Evidence map›Paper›PMID 36221107›Full record

ReviewTrials2022

Managing clustering effects and learning effects in the design and analysis of randomised surgical trials: a review of existing guidance.

Elizabeth J Conroy, Jane M Blazeby, Girvan Burnside, Jonathan A Cook, Carrol Gamble

Abstract readReview
In one paragraph

Review in Trials, 2022. 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. Trial
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

5 authors.

Elizabeth J ConroyLiverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK. elizabeth.conroy@ndorms.ox.ac.uk.ORCID http://orcid.org/0000-0003-4858-727X
Jane M BlazebyCentre for Surgical Research, Bristol Biomedical Research Centre, Population Health Sciences, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0002-3354-3330
Girvan BurnsideLiverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK.ORCID https://orcid.org/0000-0001-7398-1346
Jonathan A CookOxford Clinical Trials Research Unit, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Windmill Road, Oxford, OX3 7LD, UK.ORCID https://orcid.org/0000-0002-4156-6989
Carrol GambleLiverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK.ORCID https://orcid.org/0000-0002-3021-1955

Funding

Medical Research Council MR/K025643/1Research Trainees Coordinating Centre DRF-2015-08-082
6 · The paper itself

Abstract

backgroundThe complexities associated with delivering randomised surgical trials, such as clustering effects, by centre or surgeon, and surgical learning, are well known. Despite this, approaches used to manage these complexities, and opinions on these, vary. Guidance documents have been developed to support clinical trial design and reporting. This work aimed to identify and examine existing guidance and consider its relevance to clustering effects and learning curves within surgical trials.

methodsA review of existing guidelines, developed to inform the design and analysis of randomised controlled trials, is undertaken. Guidelines were identified using an electronic search, within the Equator Network, and by a targeted search of those endorsed by leading UK funding bodies, regulators, and medical journals. Eligible documents were compared against pre-specified key criteria to identify gaps or inconsistencies in recommendations.

resultsTwenty-eight documents were eligible (12 Equator Network; 16 targeted search). Twice the number of guidance documents targeted design (n/N=20/28, 71%) than analysis (n/N=10/28, 36%). Managing clustering by centre through design was well documented. Clustering by surgeon had less coverage and contained some inconsistencies. Managing the surgical learning curve, or changes in delivery over time, through design was contained within several documents (n/N=8/28, 29%), of which one provided guidance on reporting this and restricted to early phase studies only. Methods to analyse clustering effects and learning were provided in five and four documents respectively (N=28).

conclusionsTo our knowledge, this is the first review as to the extent to which existing guidance for designing and analysing randomised surgical trials covers the management of clustering, by centre or surgeon, and the surgical learning curve. Twice the number of identified documents targeted design aspects than analysis. Most notably, no single document exists for use when designing these studies, which may lead to inconsistencies in practice. The development of a single document, with agreed principles to guide trial design and analysis across a range of realistic clinical scenarios, is needed.

Indexed as

LearningCluster AnalysisData CollectionHumansRandomized Controlled Trials as TopicClinical trialClusteringComplex interventionLearningRandomised controlled trialReviewSummarySurgical interventionTrial analysisTrial designTrials

Identifiers

PMID36221107
PMCPMC9552436

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.