Contamination in trials: is cluster randomisation the answer?
Open accessAbstract
Most randomised trials allocate individual participants to different treatments. However, cluster randomised trials in which groups of subjects are allocated to different treatments are becoming increasingly popular.1 Cluster randomisation is often advocated to minimise treatment “contamination” between intervention and control participants. For example, in a trial of dietary change, people in the control group might learn about the experimental diet and adopt it themselves. Contamination of control participants has two related effects. It reduces the point estimate of an intervention's effectiveness and this apparent reduction may lead to a type II error—that is, rejection of an effective intervention as ineffective because the observed effect size was neither statistically nor clinically significant. Although the threat of contamination is an issue in some controlled trials, it may be not be of much practical importance in many. Trialists should use individual randomisation if possible because of the drawbacks of cluster allocation. Cluster trials are associated with problems of recruitment bias and the need for larger samples than would be required in similar, individually randomised trials. In recruitment bias, different sorts of participants are selected into the various arms of the trial, thereby defeating the objective of randomisation, while a larger sample size may increase the cost of a trial, its length, or its complexity. This paper describes the difficulties of cluster trials and argues that the problem of contamination can often be dealt with by individual randomisation. #### Summary points Cluster trials are often used to prevent “contamination” between intervention and control groups Cluster trials are usually very much larger than individually randomised trials and can be susceptible to recruitment bias The problem of contamination can often be overcome by increasing the sample size In terms of total sample size, cluster trials are only more efficient where contamination exceeds 30% Members of clusters cannot be …
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In a comparison between a novel treatment and usual care any bias due to contamination will attenuate the true treatment effect.
“In a comparison between a novel treatment and usual care any bias due to contamination will attenuate the true treatment effect [18,26,35].”
From Cluster randomized trials of individual-level interventions were at high risk of bias (2021) · cites this paper as [35]
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- Authors
- Torgerson, D. J.
- Affiliations
- University of York (GB)
- Corresponding author
- Torgerson, David J
- ORCID
- Torgerson, David J
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2001-02-10
- Volume
- 322
- Issue
- 7282
- Pages
- 355-357
- ISSN
- 0007-1447 (ISSN-L)
- 0959-8138
- 1468-5833
- PMC
- PMC1119583
- OpenAlex
- W2101584773
- Cited by
- 508
- References
- 2
- Citations by year
- 2026: 15 · 2025: 19 · 2024: 29 · 2023: 35 · 2022: 41 · 2021: 36 · 2020: 32 · 2019: 34 · 2018: 37 · 2017: 22 · 2016: 13 · 2015: 19 · 2014: 20 · 2013: 21 · 2012: 12
- FWCI
- 11.67
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.bmj.com/company/legal-information/terms-conditions/legal-information/tdm-licencepolicy (tdm)
- Locations
- BMJ (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion)
- HighWire Press Open Archive (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- recruitment bias, type II error, sample size, trial design, dietary intervention trials
- MeSH terms
- Humans, Research Design, Statistics as Topic, Selection Bias, Randomized Controlled Trials as Topic, Sample Size
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