Skip to main content Accessibility help

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

  • Lyndsay M. O’Hara (a1), Natalia Blanco (a1), Surbhi Leekha (a1), Kristen A. Stafford (a1), Gerard P. Slobogean (a2), Emilie Ludeman (a3), Anthony D. Harris (a1) and for the CDC Prevention Epicenters Program (a1) (a2) (a3)...



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.


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


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.


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


Corresponding author

Author for correspondence: Anthony D. Harris, Email:


Hide All
1. Donner, A, Klar, N. Design and Analysis of Cluster Randomization Trials in Health Research. London: Arnold, 2000.
2. Moberg, J, Kramer, M. A brief history of the cluster randomized trial design. JLL Bulletin: Commentaries on the history of treatment evaluation. James Lind Library website. Published 2015. Accessed March 8, 2019.
3. Hayes, RJ, Alexander, ND, Bennett, S, Cousens, SN. Design and analysis issues in cluster-randomized trials of interventions against infectious diseases. Statist Method Med Res 2000;9:95116.
4. Wolkewitz, M, Barnett, AG, Martinez, MP, Frank, U, Schumacher, M, IMPLEMENT Study Group. Interventions to control nosocomial infections: study designs and statistical issues. J Hosp Infect 2014;86:7782.
5. Simpson, JM, Klar, N, Donnor, A. Accounting for cluster randomization: a review of primary prevention trials, 1990 through 1993. Am J Pub Health 1995;85:13781383.
6. Donner, A, Klar, N. Pitfalls of and controversies in cluster randomization trials. Am J Pub Health 2004;94:416422.
7. Arnup, SJ, McKenzie, JE, Hemming, K, Pilcher, D, Forbes, AB. Understanding the cluster randomized crossover design: a graphical illustration of the components of variation and a sample size tutorial. Trials 2007;18:381.
8. Rothman, KJ, Greenland, S, Lash, TL. Modern Epidemiology, Third Edition. Philadelphia: Lippincott, Williams & Wilkins; 2008.
9. Donner, A, Birkett, N, Buck, C. Randomization by cluster sample size requirements and analysis. Am J Epidemiol 1981;114:906914.
10. Rutterford, C, Copas, A, Eldridge, S. Methods for sample size determination in cluster randomized trials. Int J Epidemiol 2015;44:10511067.
11. Sim, J, Dawson, A. Informed consent and cluster-randomized trials. Am J Pub Health 2012;102:480485.
12. Edwards, SJL, Braunholz, DA. Lilford, RJ, et al. Ethical issues in the design and conduct of cluster randomized controlled trials. BMJ 1999;318:14071409.
13. Martin, DC, Diehr, P, Perrin, EB, Koepsell, TD. The effect of matching on the power of randomized community intervention studies. Statist Med 1993;12:329338.
14. Puffer, S, Torgerson, D, Watson, J. Evidence for risk of bias in cluster randomised trials: review of recent trials published in three general medical journals. BMJ 2003;327:785789.
15. Fitzmaurice, GM, Laird, NM, Ware, JH. Applied Longitudinal Analysis. New York: John Wiley & Sons; 2012.
16. Siebenhofer, A, Paulitsch, MA, Pregartner, G, Berghold, A, Jeitler, K, Muth, C, Engler, J. Cluster-randomized controlled trials evaluating complex interventions in general practices are mostly ineffective: a systematic review. J Clin Epidemiol 2018;94:8596.
17. Campbell, MK, Piaggio, G, Elbourne, DR, Altman DG; for the CONSORT Group. Consort 2010 statement: extension to cluster randomised trials. BMJ 2012;345:e5661.
18. Hemming, K, Eldridge, S, Forbes, G, Weijer, C, Taljaard, M. How to design efficient cluster randomised trials. BMJ 2017;358:j3064.
19. Reich, NG, Myers, JA, Obeng, D, Milstone, AM, Perl, TM. Empirical power and sample size calculations for cluster-randomized and cluster-randomized crossover studies. PLoS One 2012;7(4):e35564.
20. Caille, A, Kerry, S, Tavernier, E, Leyrat, C, Eldridge, S, Giraudeau, B. Timeline cluster: a graphical tool to identify risk of bias in cluster randomised trials. BMJ 2016;354:i4291.
Type Description Title
Supplementary materials

O’Hara et al. supplementary material
Appendix I

 Word (22 KB)
22 KB
Supplementary materials

O’Hara et al. supplementary material
Appendix II

 PDF (381 KB)
381 KB


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed