Evidence map›Paper›PMID 30132361›Full record

ArticleClinical trials (London, England)2018

Triggered or routine site monitoring visits for randomised controlled trials: results of TEMPER, a prospective, matched-pair study.

Sally P Stenning, William J Cragg, Nicola Joffe, Carlos Diaz-Montana, Rahela Choudhury, Matthew R Sydes, Sarah Meredith

Abstract read
In one paragraph

Article in Clinical trials (London, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Monitoring strategies for clinical intervention studies.The Cochrane database of systematic reviews · 2021
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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

7 authors.

Sally P StenningMRC Clinical Trials Unit at UCL, University College London, London, UK.
William J CraggMRC Clinical Trials Unit at UCL, University College London, London, UK.ORCID 0000-0002-1274-8521
Nicola JoffeMRC Clinical Trials Unit at UCL, University College London, London, UK.
Carlos Diaz-MontanaMRC Clinical Trials Unit at UCL, University College London, London, UK.
Rahela ChoudhuryMRC Clinical Trials Unit at UCL, University College London, London, UK.
Matthew R SydesMRC Clinical Trials Unit at UCL, University College London, London, UK.ORCID 0000-0002-9323-1371
Sarah MeredithMRC Clinical Trials Unit at UCL, University College London, London, UK.

Funding

Cancer Research UK 13305Cancer Research UK A13305Medical Research Council MC_EX_G0800814Medical Research Council MC_EX_UU_G0800814Medical Research Council MC_UU_12023/24
6 · The paper itself

Abstract

BACKGROUND/

aimsIn multi-site clinical trials, where trial data and conduct are scrutinised centrally with pre-specified triggers for visits to sites, targeted monitoring may be an efficient way to prioritise on-site monitoring. This approach is widely used in academic trials, but has never been formally evaluated.

methodsTEMPER assessed the ability of targeted monitoring, as used in three ongoing phase III randomised multi-site oncology trials, to distinguish sites at which higher and lower rates of protocol and/or Good Clinical Practice violations would be found during site visits. Using a prospective, matched-pair design, sites that had been prioritised for visits after having activated 'triggers' were matched with a control ('untriggered') site, which would not usually have been visited at that time. The paired sites were visited within 4 weeks of each other, and visit findings are recorded and categorised according to the seriousness of the deviation. The primary outcome measure was the proportion of sites with ≥1 'Major' or 'Critical' finding not previously identified centrally. The study was powered to detect an absolute difference of ≥30% between triggered and untriggered visits. A sensitivity analysis, recommended by the study's blinded endpoint review committee, excluded findings related to re-consent. Additional analyses assessed the prognostic value of individual triggers and data from pre-visit questionnaires completed by site and trials unit staff.

resultsIn total, 42 matched pairs of visits took place between 2013 and 2016. In the primary analysis, 88.1% of triggered visits had ≥1 new Major/Critical finding, compared to 81.0% of untriggered visits, an absolute difference of 7.1% (95% confidence interval -8.3%, +22.5%; p = 0.365). When re-consent findings were excluded, these figures reduced to 85.7% versus 59.5%, (difference = 26.2%, 95% confidence interval 8.0%, 44.4%; p = 0.007). Individual triggers had modest prognostic value but knowledge of the trial-related activities carried out by site staff may be useful.

conclusionTriggered monitoring approaches, as used in these trials, were not sufficiently discriminatory. The rate of Major and Critical findings was higher than anticipated, but the majority related to consent and re-consent with no indication of systemic problems that would impact trial-wide safety issues or integrity of the results in any of the three trials. Sensitivity analyses suggest triggered monitoring may be of potential use, but needs improvement and investigation of further central monitoring triggers is warranted. TEMPER highlights the need to question and evaluate methods in trial conduct, and should inform further developments in this area.

Indexed as

Multicenter Studies as TopicRandomized Controlled Trials as TopicClinical Trials Data Monitoring CommitteesHumansProspective StudiesResearch Designcentral monitoringon-site monitoringquality assurancequality controlRisk-based monitoringtargeted monitoringtriggered monitoring

Identifiers

PMID30132361
PMCPMC6236642

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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.