14 citations · 23 across the 5 of their papers we have counts for
5 papers
%CRTFASTGEEPWR: a SAS macro for power of the generalized estimating equations of multi-period cluster randomized trials with application to stepped wedge designs
Ying Zhang, John S. Preisser, Fan Li +2
Multi-period cluster randomized trials (CRTs) are increasingly used for the evaluation of interventions delivered at the group level. While generalized estimating equations (GEE) a…
Power considerations for generalized estimating equations analyses of four-level cluster randomized trials
Xueqi Wang, Elizabeth L. Turner, John S. Preisser +1
In this article, we develop methods for sample size and power calculations in four-level intervention studies when intervention assignment is carried out at any level, with a parti…
Marginal modeling of cluster-period means and intraclass correlations in stepped wedge designs with binary outcomes
Fan Li, Hengshi Yu, Paul J. Rathouz +2
Stepped wedge cluster randomized trials (SW-CRTs) with binary outcomes are increasingly used in prevention and implementation studies. Marginal models represent a flexible tool for…
GEECORR: A SAS macro for regression models of correlated binary responses and within-cluster correlation using generalized estimating equations
Tracie L. Shing, John S. Preisser, Richard C. Zink
A SAS macro, GEECORR, has been developed for the analysis of correlated binary data based on the Prentice (1988) estimating equations method that extends the Liang and Zeger (1986)…
A prediction criterion for working correlation structure selection in GEE
Gul Inan, Mahbub A. H. M. Latif, John Preisser
Generalized estimating equations (GEE) is one of the most commonly used methods for marginal regression analysis of longitudinal data, especially with discrete outcomes. The GEE me…