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Catalogue of bias: attrition bias
Abstract
This article is part of a series of articles featuring the Catalogue of Bias introduced in this volume of BMJ Evidence-Based Medicine that describes attrition bias and outlines its potential impact on research studies and the preventive steps to minimise its risk. Attrition bias is a type of selection bias due to systematic differences between study groups in the number and the way participants are lost from a study. Differences between people who leave a study and those who continue, particularly between study groups, can be the reason for any observed effect and not the intervention itself. Associations for mortality in trials of tranexamic acid and upper gastrointestinal bleeding were no longer apparent after studies with high or unclear risk of attrition bias were removed. Over-recruitment can help prevent important attrition bias. Sampling weights and tailored replenishment samples can help to compensate for the effects of attrition bias when present.
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“and/or (3) studies with high attrition rates (≥30% in at least one arm), given attrition rates of ≥30% are associated with imbalances at baseline and are therefore vulnerable to bias such as clinical and psychosocial differences between groups due to differences between those that leave or remain in the study, which is likely to have an impact on the estimated effect of the exposure.98”
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Metadata
- Authors
- Nunan, D., Aronson, J., & Bankhead, C.
- Affiliations
- University of Oxford (GB)
- Venue
- BMJ Evidence-Based Medicine
- Publisher
- BMJ
- Published
- 2018-01-24
- Volume
- 23
- Issue
- 1
- Pages
- 21-22
- ISSN
- 1356-5524 (ISSN-L)
- 2515-446X
- 2515-4478
- OpenAlex
- W2784801669
- Cited by
- 204
- References
- 9
- Citations by year
- 2026: 25 · 2025: 36 · 2024: 32 · 2023: 27 · 2022: 39 · 2021: 24 · 2020: 15 · 2019: 6
- FWCI
- 8.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- BMJ Evidence-Based Medicine (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (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
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- attrition bias, selection bias, participant dropout, trial bias, sampling weights, tranexamic acid, upper gastrointestinal bleeding, mortality
- MeSH terms
- Humans, Patient Dropouts, Bias, Selection Bias
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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