Evidence map›Paper›PMID 28506284›Full record

ArticleTrials2017

The INVEST project: investigating the use of evidence synthesis in the design and analysis of clinical trials.

Gemma L Clayton, Isabelle L Smith, Julian P T Higgins, Borislava Mihaylova, Benjamin Thorpe, Robert Cicero, Kusal Lokuge, Julia R Forman, Jayne F Tierney, Ian R White and 2 more

Abstract read
In one paragraph

Article in Trials, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
–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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

12 authors.

Gemma L ClaytonSchool of Social and Community Medicine, Faculty of Health Sciences, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK. gemma.clayton@bristol.ac.uk.
Isabelle L SmithLeeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK.
Julian P T HigginsSchool of Social and Community Medicine, Faculty of Health Sciences, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK.
Borislava MihaylovaHealth Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Benjamin ThorpeLeeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK.
Robert CiceroLeeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK.
Kusal LokugeHealth Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Julia R FormanCambridge Clinical Trials Unit, University of Cambridge, Cambridge, UK.
Jayne F TierneyMRC Clinical Trials Unit, University College London, London, UK.
Ian R WhiteMRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK.
Linda D SharplesLondon School of Hygiene and Tropical Medicine, London, UK.
Hayley E JonesSchool of Social and Community Medicine, Faculty of Health Sciences, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK.

Funding

Medical Research Council G0800814Medical Research Council K025643/1Medical Research Council MC_EX_UU_G0800814Medical Research Council MC_UU_12023/24Medical Research Council MRC-E270Medical Research Council MR/K025643/1Medical Research Council MR/L004933/1Medical Research Council MR/L004933/2Medical Research Council MR/M014533/1Medical Research Council U105260558
6 · The paper itself

Abstract

backgroundWhen designing and analysing clinical trials, using previous relevant information, perhaps in the form of evidence syntheses, can reduce research waste. We conducted the INVEST (INVestigating the use of Evidence Synthesis in the design and analysis of clinical Trials) survey to summarise the current use of evidence synthesis in trial design and analysis, to capture opinions of trialists and methodologists on such use, and to understand any barriers.

methodsOur sampling frame was all delegates attending the International Clinical Trials Methodology Conference in November 2015. Respondents were asked to indicate (1) their views on the use of evidence synthesis in trial design and analysis, (2) their own use during the past 10 years and (3) the three greatest barriers to use in practice.

resultsOf approximately 638 attendees of the conference, 106 (17%) completed the survey, half of whom were statisticians. Support was generally high for using a description of previous evidence, a systematic review or a meta-analysis in trial design. Generally, respondents did not seem to be using evidence syntheses as often as they felt they should. For example, only 50% (42/84 relevant respondents) had used a meta-analysis to inform whether a trial is needed compared with 74% (62/84) indicating that this is desirable. Only 6% (5/81 relevant respondents) had used a value of information analysis to inform sample size calculations versus 22% (18/81) indicating support for this. Surprisingly large numbers of participants indicated support for, and previous use of, evidence syntheses in trial analysis. For example, 79% (79/100) of respondents indicated that external information about the treatment effect should be used to inform aspects of the analysis. The greatest perceived barrier to using evidence synthesis methods in trial design or analysis was time constraints, followed by a belief that the new trial was the first in the area.

conclusionsEvidence syntheses can be resource-intensive, but their use in informing the design, conduct and analysis of clinical trials is widely considered desirable. We advocate additional research, training and investment in resources dedicated to ways in which evidence syntheses can be undertaken more efficiently, offering the potential for cost savings in the long term.

Indexed as

Research DesignResearch PersonnelBayes TheoremBiostatisticsClinical Trials as TopicCongresses as TopicData Interpretation, StatisticalEvidence-Based MedicineHumansMeta-Analysis as TopicReview Literature as TopicSurveys and QuestionnairesBayesian analysisDecision modelsInformative prior distributionsMeta-analysisNetwork meta-analysisSample size calculationsSystematic reviewValue of information analysis

Identifiers

PMID28506284
PMCPMC5433067

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.