed method is evaluated using simulation studies. The proposed method is illustrated with a clinical example dataset.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.97-105
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• 2012

The generalized canonical correlation biplot is a 2-dimensional plot to graphically investigate the relationship between more than three sets of variables and the relationship between observations and variables. Recently, Choi and Choi (2010) investigated the relationship physique, physical fitness and basic skill factors of Korea Tennis Association(KTA) players of using this biplot; however we consider the set of covariate variables affecting the linearly on two sets of variables. In this case, if we apply the generalized canonical correlation biplot, we cannot clearly interpret the other two sets of variables due to the effect of the set of covariate variables. Moreover, Yeom and Choi (2011) provided partial canonical correlation analysis that removed the linear effect of the set of covariate variables on two sets of variables. In addition, Procrustes analysis is a useful tool for comparing shape between configurations. In this study, we will investigate the relationship between physical fitness and basic skill factors of KTA players of using a partial canonical correlation biplot and Procrustes analysis. We compare shapes and shape variabilities for the generalized, partial and simple canonical correlation biplots.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1317-1325
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• 2015

There are two types of clinical research to figure out risk factor for disease using collected data. One is prospective study to approach the subjects from the present time and the other is retrospective study to find the risk factor using the subject's information in the past. Both approached and study design are different but the purpose of the two studies is to identify a significant difference between two groups and to find out what the variables to influence groups. Especially when comparing the two groups in clinical research, we have to look at the difference between the impact clinical variables by group while controlling the influence of the baseline characteristics variables such as age and sex. However, in the retrospective study, the difference of baseline characteristic variables can occur more frequently because the past records did not randomly assign subjects into two groups. In clinical data analysis use covariates to solve this problem. Typically, the analysis method using the analysis of covariance of variance, adjusted model, and propensity score matching method. This study is introduce the way of equality adjustment between groups data analysis using covariates in retrospective clinical studies and apply it to the recurrence of gastric cancer data.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.94-101
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• 1994

본 논문은 패널(panel) 자기회귀 모형에서 자기회귀 계수의 추정을 베이지안 방법으로 접근하였는데, 이 때 특별히 Gibbs Sampling 방법을 이용하여 사후분포를 계산하였다. 또한 모의 실험을 통하여 자기회귀계수를 Gibbs Sampling 방법으로 추정한 베이지안 추정치가 non-Bayesian 방법으로 구한 추정치보다 더 우월함을 보였다.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.311-322
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• 1998

In this paper the problem of modeling count data where the observation period is determined by the survival time of the individual under study is considered. We assume marginal frailty model in the counts. We assume that the death times follow a Weibull distribution with a rate that depends on some covariates. For the counts, given a frailty, a Poisson process is assumed with the intensity depending on time and the covariates. A gamma model is assumed for the frailty. Maximum likelihood estimators of the model parameters are obtained. The model is applied to data set of patients with breast cancer who received a bone marrow transplant. A model for the time to death and the number of supportive transfusions a patient received is constructed and consequences of the model are examined.

• The Korean Journal of Elementary Counseling
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• v.11 no.1
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• pp.107-122
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• 2012

This study examined the effects of gratitude enrichment program on the school adjustment, life satisfaction, and emotional-behavioral problems of children. The subjects were 5th grade 69 boys and girls(experimental group: 34, control group: 35). School adjustment, life satisfaction, and emotional-behavioral problems were measured before and after the program intervention. The findings showed that the gratitude enrichment program increased the school adjustment and life satisfaction. But it was not effective in decreasing the emotional-behavioral problems of children. The importance and necessity of gratitude enrichment program and education activities were discussed.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.427-439
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• 2017

Quade (1967) proposed RANK ANCOVA, which is a nonparametric method to test differences between treatments when there are covariates. Hwang and Kim (2012) also proposed a joint placement test on covariate-adjusted residuals. In this paper, we proposed a new nonparametric method to control the effect of covariate on a response variable that uses linear statistics on covariate adjusted-residuals. The score function used in the linear statistics was proposed by Jeon and Kim (2016). Monte Carlo simulation is also conducted to compare the empirical powers of the proposed method with previous methods.

• Survey Research
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• v.10 no.3
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• pp.21-36
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• 2009

This study suggests the applicability of web surveys regarding elections in order to contact a great number of young people. The propensity weighting model was estimated using the demographic variables and the covariate variables collected during the 2007 presidential election surveys. In order to adjust the internet survey to the telephone survey, we used the propensity score method. Propensity score weighting made the internet survey results closer to the telephone survey results. This shows that an internet survey with propensity weighting model is a potential alternative survey method in the prediction of elections.

• Women's Health Nursing
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• v.4 no.2
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• pp.162-172
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• 1998

모성역할 획득에 영향을 주는 변수를 이해한다는 것은 모성역할 획득에 대한 체계적인 연구와 실질적인 간호중재를 가능하게 한다. 생후 4개월된 첫 아기가 있는 어머니를 대상으로 모성역할 획득모형을 발전시키려고 이 연구를 시도하였다. 가설적 모형은 5개의 변수(모성역할 획득, 모성역할 긴장, 일상생활의 시간적 여유, 아기 기질, 사회적지지)로 구성되었다. 대전시에 거주하는 대상자에게 전화를 통하여 동의를 얻은 후, 우편으로 250명에게 질문지를 보내어 161명(64.4%)으로부터 회수되었다. 자료를 Lisrel 프로그램을 이용하여 공변량구조분석을 한 결과, 자료와 가설적 모형과의 전반적 지수는 자유도 21에 카이자승치 31.47, GFI와 AGFI의 값은 .970, .935였고, RMSR, NFI, NNFI는 각각 1.668, .9097, .9425이었다. 간명하고 부합도가 더 좋은 모형을 찾기 위하여 통계적 결과와 이론적 적합성에 따라 2경로(아기기질에서 모성역할 획득, 모성역할 긴장에서 모성역할 획득)를 고정시켰다. 수정모형의 PFI (간명도)가 .7297로 증가하여 수정모형이 더 간명하면서도 부합도가 좋은 모형으로 나타났다. 공변량구조분석 결과 나타난 변인간의 관계는 일상생활의 시간적 여유가 많을수록, 사회적 지지를 많이 받을수록, 아기기질이 순하다고 생각할수록 모성역할 긴장은 적었고, 일상생활의 시간적 여유가 많을수록, 사회적 지지를 많이 받을수록 모성역할 획득은 잘 되었다.