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Choosing appropriate analysis methods for cluster randomised cross‐over trials with a binary outcome
Author(s) -
Morgan Katy E.,
Forbes Andrew B.,
Keogh Ruth H.,
Jairath Vipul,
Kahan Brennan C.
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7137
Subject(s) - statistics , type i and type ii errors , correlation , cluster (spacecraft) , cluster analysis , outcome (game theory) , hierarchical clustering , random effects model , binary number , cluster randomised controlled trial , regression , mathematics , computer science , meta analysis , medicine , randomized controlled trial , geometry , arithmetic , mathematical economics , surgery , programming language
In cluster randomised cross‐over (CRXO) trials, clusters receive multiple treatments in a randomised sequence over time. In such trials, there is usual correlation between patients in the same cluster. In addition, within a cluster, patients in the same period may be more similar to each other than to patients in other periods. We demonstrate that it is necessary to account for these correlations in the analysis to obtain correct Type I error rates. We then use simulation to compare different methods of analysing a binary outcome from a two‐period CRXO design. Our simulations demonstrated that hierarchical models without random effects for period‐within‐cluster, which do not account for any extra within‐period correlation, performed poorly with greatly inflated Type I errors in many scenarios. In scenarios where extra within‐period correlation was present, a hierarchical model with random effects for cluster and period‐within‐cluster only had correct Type I errors when there were large numbers of clusters; with small numbers of clusters, the error rate was inflated. We also found that generalised estimating equations did not give correct error rates in any scenarios considered. An unweighted cluster‐level summary regression performed best overall, maintaining an error rate close to 5% for all scenarios, although it lost power when extra within‐period correlation was present, especially for small numbers of clusters. Results from our simulation study show that it is important to model both levels of clustering in CRXO trials, and that any extra within‐period correlation should be accounted for. Copyright © 2016 John Wiley & Sons, Ltd.