The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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The Use of Joint Hierarchical Generalized Linear Models: Application to Multivariate Longitudinal Data

결합 다단계 일반화 선형모형을 이용한 다변량 경시적 자료 분석

  • Lee, Donghwan (Department of Statistics, Ewha Womans University) ;
  • Yoo, Jae Keun (Department of Statistics, Ewha Womans University)
  • 이동환 (이화여자대학교 통계학과) ;
  • 유재근 (이화여자대학교 통계학과)
  • Received : 2015.03.24
  • Accepted : 2015.03.31
  • Published : 2015.04.30
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Abstract

Joint hierarchical generalized linear models proposed by Molas et al. (2013) extend the simple longitudinal model into multiple models fitted jointly. It can easily handle the correlation of multivariate longitudinal data. In this paper, we apply this method to analyze KoGES cohort dataset. Fixed unknown parameters, random effects and variance components are estimated based on a standard framework of h-likelihood theory. Furthermore, based on the conditional Akaike information criterion the correlated covariance structure of random-effect model is selected rather than an independent structure.

경시적 자료는 각 환자마다 시간에 따라 반복 측정되는 코호트 연구 등에서 많이 쓰인다. 본 연구는 반응변수 간 상관성을 고려할 수 있는 결합 다단계 일반화 선형모형을 이용하여, 다변량 경시적 자료 분석을 수행하였다. 한국 유전체 역학 연구에서 실시한 코호트 자료를 적합하고 결과를 해석한다. 조건부 아카이케 정보 기준을 이용하여 모형 선택을 하고, 변량효과들의 추정치들을 설명한다.

Keywords

References

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