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Biases in Randomized Trials: A Conversation Between Trialists and Epidemiologists
Open accessAbstract
Trialists and epidemiologists often employ different terminology to refer to biases in randomized trials and observational studies, even though many biases have a similar structure in both types of study. We use causal diagrams to represent the structure of biases, as described by Cochrane for randomized trials, and provide a translation to the usual epidemiologic terms of confounding, selection bias, and measurement bias. This structural approach clarifies that an explicit description of the inferential goal-the intention-to-treat effect or the per-protocol effect-is necessary to assess risk of bias in the estimates. Being aware of each other's terminologies will enhance communication between trialists and epidemiologists when considering key concepts and methods for causal inference.
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“We label the domains using descriptions of the causes of bias addressed, avoiding terms used in the original RoB tool (such as 'selection bias' and 'performance bias') because they are used inconsistently or not known by many people outside Cochrane. 16”
From RoB 2: a revised tool for assessing risk of bias in randomised trials (2019) · cites this paper as [16]
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Cites
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Self-citationMatched designs and causal diagrams
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Cited by
Sorted by citations
RoB 2: a revised tool for assessing risk of bias in randomised trials
Sterne, J. A. C., Savović, J., Page, M. J., Elbers, R. G., Blencowe, N. S., Boutron, I., Cates, C. J., Cheng, H.-Y., Corbett, M. S., Eldridge, S. M., et al. (2019) · BMJ · doi:10.1136/bmj.l4898 · Cited by 34,787
Self-citationROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions
Sterne, J. A., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., Viswanathan, M., Henry, D., Altman, D. G., Ansari, M. T., Boutron, I., et al. (2016) · BMJ · doi:10.1136/bmj.i4919 · Cited by 20,329
Self-citationAssessing risk of bias in a randomized trial
Higgins, J. P., Savović, J., Page, M. J., Elbers, R. G., & Sterne, J. A. (2019) · Cochrane Handbook for Systematic Reviews of Interventions · doi:10.1002/9781119536604.ch8 · Cited by 1,765
Self-citationDekkers, O. M., Vandenbroucke, J. P., Cevallos, M., Renehan, A. G., Altman, D. G., & Egger, M. (2019) · PLOS Medicine · doi:10.1371/journal.pmed.1002742 · Cited by 525
Tutorial on directed acyclic graphs
Digitale, J. C., Martin, J. N., & Glymour, M. M. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.08.001 · Cited by 408
Metadata
- Authors
- Mansournia, M. A., Higgins, J. P. T., Sterne, J. A. C., & Hernán, M. A.
- Affiliations
- Harvard University (US)
- Venue
- Epidemiology
- Published
- 2017-01
- Volume
- 28
- Issue
- 1
- Pages
- 54-59
- ISSN
- 1044-3983 (ISSN-L)
- PMC
- PMC5130591
- OpenAlex
- W2336975416
- Cited by
- 285
- References
- 25
- Citations by year
- 2026: 18 · 2025: 30 · 2024: 30 · 2023: 29 · 2022: 40 · 2021: 39 · 2020: 42 · 2019: 29 · 2018: 17 · 2017: 9
- FWCI
- 19.21
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Bristol Research (University of Bristol) (acceptedVersion, other-oa)
- Explore Bristol Research (submittedVersion, other-oa)
- Funders
- National Institute for Health and Care Research (NF-SI-0611-10168, NF-SI-0616-10111)
- Medical Research Council (MR/M025209/1, MR/K025643/1)
- National Cancer Institute (P01CA134294, R35CA197449)
- National Institute of Allergy and Infectious Diseases (R01AI102634)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- confounding, selection bias, measurement bias, causal graphs, intention-to-treat effect, risk of bias, terminology translation, causal inference
- MeSH terms
- Humans, Patient Dropouts, Observer Variation, Bias, Selection Bias, Causality, Confounding Factors, Epidemiologic, Randomized Controlled Trials as Topic, Intention to Treat Analysis
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