• Genomics & Informatics
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• 제20권3호
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• pp.34.1-34.11
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• 2022

Multilevel analysis is an appropriate and powerful tool for analyzing hierarchical structure data widely applied from public health to genomic data. In practice, however, we may lose the information on multiple nesting levels in the multilevel analysis since data may fail to capture all levels of hierarchy, or the top or intermediate levels of hierarchy are ignored in the analysis. In this study, we consider a multilevel linear mixed effect model (LMM) with single imputation that can involve all data hierarchy levels in the presence of missing top or intermediate-level clusters. We evaluate and compare the performance of a multilevel LMM with single imputation with other models ignoring the data hierarchy or missing intermediate-level clusters. To this end, we applied a multilevel LMM with single imputation and other models to hierarchically structured cohort data with some intermediate levels missing and to simulated data with various cluster sizes and missing rates of intermediate-level clusters. A thorough simulation study demonstrated that an LMM with single imputation estimates fixed coefficients and variance components of a multilevel model more accurately than other models ignoring data hierarchy or missing clusters in terms of mean squared error and coverage probability. In particular, when models ignoring data hierarchy or missing clusters were applied, the variance components of random effects were overestimated. We observed similar results from the analysis of hierarchically structured cohort data.

• 응용통계연구
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• 제27권1호
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• pp.89-102
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• 2014

본 연구에서는 질병관리본부에서 매년 조사하고 있는 지역사회 건강조사 자료를 이용하여 서울시 지역을 대상으로 개인의 흡연 여부에 대한 영향 요인을 확인하고 지역간 차이를 모형에 반영시키는 다수준 로지스틱 모형을 이용하여 분석하였다. 다수준 모형에서의 적합한 분석 모형의 수준을 결정하기 위해 ICC(intraclass correlation coefficient)와 프로파일링 분석, 수준별 모형의 예측정확도를 이용하였다. 제안된 모형들의 성능을 평가하기 위해 민감도, 특이도, 정확도를 구하고 ROC curve를 작성하였다. 결과적으로 지역사회 건강조사 자료와 같이 개인과 집단 변수를 동시에 고려할 수 있다면 다양한 다수준 모형의 적용이 가능하며 활용성이 높다는 것을 알 수 있었다.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.701-718
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• 2006

In cancer microarray experiments, the experimenter or patient which is nested in each experimenter often shows quite heterogeneous error variability, which should be estimated for identifying a source of variation. Our study describes a Bayesian method which utilizes clinical information for identifying a set of DE genes for the class of subtypes as well as assesses and examines the experimenter effect and patient effect which is nested in each experimenter as a source of variation. We propose a Bayesian multilevel mixed effect model based on analysis of covariance (ANACOVA). The Bayesian multilevel mixed effect model is a combination of the multilevel mixed effect model and the Bayesian hierarchical model, which provides a flexible way of defining a suitable correlation structure among genes.

• 대한토목학회논문집
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• 제31권3D호
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• pp.469-476
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• 2011

본 연구는 다층모형(multilevel model)을 상수원관리지역 주민지원사업 정책평가에 활용하는데 있어 유효성 및 효과를 이해하는데 연구의 목적이 있다. 정책의 대상이 위계적인(hierarchical) 특성을 지니고 있는 경우 다층모형을 이용하여 정책평가를 하는 것이 정책의 효과를 평가하고 검증하는데 있어 적절하다. 또한 이 모형은 기존 평가결과의 재평가 등에 응용될 수 있다. 상수원관리지역의 주민지원사업을 대상으로 주민만족도와 경제적 도움정도를 정책효과변수로 하여 다층모형을 적용 하였다. 그 결과 정책효과변수에 기존 평가결과 및 중요한 사업으로 여겨지던 소득증대사업 등이 직접적인 영향을 미치지 못함을 통계적으로 확인할 수 있었다. 본 연구는 다음과 같은 세 가지의 정책적 함의를 지닌다. 첫째, 정책 및 사업평가에 있어 정책의 효과를 엄밀히 파악하기 위하여 위계적 구조를 고려할 수 있는 다층모형을 이용하여야 한다. 둘째, 기관평가의 순위 도출에 있어 다층모형을 이용하여 지표에 의한 성과지표와 보완적으로 이용해야 한다. 셋째, 위계적 구조의 하나로서 공간적 위계를 고려하여 정책평가를 시행하여야 한다.

• 보건교육건강증진학회지
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• 제30권1호
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• pp.53-64
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• 2013

Objectives: In this study, a multilevel analysis model has been designed to investigate the effect of personal characteristics associated with smoking cessation on anti-smoking determinants with a goal of finding out the factors which have influence on smoking cessation among the entrants of smoking cessation clinic in a public health center. Methods: A total of 253,136 male smokers who received smoking cessation services for more than six(6) months in a smoking cessation clinic of public health center from July 16, 2007 to July 15, 2008 were examined. For technical analysis, SPSS Version 2.0 has been used. For multilevel analysis on smoking cessation determinants, in addition, HLM 7.0 has been adopted. Results: According to the unconditional model of multilevel analysis, the success rates of smoking cessation among the entrants of a smoking cessation clinic were 47.3%. In an unconditional slope model test to which regional variables were added, a negative effect was observed in average smoking amount, total smoking period, nicotine dependence and services while a positive effect was found in age, stress and type of social security in terms of the log of the odds of smoking cessation. In a conditional model test, a positive effect was observed in Non-Smoking Campaigns (NSC) and Frequency of Counseling (FC) in terms of the log of the odds of smoking cessation in regional variables. Conclusions: It is important to approach smokers individually and, at the same time, build healthy environment for a local community to increase smoking cessation rates among the entrants of smoking cessation clinic in a public health center.

• 한국병원경영학회지
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• 제20권1호
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• pp.53-64
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• 2015

Our objective was to evaluate differences in linear regression versus multilevel(cross-level interaction model) modeling for affecting factors lumbar discectomy. The data were used in 2011 patients with HIRA sample data. Total number of analysis is 3,641 patients and 248 hospitals. The results of research model showed that the type and location of the hospital-level factors were significant. However, all factors of patient-level were similar in the two models. Therefore, it requires the selection of an appropriate model for a more accurate analysis of the influencing factors in the daily medical charge.

• 감성과학
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• 제19권4호
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• pp.95-110
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• 2016

본 연구의 목적은 다층자료 매개효과의 분석 방법을 제안하는 것이다. 연구내용은 다층자료 매개효과의 통계방법 탐색, 분석절차 제안, 그리고 분석의 실례 제시 등 세 가지다. 첫째, MLM (multilevel modeling)과 MSEM (multilevel structural equation modeling) 중에서 어떤 방법이 다층자료의 매개효과 분석에 유용한지 탐색하였다. MSEM은 MLM의 약점을 극복한 것으로서 유용한 다층 매개효과 분석방법이었다. 둘째, 다층자료 매개효과의 분석절차를 연구모형설정, 전제조건 검토, 모형검증, 계수검증의 4단계로 전개하였다. 셋째, 매개효과 분석의 실례에 사용된 자료는 2,695명의 초중등 학생과 88명의 학급교사로 구성되었다. 분석 실례로 2층${\rightarrow}$2층${\rightarrow}$1층과 1층${\rightarrow}$1층${\rightarrow}$2층 두 가지를 제시하였다. 2층${\rightarrow}$2층${\rightarrow}$1층과 1층${\rightarrow}$1층${\rightarrow}$2층 모형은 완전매개모형이 지지되었지만, 2층${\rightarrow}$2층${\rightarrow}$1층 모형의 매개효과 계수만 95% 신뢰구간에서 유의하였다. 분석 실례에 사용된 Mplus 프로그램은 부록에 제시하였다. 연구결과를 기초로 본 연구의 의의와 제한점, 그리고 후속연구의 방향이 논의되었다.

• 지역사회간호학회지
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• 제28권4호
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• pp.482-493
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• 2017

Purpose: This study aims to investigate the individual and environmental factors related to emotional/behavioral problems to early adolescence in Korea by applying multilevel modeling. Methods: From the database of the 2014 Korean Child and Youth Panel Survey (KCYPS), the researchers selected 1,977 adolescents who are in the second year of middle school. Multilevel model analysis was performed to estimate the impact of relevant factors at the individual and environmental levels. Results: At the individual level, the significant factors associated with emotional/behavioral problems included BMI and study tendency in boys, and drinking, study tendency and economic levels in girls. At the environmental level, the significant factor associated with emotional/behavioral problems included relationship with the teacher. Conclusion: The emotional/behavioral problems of early adolescence are influenced not only by the individual factors but also by the environment factor. Therefore, the environment surrounding the adolescents should also be considered to prevent emotional/behavioral problems.

• 융합경영연구
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• 제10권5호
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• pp.1-6
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• 2022

Purpose: To overcome the question that depends too much on expert's subjective judgment in traditional risk identification, this paper structure the multilevel generalized fuzzy comprehensive evaluation mathematics model of the risk identification of project, to research the risk identification of the project. Research design, data and methodology: This paper constructs the multilevel generalized fuzzy comprehensive evaluation mathematics model. Through iterative algorithm of AHP analysis, make sure the important degree of the sub project in risk analysis, then combine expert's subjective judgment with objective quantitative analysis, and distinguish the risk through identification models. Meanwhile, the concrete method of multilevel generalized fuzzy comprehensive evaluation is probed. Using the index weights to analyse project risks is discussed in detail. Results: The improved fuzzy comprehensive evaluation algorithm is proposed in the paper, at first the method of fuzzy sets core is used to optimize the fuzzy relation matrix. It improves the capability of the algorithm. Then, the method of entropy weight is used to establish weight vectors. This makes the computation process fair and open. And thereby, the uncertainty of the evaluation result brought by the subjectivity can be avoided effectively and the evaluation result becomes more objective and more reasonable. Conclusions: In this paper, we use an improved fuzzy comprehensive evaluation method to evaluate a railroad engineering project risk. It can give a more reliable result for a reference of decision making.

• 보건의료산업학회지
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• 제9권2호
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• pp.81-88
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• 2015

The purpose of this study was to compare the risk-adjusted in-hospital mortality for craniotomies between logistic regression and multilevel analysis. By using patient sample data from the Health Insurance Review & Assessment Service, in-patients with a craniotomy were selected as the survey target. The sample data were collected from a total number of 2,335 patients from 90 hospitals. The sample data were analyzed with SAS 9.3. From the results of the existing logistic regression analysis and multilevel analysis, the values from the multilevel analysis represented a better model than that of logistic regression. The intra-class correlation (ICC) was 18.0%. It was found that risk-adjusted in-hospital mortality for craniotomies may vary in every hospital. The agreement by kappa coefficient between the two methods was good for the risk-adjusted in-hospital mortality for craniotomies, but the factors influencing the outcome for that were different.