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Design, implementation, and analysis considerations for cluster-randomized trials in infection control and hospital epidemiology: A systematic review

Published online by Cambridge University Press:  02 May 2019

Lyndsay M. O’Hara ,
Natalia Blanco Open the ORCID record for Natalia Blanco [Opens in a new window] ,
Surbhi Leekha ,
Kristen A. Stafford ,
Gerard P. Slobogean ,
Emilie Ludeman ,
Anthony D. Harris  and
for the CDC Prevention Epicenters Program
Lyndsay M. O’Hara
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Natalia Blanco
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Surbhi Leekha
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Kristen A. Stafford
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Gerard P. Slobogean
Affiliation:
Department of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland
Emilie Ludeman
Affiliation:
University of Maryland Health Sciences and Human Services Library, Baltimore, Maryland
Anthony D. Harris*
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
for the CDC Prevention Epicenters Program
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland Department of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland University of Maryland Health Sciences and Human Services Library, Baltimore, Maryland
*
Author for correspondence: Anthony D. Harris, Email: aharris@som.umaryland.edu
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Abstract

Background:

In cluster-randomized trials (CRT), groups rather than individuals are randomized to interventions. The aim of this study was to present critical design, implementation, and analysis issues to consider when planning a CRT in the healthcare setting and to synthesize characteristics of published CRT in the field of healthcare epidemiology.

Methods:

A systematic review was conducted to identify CRT with infection control outcomes.

Results:

We identified the following 7 epidemiological principles: (1) identify design type and justify the use of CRT; (2) account for clustering when estimating sample size and report intraclass correlation coefficient (ICC)/coefficient of variation (CV); (3) obtain consent; (4) define level of inference; (5) consider matching and/or stratification; (6) minimize bias and/or contamination; and (7) account for clustering in the analysis. Among 44 included studies, the most common design was CRT with crossover (n = 15, 34%), followed by parallel CRT (n = 11, 25%) and stratified CRT (n = 7, 16%). Moreover, 22 studies (50%) offered justification for their use of CRT, and 20 studies (45%) demonstrated that they accounted for clustering at the design phase. Only 15 studies (34%) reported the ICC, CV, or design effect. Also, 15 studies (34%) obtained waivers of consent, and 7 (16%) sought consent at the cluster level. Only 17 studies (39%) matched or stratified at randomization, and 10 studies (23%) did not report efforts to mitigate bias and/or contamination. Finally, 29 studies (88%) accounted for clustering in their analyses.

Conclusions:

We must continue to improve the design and reporting of CRT to better evaluate the effectiveness of infection control interventions in the healthcare setting.

Type
Review
Information
Infection Control & Hospital Epidemiology , Volume 40 , Issue 6 , June 2019 , pp. 686 - 692
Copyright
© 2019 by The Society for Healthcare Epidemiology of America. All rights reserved. 

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Appendix II

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