• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.367-379
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• 2004

This article deals with the nonparametric analysis of longitudinal data when there exist possible correlations among repeated measurements for a given subject. We consider a quasi-likelihood regression model where a transformation of the regression function through a link function is linear in time-varying coefficients. We investigate the local polynomial approach to estimate the time-varying coefficients, and derive the asymptotic distribution of the estimators in this quasi-likelihood context. A real data set is analyzed as an illustrative example.

• Research in Community and Public Health Nursing
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• v.23 no.2
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• pp.201-211
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• 2012

Purpose: The purpose of this study is to evaluate the effects of a local community based multi-factorial program for high-risk younger and older elderly people. Methods: The quasi-experimental research design (pretest-post test) was employed. Participants were recruited in Seoul and a total of 98 elders completed an 8-week multi-factorial program for preventing frailty. Descriptive statistics, $x^2$-test and GLM were used in the data analysis with SPSS/WIN 15.0. Results: The high-risk elderly people in the younger and older stages showed differences in IADL, TUG and BMI, and after being provided with the multi-factorial program for preventing frailty, some effects were shown on improving the total score of frailty, a physical function, TUG, BMI, depression, subjective feeling of health, and social interaction. Conclusion: The 8-week multi-factorial program for preventing frailty had positive effects on improving physical, emotional and social functions of the high-risk elderly people. It is necessary to evaluate the effects after individual intervention as well as group intervention and to evaluate the effects of the program by setting a control group in the future.