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Trials. 2017 Aug 15;18(1):381. doi: 10.1186/s13063-017-2113-2.

Understanding the cluster randomised crossover design: a graphical illustraton of the components of variation and a sample size tutorial.

Author information

1
School of Public Health and Preventive Medicine, Monash University, The Alfred Centre, Melbourne, VIC, 3004, Australia.
2
Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
3
Australian and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation, Ievers Terrace, Carlton, VIC, 3154, Australia.
4
Department of Intensive Care, The Alfred Hospital, Commercial Road, Melbourne, VIC, 3004, Australia.
5
Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, The Alfred Centre, Melbourne, VIC, 3004, Australia.
6
School of Public Health and Preventive Medicine, Monash University, The Alfred Centre, Melbourne, VIC, 3004, Australia. Andrew.Forbes@monash.edu.

Abstract

BACKGROUND:

In a cluster randomised crossover (CRXO) design, a sequence of interventions is assigned to a group, or 'cluster' of individuals. Each cluster receives each intervention in a separate period of time, forming 'cluster-periods'. Sample size calculations for CRXO trials need to account for both the cluster randomisation and crossover aspects of the design. Formulae are available for the two-period, two-intervention, cross-sectional CRXO design, however implementation of these formulae is known to be suboptimal. The aims of this tutorial are to illustrate the intuition behind the design; and provide guidance on performing sample size calculations.

METHODS:

Graphical illustrations are used to describe the effect of the cluster randomisation and crossover aspects of the design on the correlation between individual responses in a CRXO trial. Sample size calculations for binary and continuous outcomes are illustrated using parameters estimated from the Australia and New Zealand Intensive Care Society - Adult Patient Database (ANZICS-APD) for patient mortality and length(s) of stay (LOS).

RESULTS:

The similarity between individual responses in a CRXO trial can be understood in terms of three components of variation: variation in cluster mean response; variation in the cluster-period mean response; and variation between individual responses within a cluster-period; or equivalently in terms of the correlation between individual responses in the same cluster-period (within-cluster within-period correlation, WPC), and between individual responses in the same cluster, but in different periods (within-cluster between-period correlation, BPC). The BPC lies between zero and the WPC. When the WPC and BPC are equal the precision gained by crossover aspect of the CRXO design equals the precision lost by cluster randomisation. When the BPC is zero there is no advantage in a CRXO over a parallel-group cluster randomised trial. Sample size calculations illustrate that small changes in the specification of the WPC or BPC can increase the required number of clusters.

CONCLUSIONS:

By illustrating how the parameters required for sample size calculations arise from the CRXO design and by providing guidance on both how to choose values for the parameters and perform the sample size calculations, the implementation of the sample size formulae for CRXO trials may improve.

KEYWORDS:

Between-period correlation; Cluster randomised; Components of variability; Crossover; Intracluster correlation; Sample size; Within-period correlation

PMID:
28810895
PMCID:
PMC5557529
DOI:
10.1186/s13063-017-2113-2
[Indexed for MEDLINE]
Free PMC Article

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