Handling missing data in RCTs; a review of the top medical journals

Bell, Melanie L; Fiero, Mallorie; Horton, Nicholas J; Hsu, Chiu-Hsieh

doi:10.1186/1471-2288-14-118

Cited by 396Statistical Methods in Clinical TrialsMeta-analysis and systematic reviewsStatistical Methods and Bayesian Inference

Handling missing data in RCTs; a review of the top medical journals

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Bell, Melanie L, Fiero, Mallorie, Horton, Nicholas J, Hsu, Chiu-Hsieh·2014·BMC Medical Research Methodology

DOI 10.1186/1471-2288-14-118PMC PMC4247714

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Abstract

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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Missing data were managed using LMM, which is based on all observed data and accounts for data missing at random.
“Missing data were managed using LMM, which is based on all observed data and accounts for data missing at random [108-110].”
From Development and Evaluation of a Smartphone-Based Chatbot Coach to Facilitate a Balanced Lifestyle in Individuals With Headaches (BalanceUP App): Randomized Controlled Trial (2024) · cites this paper as [110]
Many analyses are described as intention to treat but apply variable definitions in terms of handling missing outcome data or excluding participants who deviate from the intervention or follow-up protocols.
“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]
Most randomised trials encounter some degree of missing outcome and covariate data.
“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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[7]Council NR The prevention and treatment of missing data in clinical trials Committee on National Statistics, Division of Behavioral and Social Sciences and Education 2010 Washington DC National Academies Press 24983040
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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
ORCID: Bell, Melanie L
Fiero, Mallorie
Horton, Nicholas J
Hsu, Chiu-Hsieh
Venue: BMC Medical Research Methodology
Publisher: Springer Science and Business Media LLC
Published: 2014-11-19
Volume: 14
Issue: 1
Pages: 118-118
ISSN: 1471-2288 (ISSN-L)
DOI: 10.1186/1471-2288-14-118
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: cc-by
www.springer.com/tdm (unspecified)
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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