Random-Effect Prediction Approach for Clustered Binominal Data

• 발행기관: 한국자료분석학회
• 저자명: Geon-Ho Cho
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.12 No.1, 17~27쪽, 전체 11쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2010.02.28

영문 초록

Clustered data may have not only a correlation within-cluster but also a heterogeneity between-cluster. Thus, various random-effect models including generalized linear mixed models(GLMMs) have been widely used. However, the analysis of heterogeneity has been less studied. For this the prediction of random effects is very useful. In this paper we show a general framework how to model the clustered binominal data, and to estimate parameters and to predict random effects via SAS PROC NLMIXED. Thus, we illustrate the analyses with a real data set obtained from different 8 clinics for investigating a treatment effect. We demonstrate using this data set how an appropriate model can be selected via well-known model-selection criteria. We also present a plot on predicted random effects for investigating practically a potential heterogeneity over clinics.

목차

1. Introduction

2. Example: Clinic Binominal Data

3. Models and Prediction

4. Data Analysis

5. Conclusion

References

키워드

Generalized linear mixed models Marginal likelihood Prediction of random effects Random treatment-by-clinic interaction Clinic effect