Browse > Article
http://dx.doi.org/10.7465/jkdi.2013.24.4.877

Generalized methods of moments in marginal models for longitudinal data with time-dependent covariates  

Cho, Gyo-Young (Department of Statistics, Kyungpook National University)
Dashnyam, Oyunchimeg (Department of Statistics, Kyungpook National University)
Publication Information
Journal of the Korean Data and Information Science Society / v.24, no.4, 2013 , pp. 877-883 More about this Journal
Abstract
The quadratic inference functions (QIF) method proposed by Qu et al. (2000) and the generalized method of moments (GMM) for marginal regression analysis of longitudinal data with time-dependent covariates proposed by Lai and Small (2007) both are the methods based on generalized method of moment (GMM) introduced by Hansen (1982) and both use generalized estimating equations (GEE). Lai and Small (2007) divided time-dependent covariates into three types such as: Type I, Type II and Type III. In this paper, we compared these methods in the case of Type II and Type III in which full covariates conditional mean assumption (FCCM) is violated and interested in whether they can improve the results of GEE with independence working correlation. We show that in the marginal regression model with Type II time-dependent covariates, GMM Type II of Lai and Small (2007) provides more ecient result than QIF and for the Type III time-dependent covariates, QIF with independence working correlation and GMM Type III methods provide the same results. Our simulation study showed the same results.
Keywords
FCCM assumption; GEE; GMM; longitudinal data; marginal model; QIF; time-dependent covariate;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Cho, G. Y. and Dashnyam, O. (2013). Quadratic inference functions in marginal models for longitudinal data with time-varying stochastic covariates. Journal of the Korean Data & Information Science Society, 24, 651-658.   DOI   ScienceOn
2 Diggle, P. J. Heagerty, P. Liang, K-Y. and Zeger, S. L. (2002). Analysis of longitudinal data, Oxford University Press, New York.
3 Fitzmaurice, G. M. (1995). A caveat concerning independence estimating equations with multivariate binary data. Biometrics, 51, 309-317.   DOI   ScienceOn
4 Fitzmaurice, G. M, Liard, N. M. and Ware, J. H. (2004). Applied longitudinal analysis. Wiley, New York.
5 Hansen, L. (1982). Large sample properties of generalized methods of moments estimators. Econometrica, 50, 1029-1055.   DOI   ScienceOn
6 Lai, Tz. L. and Small, D. (2007). Marginal regression analysis of longitudinal data with time-dependent covariates: A generalized method of moments approach. Journal of the Royal Statistical Society B, 69, 79-99.
7 Pan, W., Thomas, A. L. and John, E. C. (2000). Note on marginal linear regression with correlated response data. The American Statistician, 54, 191-195.
8 Pepe, M. S. and Anderson, G. L. (1994). A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data. Communications in Statistics-Simulation, 23, 939-951.   DOI   ScienceOn
9 Qu, A., Lindsay, B. G. and Li, B. (2000). Improving generalized estimating equations using quadratic inference functions. Biometrika, 87, 823-836.   DOI   ScienceOn
10 Qu, A. and Lindsay, B. G. (2003). Building adaptive estimating equation when inverse of covariance estimation is difficult. Journal of the Royal Statistical Society B, 65, 127-142.   DOI   ScienceOn
11 Song, P. X.-K., Jiang, Z., Park, E. J. and Qu, A. (2009) Quadratic inference functions in marginal models for longitudinal data. Statistical Medicine, 28, 3683-3696.   DOI   ScienceOn