Communications in Statistics - Simulation and Computation · 1994 · 402 citations · 7 references
Public HealthBiostatisticsCausal InferenceEconometricsStatistical ModelingCovariate AnalysisGeneralized Linear ModelsLongitudinal Data AnalysisLongitudinal ResearchStructural Equation ModelingTime-dependent CovariatesCorrelationCovariance Matrix EstimationGeneralized Estimating Equations
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.
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