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Potential impact on estimated treatment effects of information lost to follow-up in randomised controlled trials (LOST-IT): systematic review
Open accessAbstract
OBJECTIVE: To assess the reporting, extent, and handling of loss to follow-up and its potential impact on the estimates of the effect of treatment in randomised controlled trials. DESIGN: Systematic review. We calculated the percentage of trials for which the relative risk would no longer be significant under a number of assumptions about the outcomes of participants lost to follow-up. DATA SOURCES: Medline search of five top general medical journals, 2005-07. ELIGIBILITY CRITERIA: Randomised controlled trials that reported a significant binary primary patient important outcome. RESULTS: Of the 235 eligible reports identified, 31 (13%) did not report whether or not loss to follow-up occurred. In reports that did give the relevant information, the median percentage of participants lost to follow-up was 6% (interquartile range 2-14%). The method by which loss to follow-up was handled was unclear in 37 studies (19%); the most commonly used method was survival analysis (66, 35%). When we varied assumptions about loss to follow-up, results of 19% of trials were no longer significant if we assumed no participants lost to follow-up had the event of interest, 17% if we assumed that all participants lost to follow-up had the event, and 58% if we assumed a worst case scenario (all participants lost to follow-up in the treatment group and none of those in the control group had the event). Under more plausible assumptions, in which the incidence of events in those lost to follow-up relative to those followed-up is higher in the intervention than control group, results of 0% to 33% trials were no longer significant. CONCLUSION: Plausible assumptions regarding outcomes of patients lost to follow-up could change the interpretation of results of randomised controlled trials published in top medical journals.
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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 [426]
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“Missing data negatively affect trials by reducing statistical power and introducing potential bias.426”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [426]
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Metadata
- Authors
- Akl, E. A., Briel, M., You, J. J., Sun, X., Johnston, B. C., Busse, J. W., Mulla, S., Lamontagne, F., Bassler, D., Vera, C., Alshurafa, M., Katsios, C. M., Zhou, Q., Cukierman-Yaffe, T., Gangji, A., Mills, E. J., Walter, S. D., Cook, D. J., Schunemann, H. J., Altman, D. G., & Guyatt, G. H.
- Affiliations
- University at Buffalo, State University of New York (US)
- University of Basel (CH)
- McMaster University (CA)
- Kaiser Permanente (US)
- Hospital for Sick Children (CA)
- Institute for Work & Health (CA)
- Université de Sherbrooke (CA)
- University Children's Hospital Tübingen (DE)
- Pontificia Universidad Católica de Chile (CL)
- University of Ottawa (CA)
- University of Oxford (GB)
- Corresponding author
- Akl, E. A.
- ORCID
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2012-05-18
- Volume
- 344
- Issue
- may18 1
- Pages
- e2809-e2809
- ISSN
- 1756-1833 (ISSN-L)
- OpenAlex
- W1973748105
- Cited by
- 350
- References
- 40
- Citations by year
- 2026: 16 · 2025: 33 · 2024: 24 · 2023: 23 · 2022: 31 · 2021: 32 · 2020: 39 · 2019: 25 · 2018: 23 · 2017: 24 · 2016: 24 · 2015: 26 · 2014: 13 · 2013: 13 · 2012: 4
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- BMJ (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Pfizer
- Canadian Society of Nephrology
- Kidney Foundation of Canada
- Ontario Ministry of Health and Long-Term Care
- Cancer Research UK
- National Natural Science Foundation of China
- Hospital for Sick Children
- Canadian Institutes of Health Research
- 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 in Epidemiology
- Keywords
- loss to follow-up, randomized controlled trials, treatment effect estimation, survival analysis, relative risk, binary outcomes
- MeSH terms
- Data Interpretation, Statistical, Follow-Up Studies, Humans, Patient Dropouts, Risk, Survival Analysis, Randomized Controlled Trials as Topic, Treatment Outcome, Lost to Follow-Up
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