Evidence map›Paper›PMID 41419958›Full record

ReviewTrials2025

When randomisation goes horribly wrong: examples of major failures of randomisation and strategies to avoid them.

Lisa N Yelland, Kylie M Lange, Sabine Braat, Kristy P Robledo, Alana R Cuthbert, Thomas R Sullivan

Abstract readReview
In one paragraph

Review in Trials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Lisa N YellandWomen and Kids Theme, South Australian Health and Medical Research Institute, PO Box 11060, Adelaide, SA, 5001, Australia.
Kylie M LangeWomen and Kids Theme, South Australian Health and Medical Research Institute, PO Box 11060, Adelaide, SA, 5001, Australia.
Sabine BraatCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
Kristy P RobledoNHMRC Clinical Trials Centre, University of Sydney, Camperdown, NSW, Australia.
Alana R CuthbertWomen and Kids Theme, South Australian Health and Medical Research Institute, PO Box 11060, Adelaide, SA, 5001, Australia.
Thomas R SullivanWomen and Kids Theme, South Australian Health and Medical Research Institute, PO Box 11060, Adelaide, SA, 5001, Australia. thomas.sullivan@sahmri.com.

Funding

Hospital Research Foundation 104-83100National Health and Medical Research Council 1171422National Health and Medical Research Council 2033081
6 · The paper itself

Abstract

backgroundRandomisation forms the foundation of clinical trials, but its implementation can be prone to error. Often randomisation errors affect few participants and do not seriously compromise the integrity of the trial. However, in some cases randomisation errors can have widespread consequences and call into question the validity of trial conclusions. Published articles may be retracted as a result. Valuable insight can be gained from studying past errors to minimise the risk of similar errors and their disastrous consequences impacting future trials. The aims of this article are to (i) describe examples of major failures of randomisation, and (ii) provide guidance on how to avoid them in practice.

methodsMajor failures of randomisation were defined as inadvertent errors that affected many trial participants and occurred during the process of designing the randomisation scheme, generating the randomisation schedule, allocating participants to treatment groups, or providing the assigned treatment. Examples of major failures of randomisation were drawn from author experience and through a review of the published literature, which included a systematic search of the Retraction Watch Database for serious randomisation problems that led to the retraction of a published article. Practice points to avoid such errors were developed by consensus among the authors.

resultsExamples are provided of seven broad types of major failures of randomisation: randomisation schedule followed incorrectly, randomisation schedule sorted incorrectly, randomisation schedule too short, clusters handled incorrectly, incorrect or unknown treatment provided at randomisation, poorly designed randomisation scheme, and programming errors in adaptive randomisation schemes. Practice points for avoiding such errors are presented, including suggestions for written documentation, staff training, and thorough testing of the randomisation process prior to trial commencement.

conclusionsRandomisation is of fundamental importance in clinical trials. Greater consideration should be given to the potential for major failures of randomisation and strategies to avoid them. When major failures of randomisation do occur, greater transparency in reporting is needed.

Indexed as

Randomized Controlled Trials as TopicResearch DesignHumansClinical trialsGuidelinesRandomisation errorRandomisation scheduleRetractionTransparent reporting

Identifiers

PMID41419958
PMCPMC12837040

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.