• Journal of Nutrition and Health
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• v.28 no.1
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• pp.71-83
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• 1995

The purpose of the study was to develop generalized equations for estimating stature and weight for the nonambulatory elderly persons. Height weight recumbent knee height total ann length, midarm, waist and calf circumferences, triceps and subscapular skinfolds were measured from over 60 years old 315 ambulatory elderly. The equations to predict stature and weight were derived from participants in the validation sample and were applied to the participants in the cross-validation to test the accuracy and validity of equations. Stature and weight were significantly and negatively associated with age of women and similar patterns observed in men but associated to a slight degree. Knee height and total arm length were highly correlated with stature but the majority of the variances in stature was accounted for by knee height for both the men and women. In men, waist circumference was the most significantly correlated with weight and am, calf circumferences and so forth. But in women arm circumference was the highest then waist and calf circumference in order. The possible predictor variables to estimate of stature were knee height total arm length and age for both elderly men and women. Predictor variables to estimate of weight were recumbent measures of waist am, calf circumferences and knee height for both sexes. Inclusion of skinfold thickness measurements did not improve the prediction power of estimation for weight. When both equations developed from the present study and Chumlea's study were applied to cross-valida-tions samples, the equations derived from present study showed better accuracy and validity. The presentation of prediction equations using two, three, or four recommended measurements allows the selection of an equation based upon the measurements that are possible to collect on an individual basis.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.4
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• pp.121-133
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• 2015

The TANK model has been widely used in rainfall-runoff modeling due to its simplicity of concept and computation while achieving forecast accuracy. A major barrier to the model application is to determine parameter values for ungauged watersheds, leading to the need of a method for the parameter estimation. The objective of this study was to develop regression equations for estimating the 3th TANK model parameters considering their variations for the ungauged watersheds. Thirty watersheds of dam sites and stream stations were selected for this study. A genetic algorithm was used to optimize TANK model parameters. Watershed characteristics used in this study include land use percent, watershed area, watershed length, and watershed average slope. Generalized equations were derived by correlating to the optimized parameters and the watershed characteristics. The results showed that the TANK model, with the parameters determined by the developed regression equations, performed reasonably with 0.60 to 0.85 of Nash-Sutcliffe efficiency for daily runoff. The developed regression equations for the TANK model can be applied for the runoff simulation particularly for the ungauged watersheds, which is common for upstream of agricultural reservoirs in Korea.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.877-883
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• 2013

The quadratic inference functions (QIF) method proposed by Qu et al. (2000) and the generalized method of moments (GMM) for marginal regression analysis of longitudinal data with time-dependent covariates proposed by Lai and Small (2007) both are the methods based on generalized method of moment (GMM) introduced by Hansen (1982) and both use generalized estimating equations (GEE). Lai and Small (2007) divided time-dependent covariates into three types such as: Type I, Type II and Type III. In this paper, we compared these methods in the case of Type II and Type III in which full covariates conditional mean assumption (FCCM) is violated and interested in whether they can improve the results of GEE with independence working correlation. We show that in the marginal regression model with Type II time-dependent covariates, GMM Type II of Lai and Small (2007) provides more ecient result than QIF and for the Type III time-dependent covariates, QIF with independence working correlation and GMM Type III methods provide the same results. Our simulation study showed the same results.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.318-327
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• 2014

Today, Competition in employment among the youth is more intensified and this phenomena lead to youth unemployment problems. This study was conducted to found the influence of youth employment effort on labor participation. We studied what variables could affect motivation of employment preparation especially among undergraduate and graduated students. We used 3rd~5th(2009~2011) 'Youth Panel Data' designed by Korean Employment Information Service. Data we adjusted were male(3,481) female(3,770). We applied the Generalized Estimating Equations to Panel logit model. We found that job education and training, career guidance, job shadowing program, getting new certification, sex, age and change of gross income affected employment preparation with controling education factors, socio-economic factors. This study found the effort of employment preparation was significant impact on labor participation and showed an influence on each variable empirically. We suggest that the youth-unemployment problem there is a need to approach fundamental aspects.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.3
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• pp.941-944
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• 2012

Objective: The aim of current study was to evaluate the changes of health-related quality of life (HRQoL) and its clinical, demographic and socioeconomic determinants during chemotherapy and 4 months follow-up in women with breast cancer using a repeated measures framework. Methods and Materials: A double blind cohort study was performed in 100 breast cancer patients given fluorouracil, doxorubicin and cyclophosphamide (FAC) or docetaxel, doxorubicin, cyclophosphamide (TAC) in south of Iran. HRQoL was assessed at baseline, end of chemotherapy and four months thereafter using the QLQ-C30 questionnaire from European Organization for Research and Treatment of Cancer (EORTC). Generalized estimating equations (GEE) was applied for statistical analysis. Results: The mean of age at baseline was $48.5{\pm}10.6$. 70% and 14% of patients were married and smokers, respectively, and 20% suffered from another disease besides breast cancer. The results of GEE showed that after control for baseline scores, the HRQoL significantly improved over time. Although, the patients in FAC group had higher scores than the TAC group, the differences also diminished over time. Smoking, marital status and having child affected some scales of HRQoL. None of other variables were significantly related to HRQoL. Conclusion: Although patients in TAC groups had lower level of HRQoL over 8 months follow up, they experienced faster improvement than the FAC group. This implies that in long-term, improvements in TAC group are higher than FAC. Having children was positively correlated with HRQoL. Generally, there were no demographic and socio-economic differences in HRQoL in these patients between the chemotherapeutic regimens.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.407-426
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• 2003

We consider the missing covariates problem in generalized estimating equations(GEE) model. If the covariate is partially missing, GEE can not be calculated. In this paper, we study the performance of 7 imputation methods to handle missing covariates in GEE models, and the properties of GEE estimators are investigated after missing covariates are imputed for ordinal data of repeated measurements. The 7 imputation methods include i) Naive Deletion ii) Sample Average Imputation iii) Row Average Imputation iv) Cross-wave Regression Imputation v) Carry-over Imputation vi) Bayesian Bootstrap vii) Approximate Bayesian Bootstrap. A Monte-Carlo simulation is used to compare the performance of these methods. For the missing mechanism generating the missing data, we assume ignorable nonresponse. Furthermore, we generate missing covariates with or without considering wave nonresp onse patterns.

• Health Policy and Management
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• v.30 no.4
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• pp.467-478
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• 2020

Background: The purpose of this study is to explain the factors influencing the incurrence of catastrophic health expenditure of national health insurance households using panel data observed over a long period. Methods: The study targeted 3,652 households who had no censoring during the 11-year survey period (2007-2017) and householders whose insurance type was consistently maintained as national health insurance. Generalized estimating equations were adopted to identify factors affecting the occurrence of catastrophic health expenditure at 20%, 30%, and 40% threshold levels. A subgroup analysis was conducted by categorizing groups depending on the existence of the elderly in the household. Results: For the last 11 years, the incidence of catastrophic health expenditure in the households without the elderly decreased slightly at all threshold levels, but the households with the elderly seemed to be increased. At baseline, household type showed a statistically significant relationship with all other variables. The results of generalized estimating equations analyses show that household income was not significant at all threshold levels in the households without elderly. On the other hand, in the households with the elderly, the 2nd (odds ratio [OR], 1.33-2.05) and 3rd quintile groups (OR, 1.25-2.55) were more likely to have catastrophic health expenditure compared to the 1st quintile of household income group. Conclusion: As the amount of health expenditures relative to the ability to pay is increasing in households with the elderly, the application of an intervention followed by consistent monitoring is needed. This study found that there were differences in influencing factors according to the presence of the elderly in the households. In particular, in households with the elderly, interesting results have been drawn regarding the occurrence of catastrophic health expenditure in the near-poor, so additional research is required.

• The Korean Journal of Pain
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• v.36 no.2
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• pp.184-194
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• 2023

Background: Degenerative lumbar spondylolisthesis (DLS) is frequently associated with lumbar spinal stenosis (LSS) and conservative treatments such as epidural steroid injection do not have long-term benefits in LSS patients with DLS. This study evaluated the effectiveness of percutaneous epidural neuroplasty using a balloon catheter in patients with LSS and DLS. Methods: Patients' sex, age, body mass index, diabetes, hypertension, stenosis grading, pain duration, location, pain intensity, and medications were retrieved from electronic medical records. At 1, 3, and 6 months following the procedure, data on pain severity, medication usage, and physical functional status were analyzed. A generalized estimating equations model was used at the six-month follow-up. Patients were divided into those with DLS (the spondylolisthesis group) and those without DLS (the no spondylolisthesis group) to evaluate whether the effects of percutaneous epidural neuroplasty using a balloon catheter were different. Results: A total of 826 patients were included (spondylolisthesis: 433 patients, 52.4%; no spondylolisthesis: 393 patients, 47.6%). Age, body mass index, hypertension, pain location, and stenosis grading were statistically different between the two groups. The generalized estimating equations analyses with unadjusted and adjusted estimation revealed a significant improvement in the estimated mean numerical rating scale of pain intensities compared to that at baseline in both groups (P < 0.001). Any adverse events that occurred were minor and temporary. Conclusions: Percutaneous epidural neuroplasty using a balloon catheter may be an alternative treatment option for patients with chronic LSS, regardless of accompanying DLS, who have had failed conservative management.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.32-39
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• 2023

Using the logarithmic methods and the generalized method of moments (GMM), this study developed carbon storage equations for maple trees (Acer palmatum Thunb.) planted in an urban settlement area. A total of 20 maple trees of various ages and diameters were destructively harvested to determine their dry weight and carbon concentration by component. The allometric equations with DBH and DBH²×H as independent variables were developed to estimate the carbon storage for each tree component. The carbon concentration of tree components was the highest in stem wood (49.8%) and lowest in stem bark (46.5%). Allometric equations to estimate the carbon storage of tree components (stem, root, aboveground, and total) showed a similar coefficient of determinations (R²) between the allometric equations of the logarithmic method (0.7494-0.9036) and the GMM (0.7085-0.8847). However, the R² values of the leaves and branches were in the range of 0.3027 to 0.6380, lower than those of the R² of the other tree components. These results indicate that the carbon storage of maple trees growing in urban settlement areas can be efficiently predicted from the equations of GMM methods in the case of a small sample size or the heteroscedasticity of logarithmic equations.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.751-759
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• 2011

The method of generalized estimating equations(GEE) is widely used in the analysis of a correlated dataset that consists of repeatedly observed responses within subjects. The GEE uses a quasi-likelihood equations to find the parameter estimates without assuming a specific distribution for the correlated responses. In this paper we study the importance of specifying the working correlation structure appropriately in fitting GEE for correlated counts data. We investigate the empirical coverages of confidence intervals for the regression coefficients according to four kinds of working correlations where one structure should be specified by the users. The confidence intervals are computed based on the asymptotic normality and the sandwich variance estimator.