Journal
STATISTICS IN MEDICINE
Volume 35, Issue 21, Pages 3733-3744Publisher
WILEY-BLACKWELL
DOI: 10.1002/sim.6967
Keywords
correlation selection; degrees of freedom; efficiency; empirical covariance matrix
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Funding
- National Institute on Aging [R01 AG019241]
- National Center for Research Resources
- National Center for Advancing Translational Sciences, National Institutes of Health [UL1TR000117]
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Generalized estimating equations (GEE) are often used for the marginal analysis of longitudinal data. Although much work has been performed to improve the validity of GEE for the analysis of data arising from small-sample studies, little attention has been given to power in such settings. Therefore, we propose a valid GEE approach to improve power in small-sample longitudinal study settings in which the temporal spacing of outcomes is the same for each subject. Specifically, we use a modified empirical sandwich covariance matrix estimator within correlation structure selection criteria and test statistics. Use of this estimator can improve the accuracy of selection criteria and increase the degrees of freedom to be used for inference. The resulting impacts on power are demonstrated via a simulation study and application example. Copyright (c) 2016 John Wiley & Sons, Ltd.
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