• Proceedings of the Korean Reliability Society Conference
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• 2005.06a
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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.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.47-55
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• 2019

It is important to identify informative variables in high dimensional data analysis; however, it becomes a challenging task when covariates are contaminated by measurement error due to the bias induced by measurement error. In this article, we present a two-step approach for variable selection in the presence of measurement error. In the first step, we directly select important variables from the contaminated covariates as if there is no measurement error. We then apply, in the following step, orthogonal regression to obtain the unbiased estimates of regression coefficients identified in the previous step. In addition, we propose a modification of the two-step approach to further enhance the variable selection performance. Various simulation studies demonstrate the promising performance of the proposed method.

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.359-370
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• 2019

Grouping structures in covariates are often ignored in regression models. Recent statistical developments considering grouping structure shows clear advantages; however, reflecting the grouping structure on the quantile regression model has been relatively rare in the literature. Treating the grouping structure is usually conducted by employing a group penalty. In this work, we explore the idea of group penalty to the quantile regression models. The grouping structure is assumed to be known, which is commonly true for some cases. For example, group of dummy variables transformed from one categorical variable can be regarded as one group of covariates. We examine the group quantile regression models via two real data analyses and simulation studies that reveal the beneficial performance of group quantile regression models to the non-group version methods if there exists grouping structures among variables.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.269-278
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• 2011

There are many researches that have considered the distribution functions and appropriate covariates corresponding to the scores in order to improve the accuracy of a diagnostic test, including the ROC curve that is represented with the relations of the sensitivity and the specificity. The ROC analysis was used by the regression model including some covariates under the assumptions that its distribution function is known or estimable. In this work, we consider a general situation that both the distribution function and the elects of covariates are unknown. For the ROC analysis, the mixtures of normal distributions are used to estimate the distribution function fitted to the credit evaluation data that is consisted of the score random variable and two sub-populations of parameters. The AUC measure is explored to compare with the nonparametric and empirical ROC curve. We conclude that the method using normal mixtures is fitted to the classical one better than other methods.

• Health Policy and Management
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• v.19 no.4
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• pp.66-77
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• 2009

This study aims to examine the effect of socioeconomic status (hereafter, SES) on healthcare utilization of the patients with rare and incurable diseases. Information of 2,973 patients who were self-employed insured and utilized healthcare service in 2007 was drawn from the National Health Insurance (hereafter, NHI) claim data. SES was set as four groups based on the monthly contribution. Outcome variable was the expense for outpatient and in-hospital services, which was log-transformed and square-rooted in oder to obtain normal distribution. Covariates included age, gender, residence and diagnosis. To examine the effects after controlling for covariates, we employed generalized estimating equation model, since patients with the same diagnosis are likely to have similar characteristics of demographics and healthcare utilization. Univariate statistics showed that lower SES was associated with less utilization of healthcare services. After controlling for covariates, a significantly smaller amount of money was expended for the lowest SES group compared to the highest one. Rural residence was associated with less utilization, except that residents in Seoul significantly more utilized outpatient services in tertiary hospitals. Considering that there is a subsidy program for the low income patients, such differences in healthcare utilization according to SES seems to result from the burden of out-of-pocket payments for uncovered services of the NHI.

• 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.

• Korean Journal of Family Medicine
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• v.39 no.6
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• pp.340-346
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• 2018

Background: Several studies have revealed the frequency of family mealtimes to be inversely associated with depressive symptoms in adolescents. However, there have been few studies in older populations. This cross-sectional study investigated the association between family mealtime frequency and depressive symptoms in elderly Koreans. Methods: This study analyzed 4,959 elderly men and women (aged 65 years or older) who participated in the Korea National Health and Nutrition Examination Survey. Self-administered questionnaires were used to assess depressive status, family mealtime frequency, and covariates. Multiple logistic regression analysis was performed to evaluate the association using the eating alone group as a reference. Results: After adjusting for all covariates, participants who had family meals 3 times a day had fewer depressive symptoms than the eating alone group; adjusted odds ratios (ORs) (95% confidence intervals [CIs]) were 0.72 (0.58-0.89) for point depressiveness/anxiety and 0.73 (0.56-0.94) for depressiveness lasting for at least 2 weeks. In suicidal ideation, the OR (95% CI) of eating with family twice a day was significant after full adjusting for covariates at 0.67 (0.50-0.88). Conclusion: Family mealtimes were closely associated with depressive symptoms in elderly Koreans, which suggests that maintaining intrafamilial bonding is important for mental health in an older population.

• Nutrition Research and Practice
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• v.16 no.5
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• pp.673-684
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• 2022

BACKGROUND/OBJECTIVES: Although adherence to a higher diet quality may help prevent cognitive decline in older adults, literature for this in a Korean population is limited. Thus, the aim of this study was to examine the association between diet quality indices and the risk of mild cognitive impairment (MCI) in Korean older adults. SUBJECTS/METHODS: This cross-sectional study included 806 community-dwelling people aged 60 yrs and over in Korea. Diet quality was assessed via the revised Recommended Food Score (RFS) and alternate Mediterranean Diet Score (aMDS). Cognitive function was measured using a Korean version of the Mini-Mental State Examination (MMSE-KC). Associations between diet quality indices and MMSE-KC score were assessed with a general linear model after adjusting for covariates. Logistic regression was used to determine the association between diet quality indices and the risk of MCI. RESULTS: The prevalence of MCI was 35.3%. There were no significant trends between MMSE-KC scores and RFS and aMDS after adjusting for age, gender, education, exercise, living status, social activity, and alcohol drinking. Among total subjects, RFS was inversely associated with the risk of MCI after adjusting for covariates (Q5 vs. Q1; odds ratio [OR], 0.49; 95% confidence interval [CI], 0.28-0.83). Among total subjects and men, aMDS was inversely related to the risk of MCI after adjusting for covariates (Q5 vs. Q1; OR, 0.51; 95% CI, 0.29-0.89 for total subjects; Q5 vs. Q1; OR, 0.36; 95% CI, 0.15-0.83 for men). CONCLUSIONS: Our results demonstrate that high diet quality evaluated by RFS and aMDS is inversely associated with the risk of MCI. Thus, high quality diet may reduce or retard cognitive decline in the old population. Longitudinal studies are needed to determine the causal relationship between diet quality and the risk of MCI in the elderly.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.323-335
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• 2023

In causal analysis of high dimensional data, it is important to reduce the dimension of covariates and transform them appropriately to control confounders that affect treatment and potential outcomes. The augmented inverse probability weighting (AIPW) method is mainly used for estimation of average treatment effect (ATE). AIPW estimator can be obtained by using estimated propensity score and outcome model. ATE estimator can be inconsistent or have large asymptotic variance when using estimated propensity score and outcome model obtained by parametric methods that includes all covariates, especially for high dimensional data. For this reason, an ATE estimation using an appropriate dimension reduction method and semiparametric model for high dimensional data is attracting attention. Semiparametric method or sparse sufficient dimensionality reduction method can be uesd for dimension reduction for the estimation of propensity score and outcome model. Recently, another method has been proposed that does not use propensity score and outcome regression. After reducing dimension of covariates, ATE estimation can be performed using matching. Among the studies on ATE estimation methods for high dimensional data, four recently proposed studies will be introduced, and how to interpret the estimated ATE will be discussed.

• Journal of Society of Preventive Korean Medicine
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• v.28 no.1
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• pp.69-87
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• 2024

Objectives : This study is to investigate the current National Health Insurance Database cohort studies related to complications of Parkinson's Disease (PD) and suggest the design of Korean medical epidemiological studies of PD. Methods : Nationwide longitudinal studies of PD patients in South Korea were collected through Pubmed and the Korea Citation Index (KCI). We selected cohort studies that used the National Health Insurance Database in Korea and targeted Parkinson's disease patients. Studies published before February 2024 were categorized according to study designs. We examined variables and covariates, enroll dates and matching methods. Results : Of a total of 536 studies, 18 studies met the inclusion criteria. All studies used the National Health Insurance (NHI) Research Database and among them, 5 used sample data and one senior database. Studies can be classified into two types. 11 cohort studies were comparing PD patients and non-PD patients. Another type was 4 PD patients cohort studies. Most studies used two diagnostic codes (G20 and V124) for inclusion criteria. Enroll periods were from 2002 to 2017, and follow-up periods were from 7 to 14 years. 16 studies considered age and sex as covariates. 15 studies used the propensity score matching method to increase the level of causality. There was only one study related to the Korean medical treatment. Conclusion : In future cohort studies on Korean medical treatment, more attempts should be made to reveal the effect of the treatments on PD patients by defining inclusion criteria for patient groups, covariates, exposure variables, and assessment indicators more operatively.