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.
Keywords
Sample size calculation - controlled clinical trials - generalized estimating equations
- SAS software
