@article{ef7d0be5cc0e468b9c85968e18c94d1d,
title = "Covariance estimators for generalized estimating equations (GEE) in longitudinal analysis with small samples",
abstract = "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.",
author = "Ming Wang and Lan Kong and Zheng Li and Lijun Zhang",
note = "Funding Information: The author was supported by a pilot grant and KL2 career grant from the Penn State Clinical and Translational Science Institute (CTSI). The project was supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health (NIH), through Grant 5 UL1 RR0330184-04 and Grant 5 KL2 TR 126-4. The content is solely the responsibility of the author and does not represent the views of the NIH. The authors thank Nicholas Sterling for correcting grammar errors during the revision of the manuscript. Funding Information: The author was supported by a pilot grant and KL2 career grant from the Penn State Clinical and Translational Science Institute (CTSI). The project was supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health (NIH), through Grant 5 UL1 RR0330184-04 and Grant 5 KL2 TR 126-4. The content is solely the responsibility of the author and does not represent the views of the NIH. The authors thank Nicholas Sterling for correcting grammar errors during the revision of the manuscript. Publisher Copyright: {\textcopyright} 2016 John Wiley & Sons, Ltd.",
year = "2016",
month = may,
day = "10",
doi = "10.1002/sim.6817",
language = "English (US)",
volume = "35",
pages = "1706--1721",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "10",
}