• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.200-201
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• 2005

• Journal of Periodontal and Implant Science
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• v.45 no.1
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• pp.2-7
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• 2015

A fundamental problem in analyzing complex multilevel-structured periodontal data is the violation of independency among the observations, which is an assumption in traditional statistical models (e.g., analysis of variance and ordinary least squares regression). In many cases, aggregation (i.e., mean or sum scores) has been employed to overcome this problem. However, the aggregation approach still exhibits certain limitations, such as a loss of power and detailed information, no cross-level relationship analysis, and the potential for creating an ecological fallacy. In order to handle multilevel-structured data appropriately, mixed effects models have been introduced and employed in dental research using periodontal data. The use of mixed effects models might account for the potential bias due to the violation of the independency assumption as well as provide accurate estimates.

• Genomics & Informatics
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• v.20 no.3
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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.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.923-932
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• 2008

We are likely to face complex multivariate data which can be characterized by having a non-trivial correlation structure. For instance, omitted covariates may simultaneously affect more than one count in clustered data; hence, the modeling of the correlation structure is important for the efficiency of the estimator and the computation of correct standard errors, i.e., valid inference. A standard way to insert dependence among counts is to assume that they share some common unobservable variables. For this assumption, we fitted correlated random effect models considering multilevel model. Estimation was carried out by adopting the semiparametric approach through a finite mixture EM algorithm without parametric assumptions upon the random coefficients distribution.

• 한국노년학
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• v.41 no.3
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• pp.397-420
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• 2021

The paper tried to determine relationships between volunteering and hypertension risks, symptom betterment, activity difficulty occurrences, and medicine treatment among middle-aged and older adults, with a prospective cohort study. Multilevel mixed-effects generalized linear models were used for the analysis of longitudinal panel data collected over 10 years from 2008 to 2018, using 5,867 cohort samples. The results showed that those who volunteered at least 200 hours per year were 3.4 times more lower than not-volunteering in risks of hypertension, those who volunteered yearly 50~99 hours were a lot more improved than not-volunteering in the symptom betterment, those who volunteered yearly at least 200 hours were 7.7 times lower than not-volunteering in activity difficulty occurrences, and those who volunteered yearly 50~99 hours were 2.5 times lower than not-volunteering in the occurrences of medicine treatment. These indicate that volunteering among middle-aged and older adults may have health benefits against incident hypertension. Finally the thesis discusses the study limitations, future directions of studies, and the practices implications.