Journal
STATISTICS IN MEDICINE
Volume 30, Issue 26, Pages 3117-3124Publisher
WILEY-BLACKWELL
DOI: 10.1002/sim.4300
Keywords
pseudolikelihood; correlation; covariance models; estimating functions; longitudinal data; repeated measures
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Funding
- National Institutes of Health [HL40619]
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We investigate methods for data-based selection of working covariance models in the analysis of correlated data with generalized estimating equations. We study two selection criteria: Gaussian pseudolikelihood and a geodesic distance based on discrepancy between model-sensitive and model-robust regression parameter covariance estimators. The Gaussian pseudolikelihood is found in simulation to be reasonably sensitive for several response distributions and noncanonical mean-variance relations for longitudinal data. Application is also made to a clinical dataset. Assessment of adequacy of both correlation and variance models for longitudinal data should be routine in applications, and we describe open-source software supporting this practice. Copyright (C) 2011 John Wiley & Sons, Ltd.
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