The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
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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Cites
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Reporting attrition in randomised controlled trials
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Tranexamic acid for upper gastrointestinal bleeding
Bennett, C., Klingenberg, S. L., Langholz, E., & Gluud, L. L. (2014) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd006640.pub3 · Cited by 188
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Randomized controlled trial of hip protectors among women living in the community
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Sorted by citations
Barker, T. H., Stone, J. C., Sears, K., Klugar, M., Leonardi-Bee, J., Tufanaru, C., Aromataris, E., & Munn, Z. (2023) · JBI Evidence Synthesis · doi:10.11124/jbies-22-00125 · Cited by 471
Where to look for the most frequent biases?
Jager, K. J., Tripepi, G. L., Chesnaye, N. C., Dekker, F. W., Zoccali, C., & Stel, V. S. (2020) · Nephrology · doi:10.1111/nep.13706 · Cited by 168
Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024
Carriero, A., Groenhoff, L., Vologina, E., Basile, P., & Albera, M. (2024) · Diagnostics · doi:10.3390/diagnostics14080848 · Cited by 187
Algorithmic fairness in computational medicine
Xu, J., Xiao, Y., Wang, W. H., Ning, Y., Shenkman, E. A., Bian, J., & Wang, F. (2022) · eBioMedicine · doi:10.1016/j.ebiom.2022.104250 · Cited by 170
Defining and evaluating the Hawthorne effect in primary care, a systematic review and meta-analysis
Berkhout, C., Berbra, O., Favre, J., Collins, C., Calafiore, M., Peremans, L., & Royen, P. V. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.1033486 · Cited by 174
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
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