Sample size slippages in randomised trials: exclusions and the lost and wayward.
Abstract
Proper randomisation means little if investigators cannot include all randomised participants in the primary analysis. Participants might ignore follow-up, leave town, or take aspartame when instructed to take aspirin. Exclusions before randomisation do not bias the treatment comparison, but they can hurt generalisability. Eligibility criteria for a trial should be clear, specific, and applied before randomisation. Readers should assess whether any of the criteria make the trial sample atypical or unrepresentative of the people in which they are interested. In principle, assessment of exclusions after randomisation is simple: none are allowed. For the primary analysis, all participants enrolled should be included and analysed as part of the original group assigned (an intent-to-treat analysis). In reality, however, losses frequently occur. Investigators should, therefore, commit adequate resources to develop and implement procedures to maximise retention of participants. Moreover, researchers should provide clear, explicit information on the progress of all randomised participants through the trial by use of, for instance, a trial profile. Investigators can also do secondary analyses on, for instance, per-protocol or as-treated participants. Such analyses should be described as secondary and non-randomised comparisons. Mishandling of exclusions causes serious methodological difficulties. Unfortunately, some explanations for mishandling exclusions intuitively appeal to readers, disguising the seriousness of the issues. Creative mismanagement of exclusions can undermine trial validity.
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The attrition rate was close to the acceptable threshold of 20% of randomized participants, and the only baseline differences between completers and dropouts were lower levels of anxiety and perceived stress in patients who dropped out of the PEI.
“Although the attrition rate was close to the acceptable threshold, which is 20% of randomized participants (Schulz and Grimes, 2002), the only baseline differences between completers and dropouts, were lower levels of anxiety and perceived stress in patients that dropped out of the PEI.”
From The effectiveness of a body-affective mindfulness intervention for multiple sclerosis patients with depressive symptoms: A randomized controlled clinical trial (2017) · cites this paper as Schulz and Grimes, 2002
Such attrition can affect the ability of results to be generalized and it undermines the statistical power of the trial.
“Such attrition can affect the ability of results to be generalized [10,11] and it undermines the statistical power of the trial.”
From A Systematic Review of the Impact of Adherence on the Effectiveness of e-Therapies (2011) · cites this paper as [11]
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- Authors
- Schulz, K. F., & Grimes, D. A.
- Affiliations
- Family Health International 360 (US)
- Corresponding author
- Schulz, Kenneth F
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2002-03
- Volume
- 359
- Issue
- 9308
- Pages
- 781-785
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- OpenAlex
- W2115086661
- Cited by
- 642
- References
- 27
- Citations by year
- 2026: 42 · 2025: 30 · 2024: 39 · 2023: 32 · 2022: 30 · 2021: 49 · 2020: 34 · 2019: 37 · 2018: 35 · 2017: 33 · 2016: 28 · 2015: 26 · 2014: 29 · 2013: 29 · 2012: 17
- FWCI
- 20.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Lancet (publishedVersion)
- PubMed (publishedVersion)
- 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 › Advanced Causal Inference Techniques
- Keywords
- randomized controlled trials, sample size, exclusion, loss to follow-up, eligibility criteria, intention-to-treat analysis, per-protocol analysis, as-treated analysis, participant retention, generalizability, trial validity, missing data
- MeSH terms
- Follow-Up Studies, Humans, Patient Dropouts, Bias, Randomized Controlled Trials as Topic, Outcome Assessment, Health Care, Sample Size
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