Preventing Bias in Cluster Randomised Trials

Giraudeau, Bruno; Ravaud, Philippe

doi:10.1371/journal.pmed.1000065

Cited by 150Statistical Methods in Clinical TrialsMeta-analysis and systematic reviewsAdvanced Causal Inference Techniques

Preventing Bias in Cluster Randomised Trials

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Giraudeau, Bruno, Ravaud, Philippe·2009·PLoS Medicine

DOI 10.1371/journal.pmed.1000065PMC PMC2668175

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Abstract

Bruno Giraudeau and Philippe Ravaud discuss the difficulties in preventing selection bias and applying intention-to-treat analysis in cluster randomized trials, and propose some solutions.

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More eligibility criteria at the participant level increases the likelihood of differential inclusion.
“We extracted the number of eligibility criteria at the participant level as more eligibility criteria increases the likelihood of differential inclusion [14].”
From Cluster randomized trials of individual-level interventions were at high risk of bias (2021) · cites this paper as [14]
The sections on comparability of groups and active recruitment describe differential recruitment and selection bias in cluster randomised trials. They do not say that having more participant-level eligibility criteria increases the likelihood of differential inclusion. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Knowledge of treatment condition at the time of patient recruitment is known to be a risk factor for differential identification and recruitment of participants across treatment arms, unless recruitment and identification are conducted by someone blind to the treatment allocation or the inclusion criteria are broad.
“Knowledge of treatment condition at the time of patient recruitment is known to be a risk factor for differential identification and recruitment of participants across treatment arms [1,14,16,37], unless recruitment and identification are conducted by someone blind to the treatment allocation or the inclusion criteria are broad [2,11,14].”
From Cluster randomized trials of individual-level interventions were at high risk of bias (2021) · cites this paper as [14]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured

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Authors: Giraudeau, B., & Ravaud, P.
Affiliations: Université de Tours (FR)
Inserm (FR)
Corresponding author: Giraudeau, Bruno
ORCID: Giraudeau, Bruno
Ravaud, Philippe
Venue: PLoS Medicine
Publisher: Public Library of Science (PLoS)
Published: 2009-05-05
Volume: 6
Issue: 5
Pages: e1000065
ISSN: 1549-1277 (ISSN-L)
1549-1676
DOI: 10.1371/journal.pmed.1000065
PMC: PMC2668175
OpenAlex: W2065087661
Cited by: 150
References: 38
Citations by year: 2026: 2 · 2025: 7 · 2024: 10 · 2023: 7 · 2022: 10 · 2021: 13 · 2020: 14 · 2019: 10 · 2018: 8 · 2017: 5 · 2016: 10 · 2015: 10 · 2014: 11 · 2013: 10 · 2012: 9
FWCI: 6.38
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS Medicine (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
HAL (submittedVersion, other-oa)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
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: bias prevention, trial design, confounding, randomization
MeSH terms: Humans, Bias, Randomized Controlled Trials as Topic, Practice Guidelines as Topic

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Preventing Bias in Cluster Randomised Trials

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