Timeline cluster: a graphical tool to identify risk of bias in cluster randomised trials
Open accessAbstract
Robust evidence of the effectiveness of interventions relating to policy, practice, and organisation of healthcare often comes from well conducted cluster randomised trials.Such trials are, however, prone to recruitment bias depending on whether participants are recruited before the randomisation of clusters and whether the recruiter is blinded to the allocation status.In most cases, participants and trial staff cannot be blinded to the intervention, which might lead to performance and detection bias.Unfortunately, cluster trial reports often do not provide a clear description of the timing of trial processes and blinding, and these aspects are not covered by current reporting tools.This article proposes a graphical tool depicting the time sequence of steps and blinding status in cluster randomised trials.The tool might be helpful at both the protocol and the report writing stages to clarify the process and to help identify potential bias in cluster randomised trials.
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Timeline diagrams provide one method of improving reporting of the elements around timing and blinding status of identification and recruitment of participants.
“Timeline diagrams provide one method of improving reporting of the elements around timing and blinding status of identification and recruitment of participants [4].”
From Cluster randomized trials of individual-level interventions were at high risk of bias (2021) · cites this paper as [4]
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Self-citation
- Authors
- Caille, A., Kerry, S., Tavernier, E., Leyrat, C., Eldridge, S., & Giraudeau, B.
- Affiliations
- Université de Tours (FR)
- Queen Mary University of London (GB)
- Inserm (FR)
- London School of Hygiene & Tropical Medicine (GB)
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2016-08-16
- Volume
- 354
- Pages
- i4291
- ISSN
- 1756-1833 (ISSN-L)
- OpenAlex
- W2515337482
- Cited by
- 64
- References
- 23
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 5 · 2023: 3 · 2022: 12 · 2021: 5 · 2020: 15 · 2019: 8 · 2018: 6 · 2017: 3
- FWCI
- 5.34
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- BMJ (publishedVersion)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- HAL (submittedVersion)
- HAL (submittedVersion, other-oa)
- HighWire Press Open Archive (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Keywords
- risk of bias, recruitment bias, blinding, performance bias, detection bias, trial reporting, graphical tools, chronology of events
- MeSH terms
- Computer Graphics, Data Interpretation, Statistical, Humans, Risk Factors, Time Factors, Bias, Cluster Analysis, Randomized Controlled Trials as Topic, Patient Selection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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