Browse > Article
http://dx.doi.org/10.5351/KJAS.2014.27.1.089

A Study of Effect on the Smoking Status using Multilevel Logistic Model  

Lee, Ji Hye (Department of Information and Statistics, Chungbuk National University)
Heo, Tae-Young (Department of Information and Statistics, Chungbuk National University)
Publication Information
The Korean Journal of Applied Statistics / v.27, no.1, 2014 , pp. 89-102 More about this Journal
Abstract
In this study, we analyze the effect on the smoking status in the Seoul Metropolitan area using a multilevel logistic model with Community Health Survey data from the Korea Centers for Disease Control and Prevention. Intraclass correlation coefficient (ICC), profiling analysis and two types of predicted value were used to determine the appropriate multilevel analysis level. Sensitivity, specificity, percentage of correctly classified observations (PCC) and ROC curve evaluated model performance. We showed the applicability for multilevel analysis allowed for the possibility that different factors contribute to within group and between group variability using survey data.
Keywords
Multilevel analysis; multilevel logistic regression model; hierarchical data; community health survey data;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Kim, C.-H. (1999). Factors related to smoking among male smokers in Seoul, Inje Medical Journal, 20, 699-704.
2 Kim, D.-S., Hwang, J.-H. and Hwang, J.-I. (2012). A multi-level analysis of injection requests and associated patient characteristics in the Korean acute-care outpatient setting, Korean College of Clinical Pharmacy, 22, 13-20.
3 Lee, H., Lee, S. and Lee, E. (2012). Characteristics and factors related to problem drinking of the elderly in Korea, Journal of The Korea Society of Health Informatics and Statistics, 37, 34-75.
4 Li, J., Alterman, T. and Deddens, J. A. (2006). Analysis of Large Hierarchical Data with Multilevel Logistic Modeling Using PROC GLIMMIX, SAS Users Group International 31.
5 Park, W.-W. and Ko, S. (2005). Procedures and methods of multilevel analysis: With a focus on WABA, Seoul Journal of Business, 39, 59-90.
6 Schabenberger, O. (2005). Introducing the GLIMMIX Procedure for Generalized Linear Mixed Models, SUGI 30.
7 Seoul Metropolitan Government (2013). http://health.seoul.go.kr/archives/825
8 WHO (2013). http://www.who.int/mediacentre/factsheets/fs339/en/index.html
9 Dai, J., Li, Z. and Rocke, D. (2006). Hierarchical Logistic Regression Modeling with SAS GLIMMIX, Western Users of SAS Software 2006.
10 Flom, P. L., McMahon, J. M. and Pouget, E. R. (2006). Using PROC NLMIXED and PROC GLIMMIX to Analyze Dyadic Data with Binary Outcomes, Northeast SAS Users Group 2006.
11 Guo, G. and Zhao, H. (2000). Multilevel modeling for binary data, Annual Review of Sociology, 26, 441-462.   DOI   ScienceOn
12 Jung, J.-H., Kwon, S.-M., Kim, K.-H., Lee, S.-K. and Kim, D.-S. (2010). Impact of health insurance type on the quality of hemodialysis services: A multilevel analysis, Journal of Preventive Medicine and Public Health, 43, 245-256.   DOI   ScienceOn
13 Khan, H. and Shaw, J. (2011). Multilevel logistic regression analysis applied to binary contraceptive prevalence data, Journal of Data Science, 9, 93-110.