Choosing appropriate analysis methods for cluster randomised cross-over trials with a binary outcome.
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.
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, U.K.
Katy E
KE
Morgan
School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Andrew B
AB
Forbes
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, U.K.
Ruth H
RH
Keogh
Department of Medicine, London Health Sciences Network and Western University, London, ON, Canada.
Vipul
V
Jairath
Pragmatic Clinical Trials Unit, Queen Mary University of London, London, U.K.
Brennan C
BC
Kahan
D056808:Biostatistics; D016000:Cluster Analysis; D003198:Computer Simulation; D018592:Cross-Over Studies; D017707:Erythrocyte Transfusion; D006471:Gastrointestinal Hemorrhage / Q000097:blood / Q000628:therapy; D006454:Hemoglobins / Q000378:metabolism; D006801:Humans; D015233:Models, Statistical; D016017:Odds Ratio; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D012044:Regression Analysis; D018401:Sample Size; D016896:Treatment Outcome
D006454:Hemoglobins
binary outcomes; cluster randomised; cross-over; intra-cluster correlation coefficient (ICC); randomised trial