Quadratic inference functions in marginal models for longitudinal data with time-varying stochastic covariates.

Хэвлэлийн нэр: Journal of the Korean Data & Information Science Society

Зохиогч:  Д.Оюунчимэг

Хамтран зохиогч:

Хэвлүүлсэн огноо: 2013-05-31

Хуудас дугаар: 651-658

Өгүүллийн хураангуй:

For the marginal model and generalized estimating equations (GEE) method there is important full covariates conditional mean (FCCM) assumption which is pointed out by Pepe and Anderson (1994). With longitudinal data with time-varying stochastic covariates, this assumption may not necessarily hold. If this assumption is violated, the biased estimates of regression coefficients may result. But if a diagonal working correlation matrix is used, irrespective of whether the assumption is violated, the resulting estimates are (nearly) unbiased. (Pan et al., 2000)

The Quadratic inference functions (QIF) method proposed by Qu et al. (2000) is the method based Generalized Method of Moment (GMM) using GEE. QIF yields a substantial improvement in efficiency for the estimator of β when the working correlation is misspecified, and equal efficiency to the GEE when the working correlation is correct. (Qu et al., 2000).

In this paper, we interest in whether QIF can improve the properties of GEE estimators in the case of FCCM is violated. We show that the QIF may be unbiased as GEE and QIF with independence working correlation matrix will be the “safe” choice as GEE in this case. Our simulation study verify the result.