Back to Search Start Over

Recommendations for choosing an analysis method that controls Type I error for unbalanced cluster sample designs with Gaussian outcomes

Authors :
Deborah H. Glueck
Sarah M. Kreidler
Diane Catellier
David M. Murray
Keith E. Muller
Jacqueline L. Johnson
Source :
Statistics in Medicine. 34:3531-3545
Publication Year :
2015
Publisher :
Wiley, 2015.

Abstract

We used theoretical and simulation-based approaches to study Type I error rates for one-stage and two-stage analytic methods for cluster-randomized designs. The one-stage approach uses the observed data as outcomes and accounts for within-cluster correlation using a general linear mixed model. The two-stage model uses the cluster specific means as the outcomes in a general linear univariate model. We demonstrate analytically that both one-stage and two-stage models achieve exact Type I error rates when cluster sizes are equal. With unbalanced data, an exact size α test does not exist, and Type I error inflation may occur. Via simulation, we compare the Type I error rates for four one-stage and six two-stage hypothesis testing approaches for unbalanced data. With unbalanced data, the two-stage model, weighted by the inverse of the estimated theoretical variance of the cluster means, and with variance constrained to be positive, provided the best Type I error control for studies having at least six clusters per arm. The one-stage model with Kenward-Roger degrees of freedom and unconstrained variance performed well for studies having at least 14 clusters per arm. The popular analytic method of using a one-stage model with denominator degrees of freedom appropriate for balanced data performed poorly for small sample sizes and low intracluster correlation. Because small sample sizes and low intracluster correlation are common features of cluster-randomized trials, the Kenward-Roger method is the preferred one-stage approach.

Details

ISSN :
10970258 and 02776715
Volume :
34
Database :
OpenAIRE
Journal :
Statistics in Medicine
Accession number :
edsair.doi.dedup.....70d635f0d5e121eec68a82d1ef905482
Full Text :
https://doi.org/10.1002/sim.6565