• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.100-106
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• 2022

A longitudinal study refers to a research method based on longitudinal data repeatedly measured on the same object. Most of the longitudinal analysis methods are suitable for prediction or inference, and are often not suitable for use in exploratory study. In this study, an exploratory method to analyze longitudinal data is presented, which is to find the longitudinal trajectory after determining the best number of clusters by clustering longitudinal data using self-organizing map technique. The proposed methodology was applied to the longitudinal data of the Employment Information Service, and a total of 2,610 samples were analyzed. As a result of applying the methodology to the actual data applied, time-series clustering results were obtained for each panel. This indicates that it is more effective to cluster longitudinal data in advance and perform multilevel longitudinal analysis.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.363-369
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• 2010

In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1459-1473
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• 2016

Both longitudinal data and survival data are collected simultaneously in longitudinal data which are observed throughout the passage of time. In this case, the effect of the independent variable becomes biased (provided that sole use of longitudinal data analysis does not consider the relation between both data used) if the missing that occurred in the longitudinal data is non-ignorable because it is caused by a correlation with the survival data. A joint model of longitudinal data and survival data was studied as a solution for such problem in order to obtain an unbiased result by considering the survival model for the cause of missing. In this paper, a joint model of the longitudinal zero-inflated count data and survival data is studied by replacing the longitudinal part with zero-inflated count data. A hurdle model and proportional hazards model were used for each longitudinal zero inflated count data and survival data; in addition, both sub-models were linked based on the assumption that the random effect of sub-models follow the multivariate normal distribution. We used the EM algorithm for the maximum likelihood estimator of parameters and estimated standard errors of parameters were calculated using the profile likelihood method. In simulation, we observed a better performance of the joint model in bias and coverage probability compared to the separate model.

• Korean Medical Education Review
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• v.25 no.2
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• pp.84-101
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• 2023

Longitudinal data can provide important evidence with the potential to stimulate innovation and affect policies in medical education and can serve as a driving force for further developments in medical education through evidence-based decisions. Tracking and observing cohorts of students and graduates using longitudinal data can be a way to link the past, present, and future of medical education. This study reviewed practical methods and technical, administrative, and ethical considerations for the establishment and operation of a longitudinal database and presented examples of longitudinal databases. Cohort study design methods and previous examples of research using longitudinal databases to explore major topics in medical education were also reviewed. The implications of this study are as follows: (1) a systematic design process is required to establish longitudinal data, and each university should engage in ongoing deliberation about this issue; (2) efforts are needed to alleviate "survey fatigue" among respondents and reduce the administrative burden of those conducting data collection and analysis; (3) it is necessary to regularly review issues of personal information protection, data security, and ethics regarding the survey respondents; and (4) a system should be established that integrates and manages a longitudinal database of medical education at the national level. The hope is that establishing longitudinal data and cohorts at individual medical schools will not be a temporary phenomenon, but rather that they will be well utilized at the national level to innovate and implement ongoing changes in medical education.

• Korean Medical Education Review
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• v.24 no.2
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• pp.156-171
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• 2022

The purpose of this study was to establish a model for constructing longitudinal data for medical school, and to structure cohort and longitudinal data using data from Yonsei University College of Medicine (YUCM) according to the established input-environment-output (I-E-O) model. The study was conducted according to the following procedure. First, the data that YUCM has collected was reviewed through data analysis and interviews with the person in charge of each questionnaire. Second, the opinions of experts on the validity of the I-E-O model were collected through the first expert consultation, and as a result, a model was established for each stage of medical education based on the I-E-O model. Finally, in order to further materialize and refine the previously established model for each stage of medical education, secondary expert consultation was conducted. As a result, the survey areas and time period for collecting longitudinal data were organized according to the model for each stage of medical education, and an example of the YUCM cohort constructed according to the established model for each stage of medical education was presented. The results derived from this study constitute a basic step toward building data from universities in longitudinal form, and if longitudinal data are actually constructed through this method, they could be used as an important basis for determining major policies or reorganizing the curricula of universities. These research results have implications in terms of the management and utilization of existing survey data, the composition of cohorts, and longitudinal studies for many medical schools that are conducting surveys in various areas targeting students, such as lecture evaluation and satisfaction surveys.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.369-391
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• 2021

The vlda is an R (R Development Core team et al., 2011) package which provides functions for visualization of multidimensional longitudinal data. In particular, the R package vlda was developed to assist in producing a plot that more effectively expresses changes over time for two different types (long format and wide format) and uses a consistent calling scheme for longitudinal data. The main features of this package allow us to identify the relationship between categories and objects using an indicator matrix with object information, as well as to cluster objects. The R package vlda can be used to understand trends in observations over time in addition to identifying relative relationships at a simple visualization level. It also offers a new interactive implementation to perform additional interpretation, therefore it is useful for longitudinal data visual analysis. Due to the synergistic relationship between the existing VLDA plot and interactive features, the user is empowered by a refined observe the visual aspects of the VLDA plot layout. Furthermore, it allows the projection of supplementary information (supplementary objects and variables) that often occurs in longitudinal data of graphs. In this study, practical examples are provided to highlight the implemented methods of real applications.

• Genomics & Informatics
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• v.20 no.2
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• pp.16.1-16.12
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• 2022

Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1053-1065
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• 2006

This paper proposes a method in one-way layouts for longitudinal data with informative drop-out. When dropouts are informative, that is, correlated with unobserved data and/or the previous observed data, the simple imputation methods such as 'last observation carried forward' (LOCF) methods would arise the bias of the testing models. The maximum likelihood procedure combined with a logit model for the drop-out process is proposed to test treatment effects for one factorial designs and compared with LOCF method in two examples.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.589-598
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• 2015

In longitudinal studies missing data are common and require a complicated analysis. There are two popular modeling frameworks, pattern mixture model (PMM) and selection models (SM) to analyze the missing data. We focus on the PMM and we also propose Bayesian pattern mixture models using generalized linear mixed models (GLMMs) for longitudinal binary data. Sensitivity analysis is used under the missing not at random assumption.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.227-234
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• 2002

In this paper, we consider hierarchical Bayes generalized linear models for the analysis of longitudinal count data. Specifically we introduce the hierarchical Bayes random effects models. We discuss implementation of the Bayes procedures via Markov chain Monte Carlo (MCMC) integration techniques. The hierarchical Baye method is illustrated with a real dataset and is compared with other statistical methods.