Generalized Estimating Equations in Controlled Clinical Trials: Hypotheses Testing

2004 ◽  
Vol 46 (2) ◽  
pp. 214-232 ◽  
Author(s):  
G. Dahmen ◽  
A. Ziegler
Keyword(s):  
Clinical Trials ◽  
Generalized Estimating Equations ◽  
Estimating Equations ◽  
Hypotheses Testing ◽  
Controlled Clinical Trials ◽  
Generalized Estimating

Sample Size Calculations for Controlled Clinical Trials Using Generalized Estimating Equations (GEE)

2004 ◽  
Vol 43 (05) ◽  
pp. 451-456 ◽  
Author(s):  
J. Rochon ◽  
I. R. König ◽  
A. Ziegler ◽  
G. Dahmen
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Generalized Estimating Equations ◽  
Sample Size Calculation ◽  
Estimating Equations ◽  
Correlated Response ◽  
Power Calculation ◽  
Controlled Clinical Trials ◽  
Primary Analysis ◽  
Generalized Estimating

Summary Objectives: Clinical trials with correlated response data based on generalized estimating equations (GEE) have become increasingly popular as they require smaller samples than classical methods that ignore the clustered nature of the data. We have recently derived the recommendation to use the independence estimating equations (IEE) as primary analysis in most controlled clinical trials instead of GEE with estimated correlations [1]. Although several approaches for sample size and power calculation have been proposed, we have shown that most of these procedures are very specific and not as general as required for designing clinical trials. Methods: We extended the previously developed SAS macro GEESIZE to overcome this restriction. Specifically, we have added the option of an independence working correlation matrix required for the IEE. Additionally, we have reformulated the hypotheses to allow for coding that includes an intercept term instead of the previously used analysis of variance coding. Results: To demonstrate the validity of GEESIZE we investigate the calculated sample sizes for specific models where closed formulae are available. For illustration, we utilize GEESIZE for planning a new trial on the treatment of hypertension and thereby exemplify its flexibility. Conclusions: We show that our freely available macro is a very general and useful tool for sample size calculation purposes in clinical trials with correlated data.

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