• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.697-712
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• 2013

When analyzing repeated binary data, the generalized estimating equations(GEE) approach produces consistent estimates for regression parameters even if an incorrect working correlation matrix is used. However, time-varying covariates experience larger changes in coefficients than time-invariant covariates across various working correlation structures for finite samples. In addition, the GEE approach may give biased estimates under missing at random(MAR). Weighted estimating equations and multiple imputation methods have been proposed to reduce biases in parameter estimates under MAR. This article studies if the two methods produce robust estimates across various working correlation structures for longitudinal binary data with time-varying covariates under different missing mechanisms. Through simulation, we observe that time-varying covariates have greater differences in parameter estimates across different working correlation structures than time-invariant covariates. The multiple imputation method produces more robust estimates under any working correlation structure and smaller biases compared to the other two methods.

• Communications for Statistical Applications and Methods
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• v.21 no.5
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• pp.387-393
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• 2014

Recurrent gap times are analyzed with diverse methods under several assumptions such as a marginal model or a frailty model. Several resampling techniques have been recently suggested to estimate the covariate effect; however, these approaches can be applied with a time-fixed covariate. According to simulation results, these methods result in biased estimates for a time-varying covariate which is often observed in a longitudinal study. In this paper, we extend a resampling method by incorporating new weights and sampling scheme. Simulation studies are performed to compare the suggested method with previous resampling methods. The proposed method is applied to estimate the effect of an educational program on traffic conviction data where a program participation occurs in the middle of the study.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.315-323
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• 2019

Panel data sets have recently been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Often a dichotomous dependent variable occur in survival analysis, biomedical and epidemiological studies that is analyzed by a generalized linear mixed effects model (GLMM). The most common estimation method for the binary panel data may be the maximum likelihood (ML). Many statistical packages provide ML estimates; however, the estimates are computed from numerically approximated likelihood function. For instance, R packages, pglm (Croissant, 2017) approximate the likelihood function by the Gauss-Hermite quadratures, while Rchoice (Sarrias, Journal of Statistical Software, 74, 1-31, 2016) use a Monte Carlo integration method for the approximation. As a result, it can be observed that different packages give different results because of different numerical computation methods. In this note, we discuss the pros and cons of numerical methods compared with the exact computation method.

• Health Policy and Management
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• v.33 no.3
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• pp.295-310
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• 2023

Background: The long-term care (LTC) group has higher rates of chronic disease and disability registration compared to the general older people population. There is a need to provide integrated medical services and care for LTC group. Consequently, this study aimed to identify medical usage patterns based on the ratings of LTC and the characteristics of benefits usage in the LTC group. Methods: This study employed the National Health Insurance Service Database to analyze the effects of demographic and LTC-related characteristics on medical usage from 2015 to 2019 using a repeated measures analysis. A longitudinal logit model was applied to binary data, while a linear mixed model was utilized for continuous data. Results: In the case of LTC ratings, a positive correlation was observed with overall medical usage. In terms of LTC benefit usage characteristics, a higher overall level of medical usage was found in the group using home care benefits. Detailed analysis by medical institution classification revealed a maintained correlation between care ratings and the volume of medical usage. However, medical usage by classification varied based on the characteristics of LTC benefit usage. Conclusion: This study identified a complex interaction between LTC characteristics and medical usage. Predicting the requisite medical services based on the LTC rating presented a challenge. Consequently, it becomes essential for the LTC group to continuously monitor medical and care needs, even after admission into the LTC system. To facilitate this, it is crucial to devise an LTC rating system that accurately reflects medical needs and to broaden the implementation of integrated medical-care policies.

• Journal of Family Resource Management and Policy Review
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• v.17 no.3
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• pp.39-58
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• 2013

The purpose of this study was to identify the factors determining the participation of the poor elderly in economic activity. This study analyzed secondary data of the second wave of Korean Longitudinal Study of Ageing. Binary logistic regression was used to identify the factors that are associated with the economic activity of the poor elderly. The results of the analyses showed that age, gender, region, public assistance, education, health status, chronic illness, contacts with acquaintances, and support from children were associated with participation in economic activity. The study's findings have several implications for policies and services. The study identified the need for an age- and gender-specific approach to promoting participation in economic activity among the poor elderly. Regional differences should also be considered in the creation of work opportunities for older adults. In terms of human capital, the positive effect of good health indicates that strategies are needed to address the needs of older adults with health issues. In addition, there is a need for more jobs for elderly job seekers with high levels of education. Finally, policy makers and practitioners should explore interventions for enhancing the social network involvement and community support for the elderly living in poverty.

• 한국노년학
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• v.30 no.4
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• pp.1145-1161
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• 2010

The objectives of this study were to examine how social networks of old retirees impact on their retirement satisfaction, and through this, to suggest ways of improving their retirement satisfaction. Data used in this study were from 2006 KLoSA(Korean Longitudinal Study of Ageing), which were collected from 1,009 elderly people aged 65 and over who resided metropolis and smaller medium cities and answered regarding their retirement satisfaction. Data were analyzed by Binary Logistic Regression method. As a result, the frequency of contact with children, the number of participation in their social activities, and the satisfaction of relationship with children were the significant variables to predict retirement satisfaction. In addition, other variables such as gender, subjective health status, type of retirement, and duration of past retirement have been found as significant variables to explain retirement satisfaction. Implications for designing effective retirement plan and service systems have been discussed.

• Journal of Preventive Medicine and Public Health
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• v.42 no.5
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• pp.275-282
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• 2009

Objectives : This study aimed to verify the association between wealth or income level and health status after adjusting for other socio-economic position (SEP) indicators among Korean adults aged 45 and over. Methods : Data were obtained from the 1st wave of Korean Longitudinal Study of Ageing (households: 6,171, persons: 10,254). We used self-rated health status and activities of daily living (ADLs) as dependent variables. Explanatory variables included both net wealth measured by savings, immovables, the other valuated assets and total income including pay, transfer, property and so on. Binary logistic regression was conducted to examine the relationships. Also, in order to determine the relative health inequality across economic groups, we estimated the relative index of inequality (RII). Results : The inequality of health status was evident among various wealth and income groups. The wealthiest group (5th quintile) was much healthier than the poorest group, and this differential increased with age. Likewise, higher income was associated with better health status among the elderly. However, these effects, as measured by the odds ratio and RII, showed that wealth was more important in determining health status of elderly people. Conclusions : This study suggests that economic capability plays a significant role in determining the health status and other health-related problems among the elderly. Particularly, our results show that health status of the aged is related more closely to the individual s wealth than income.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.293-301
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• 2019

The purpose of this study is to redefine 7 social exclusion factors and to derive association between social exclusion factors and depression of middle and older elderly, focused on gender differences. The elderly aged over 50 years old were extracted from the data of the Korean Longitudinal Study of Ageing (KLoSA) in 2016. The data was analyzed by descriptive analysis, chi-squared test, and binary logistic regression analysis, using SAS 9.4. As a result, the elderly who were excluded from labor, residence, health, and relationship were found to be associated to depression. In addition, male with resident exclusion were associated with depression compared to non-excluded. Therefore, it is necessary to improve the local community programs in which elderly people can participate in labor and cultural activity. It is necessary to improve labor and culture exclusion by increasing the participation opportunities of various cultural programs. In addition, it is needed to establish specific guidelines for eliminating the social exclusion of overall elderly.

• Korean Journal of Family Social Work
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• no.55
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• pp.129-156
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• 2017

The study was conducted to identify factors that affect the first childbearing decisions among married working women in South Korea and thereby to gain insight into effective ways to improve relatively low fertility and unfavorable workplace conditions for women of childbearing age. This study used the merged data(1st-5th waves) from Korean Longitudinal Survey of Women and Families (KLoWF), which were measured by the Korean Women's Development Institute. With 1,020 married business women, cross tab and binary logistic regression were conducted to determine which factors were significantly associated with the first childbearing decisions.