Evidence map›Paper›PMID 33256626›Full record

Trial reportBMC medical research methodology2020

Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention.

Ravinder Claire, Christian Gluud, Ivan Berlin, Tim Coleman, Jo Leonardi-Bee

Open access · goldAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 14 pooled it
8.9field-weighted citation impact, top 2% of its field
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

26 citing papers in PubMed, 14 syntheses or guidelines pooled it, 55 citations in OpenAlex.

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  11. Feedback for Promoting Motor Skill Learning in Physical Education: A Trial Sequential Meta-Analysis.International journal of environmental research and public health · 2022
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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 at 3 institutions in 4 countries.

Ravinder ClaireDivision of Primary Care, University of Nottingham, Nottingham, NG7 2RD, UK. ravinder.claire@nottingham.ac.uk.ORCID 0000-0001-8039-6161
Christian GluudCopenhagen Trial Unit, Centre for Clinical Intervention Research, Department 7812, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
Ivan BerlinDépartement de pharmacologie, Hôpital Pitié-Salpêtrière, Paris, France.
Tim ColemanDivision of Primary Care, University of Nottingham, Nottingham, NG7 2RD, UK.
Jo Leonardi-BeeDivision of Epidemiology and Public Health, University of Nottingham, Nottingham, NG5 1PB, UK.
University of Nottingham · GBCopenhagen University Hospital · DKUniversity Hospital of Lausanne · CH

Funding

Department of Health RP-PG-0109-10020
6 · The paper itself

Abstract

backgroundAssessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms.

methodsWe describe using feasibility and pilot trial data in the Trial Sequential Analysis software to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using trial sequential analysis methods to assess the intervention's effects.

resultsWe provide a worked example to illustrate how we successfully used the Trial Sequential Analysis software to arrive at a sensible sample size for a new randomised clinical trial and use it in the argumentation for research funds for the trial.

conclusionsTrial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more, similarly designed, future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.

Indexed as

Smoking CessationBehavior TherapyHumansResearch DesignSample SizeSoftwareFeasibility trialInformation sizeMeta-analysisPilot trialPregnancyRandomised clinical trialSample sizeSmokingTrial sequential analysis methodsTrial Sequential Analysis software

Identifiers

PMID33256626
PMCPMC7702700
OpenAlexW3108340206

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.