TY - DATA T1 - Dataset for: A Comparison of Bias-Corrected Empirical Covariance Estimators with Generalized Estimating Equations in Small-Sample Longitudinal Study Settings PY - 2018/08/14 AU - Whitney Ford AU - Philip Michael Westgate UR - https://wiley.figshare.com/articles/dataset/Dataset_for_A_Comparison_of_Bias-Corrected_Empirical_Covariance_Estimators_with_Generalized_Estimating_Equations_in_Small-Sample_Longitudinal_Study_Settings/6741908 DO - 10.6084/m9.figshare.6741908.v1 L4 - https://ndownloader.figshare.com/files/12299630 L4 - https://ndownloader.figshare.com/files/12299666 L4 - https://ndownloader.figshare.com/files/12299669 L4 - https://ndownloader.figshare.com/files/12299672 L4 - https://ndownloader.figshare.com/files/12299675 L4 - https://ndownloader.figshare.com/files/12299678 L4 - https://ndownloader.figshare.com/files/12299681 L4 - https://ndownloader.figshare.com/files/12299684 L4 - https://ndownloader.figshare.com/files/12299687 L4 - https://ndownloader.figshare.com/files/12299690 L4 - https://ndownloader.figshare.com/files/12299663 L4 - https://ndownloader.figshare.com/files/12299660 L4 - https://ndownloader.figshare.com/files/12299633 L4 - https://ndownloader.figshare.com/files/12299636 L4 - https://ndownloader.figshare.com/files/12299639 L4 - https://ndownloader.figshare.com/files/12299642 L4 - https://ndownloader.figshare.com/files/12299645 L4 - https://ndownloader.figshare.com/files/12299648 L4 - https://ndownloader.figshare.com/files/12299651 L4 - https://ndownloader.figshare.com/files/12299654 L4 - https://ndownloader.figshare.com/files/12299657 L4 - https://ndownloader.figshare.com/files/12299693 KW - Degrees of freedom KW - Empirical standard error KW - Generalized estimating equations KW - Test size KW - Statistics KW - Medicine N2 - Data arising from longitudinal studies are commonly analyzed with generalized estimating equations (GEE). Previous literature has shown that liberal inference may result from the use of the empirical sandwich covariance matrix estimator when the number of subjects is small. Therefore, two different approaches have been used to improve the validity of inference. First, many different small-sample corrections to the empirical estimator have been offered in order to reduce bias in resulting standard error estimates. Second, critical values can be obtained from a t-distribution or F-distribution with approximated degrees of freedom. Although limited studies on the comparison of these small-sample corrections and degrees of freedom have been published, there is need for a comprehensive study of currently existing methods in a wider range of scenarios. Therefore, in this manuscript we conduct such a simulation study, finding two methods to attain nominal type I error rates more consistently than other methods in a variety of settings: First, a recently proposed method by Westgate and Burchett (2016, Statistics in Medicine 35, 3733-3744) that specifies both a covariance estimator and degrees of freedom, and second, an average of two popular corrections developed by Mancl and DeRouen (2001, Biometrics 57, 126-134) and Kauermann and Carroll (2001, Journal of the American Statistical Association 96, 1387-1396) with degrees of freedom equaling the number of subjects minus the number of parameters in the marginal model. ER -