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시간-종속적 공변량이 포함된 이분형반복측정자료의 GEE를 이용한 분석에서 결측체계에 따른 회귀계수 추정방법 비교

Other Titles
 Comparison of GEE Estimation Methods for Repeated Binary Data with Time-Varying Covariates on Different Missing Mechanisms 
Authors
 박보람 ; 정인경 
Citation
 응용통계연구, Vol.26(5) : 697~716, 2013 
Journal Title
 응용통계연구 
ISSN
 1225-066X 
Issue Date
2013
Abstract
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.
URI
http://ir.ymlib.yonsei.ac.kr/handle/22282913/88338
DOI
10.5351/KJAS.2013.26.5.697
Appears in Collections:
1. 연구논문 > 1. College of Medicine > Dept. of Biostatistics
Yonsei Authors
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