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Handling missing data in RCTs; a review of the top medical journals
Open accessAbstract
BACKGROUND: Missing outcome data is a threat to the validity of treatment effect estimates in randomized controlled trials. We aimed to evaluate the extent, handling, and sensitivity analysis of missing data and intention-to-treat (ITT) analysis of randomized controlled trials (RCTs) in top tier medical journals, and compare our findings with previous reviews related to missing data and ITT in RCTs. METHODS: Review of RCTs published between July and December 2013 in the BMJ, JAMA, Lancet, and New England Journal of Medicine, excluding cluster randomized trials and trials whose primary outcome was survival. RESULTS: Of the 77 identified eligible articles, 73 (95%) reported some missing outcome data. The median percentage of participants with a missing outcome was 9% (range 0 - 70%). The most commonly used method to handle missing data in the primary analysis was complete case analysis (33, 45%), while 20 (27%) performed simple imputation, 15 (19%) used model based methods, and 6 (8%) used multiple imputation. 27 (35%) trials with missing data reported a sensitivity analysis. However, most did not alter the assumptions of missing data from the primary analysis. Reports of ITT or modified ITT were found in 52 (85%) trials, with 21 (40%) of them including all randomized participants. A comparison to a review of trials reported in 2001 showed that missing data rates and approaches are similar, but the use of the term ITT has increased, as has the report of sensitivity analysis. CONCLUSIONS: Missing outcome data continues to be a common problem in RCTs. Definitions of the ITT approach remain inconsistent across trials. A large gap is apparent between statistical methods research related to missing data and use of these methods in application settings, including RCTs in top medical journals.
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“Many analyses are described as intention to treat but apply variable definitions in terms of handling missing outcome data (item 27c) or excluding participants who deviate from the intervention or follow-up protocols.421 422”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [422]
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“Missing data were managed using LMM, which is based on all observed data and accounts for data missing at random [108-110].”
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“Most randomised trials encounter some degree of missing outcome and covariate data.422 426”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [422]
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Metadata
- Authors
- Bell, M. L., Fiero, M., Horton, N. J., & Hsu, C.-H.
- Affiliations
- University of Arizona (US)
- Amherst College (US)
- Corresponding author
- Bell, Melanie L
- Venue
- BMC Medical Research Methodology
- Published
- 2014-11-19
- Volume
- 14
- Issue
- 1
- Pages
- 118-118
- ISSN
- 1471-2288 (ISSN-L)
- PMC
- PMC4247714
- OpenAlex
- W2085508623
- Cited by
- 395
- References
- 30
- Citations by year
- 2026: 28 · 2025: 42 · 2024: 39 · 2023: 46 · 2022: 44 · 2021: 51 · 2020: 34 · 2019: 34 · 2018: 26 · 2017: 25 · 2016: 17 · 2015: 9
- FWCI
- 17.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Research Methodology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UA Campus Repository (The University of Arizona) (acceptedVersion, cc-by)
- 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 › Statistical Methods and Bayesian Inference
- Keywords
- missing data, randomized controlled trials, intention-to-treat analysis, sensitivity analysis, complete case analysis, multiple imputation, missing data handling, reporting practices, medical journals
- MeSH terms
- Humans, Publishing, Research Design, Randomized Controlled Trials as Topic, Treatment Outcome, Intention to Treat Analysis
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