Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Upgraded quadratic inference functions for longitudinal data with type II time-dependent covariates

  • Cho, Gyo-Young (Department of Statistics, Kyungpook National University) ;
  • Dashnyam, Oyunchimeg (Department of Mathematics and Statistics, Mongolian State University of Education)
  • Received : 2013.10.15
  • Accepted : 2013.12.12
  • Published : 2014.01.31
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Abstract

Qu et. al. (2000) proposed the quadratic inference functions (QIF) method to marginal model analysis of longitudinal data to improve the generalized estimating equations (GEE). It yields a substantial improvement in efficiency for the estimators of regression parameters when the working correlation is misspecified. But for the longitudinal data with time-dependent covariates, when the implicit full covariates conditional mean (FCCM) assumption is violated, the QIF can not provide more consistent and efficient estimator than GEE (Cho and Dashnyam, 2013). Lai and Small (2007) divided time-dependent covariates into three types and proposed generalized method of moment (GMM) for longitudinal data with time-dependent covariates. They showed that their GMM type II and GMM moment selection methods can be more ecient than GEE with independence working correlation (GEE-ind) in the case of type II time-dependent covariates. We develop upgraded QIF method for type II time-dependent covariates. We show that this upgraded QIF method can provide substantial gains in efficiency over QIF and GEE-ind in the case of type II time-dependent covariates.

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References

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Cited by

  1. Improved methods for the marginal analysis of longitudinal data in the presence of time-dependent covariates vol.36, pp.16, 2017, https://doi.org/10.1002/sim.7307