Journal
STATISTICS IN MEDICINE
Volume 35, Issue 10, Pages 1706-1721Publisher
WILEY
DOI: 10.1002/sim.6817
Keywords
generalized estimating equations; longitudinal data; variance estimator; small sample size; Type I error; hypothesis testing
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Funding
- Penn State Clinical and Translational Science Institute (CTSI)
- National Center for Research Resources
- National Center for Advancing Translational Sciences, National Institutes of Health (NIH) [5 UL1 RR0330184-04, 5 KL2 TR 126-4]
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Generalized estimating equations (GEE) is a general statistical method to fit marginal models for longitudinal data in biomedical studies. The variance-covariance matrix of the regression parameter coefficients is usually estimated by a robust sandwich variance estimator, which does not perform satisfactorily when the sample size is small. To reduce the downward bias and improve the efficiency, several modified variance estimators have been proposed for bias-correction or efficiency improvement. In this paper, we provide a comprehensive review on recent developments of modified variance estimators and compare their small-sample performance theoretically and numerically through simulation and real data examples. In particular, Wald tests and t-tests based on different variance estimators are used for hypothesis testing, and the guideline on appropriate sample sizes for each estimator is provided for preserving type I error in general cases based on numerical results. Moreover, we develop a user-friendly R package geesmv incorporating all of these variance estimators for public usage in practice. Copyright (C) 2015 John Wiley & Sons, Ltd.
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