A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data
- 1 January 1994
- journal article
- research article
- Published by Taylor & Francis in Communications in Statistics - Simulation and Computation
- Vol. 23 (4), 939-951
- https://doi.org/10.1080/03610919408813210
Abstract
Inference for cross-sectional models using longitudinal data, can be accomplished with generalized estimating equations (Zeger and Liang, 1992). We show that either a diagonal working covariance matrix should be used or a key assumption should be verified. The assumption is non-trivial when covariates vary over time. The validity of this assumption is explored for some broad classes of correlation structures. Similar considerations are shown to be relevant for the more general problem of correlated response data and marginal regression analysis with individual level covariates.Keywords
This publication has 11 references indexed in Scilit:
- An overview of methods for the analysis of longitudinal dataStatistics in Medicine, 1992
- Some Covariance Models for Longitudinal Count Data with OverdispersionBiometrics, 1990
- Generalized Linear ModelsPublished by Springer Nature ,1989
- Correlated Binary Regression with Covariates Specific to Each Binary ObservationBiometrics, 1988
- Models for Longitudinal Data: A Generalized Estimating Equation ApproachBiometrics, 1988
- A regression model for time series of countsBiometrika, 1988
- Longitudinal data analysis using generalized linear modelsBiometrika, 1986
- Increased risk of respiratory disease and diarrhea in children with preexisting mild vitamin A deficiencyThe American Journal of Clinical Nutrition, 1984