Summary
Objectives:
The application of independence estimating equations (IEE) for controlled clinical
trials (CCTs) has recently been discussed, and recommendations for its use have been
derived for testing hypotheses. The robust estimator of variance has been shown to
be liberal for small sample sizes. Therefore a series of modifications has been proposed.
In this paper we systematically compare confidence intervals (CIs) proposed in the
literature for situations that are common in CCTs.
Methods:
Using Monte-Carlo simulation studies, we compared the coverage probabilities of CIs
and non-convergence probabilities for the parameters of the mean structure for small
samples using modifications of the variance estimator proposed by Mancl and de Rouen
[7], Morel et al. [8] and Pan [3].
Results:
None of the proposed modifications behave well in each investigated situation. For
parallel group designs with repeated measurements and binary response the method proposed
by Pan maintains the nominal level. We observed non-convergence of the IEE algorithm
in up to 10% of the replicates depending on response probabilities in the treatment
groups. For comparing slopes with continuous responses, the approach of Morel et al.
can be recommended.
Conclusions:
Results of non-convergence probabilities show that IEE should not be used in parallel
group designs with binary endpoints and response probabilities close to 0 or 1. Modifications
of the robust variance estimator should be used for sample sizes up to 100 clusters
for CI estimation.
Keywords
Small sample size - controlled clinical trials - generalized estimating equations
- independence estimating equations
