Evidence map›Paper›PMID 31029148›Full record

Trial reportTrials2019

Early warnings and repayment plans: novel trial management methods for monitoring and managing data return rates in a multi-centre phase III randomised controlled trial with paper Case Report Forms.

William J Cragg, Fay Cafferty, Carlos Diaz-Montana, Elizabeth C James, Johnathan Joffe, Monica Mascarenhas, Victoria Yorke-Edwards

Abstract readClinical Trial, Phase IIIMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in Trials, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

  1. Monitoring strategies for clinical intervention studies.The Cochrane database of systematic reviews · 2021
    Pooled it
  2. Article
  3. Article
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.

William J CraggMRC Clinical Trials Unit at UCL, London, UK. w.cragg@leeds.ac.uk.ORCID http://orcid.org/0000-0002-1274-8521
Fay CaffertyMRC Clinical Trials Unit at UCL, London, UK.ORCID http://orcid.org/0000-0002-0973-660X
Carlos Diaz-MontanaMRC Clinical Trials Unit at UCL, London, UK.ORCID http://orcid.org/0000-0001-9082-4596
Elizabeth C JamesMRC Clinical Trials Unit at UCL, London, UK.ORCID http://orcid.org/0000-0002-0490-7211
Johnathan JoffeCalderdale & Huddersfield NHS Foundation Trust, Huddersfield, UK.ORCID http://orcid.org/0000-0001-7249-8379
Monica MascarenhasMRC Clinical Trials Unit at UCL, London, UK.ORCID http://orcid.org/0000-0002-4623-3147
Victoria Yorke-EdwardsMRC Clinical Trials Unit at UCL, London, UK.ORCID http://orcid.org/0000-0002-6465-7330

Funding

Cancer Research UK 8690Cancer Research UK C17084/A8690Medical Research Council MC_UU_12023/24Medical Research Council MC_UU_12023/28
6 · The paper itself

Abstract

backgroundMonitoring and managing data returns in multi-centre randomised controlled trials is an important aspect of trial management. Maintaining consistently high data return rates has various benefits for trials, including enhancing oversight, improving reliability of central monitoring techniques and helping prepare for database lock and trial analyses. Despite this, there is little evidence to support best practice, and current standard methods may not be optimal.

methodsWe report novel methods from the Trial of Imaging and Schedule in Seminoma Testis (TRISST), a UK-based, multi-centre, phase III trial using paper Case Report Forms to collect data over a 6-year follow-up period for 669 patients. Using an automated database report which summarises the data return rate overall and per centre, we developed a Microsoft Excel-based tool to allow observation of per-centre trends in data return rate over time. The tool allowed us to distinguish between forms that can and cannot be completed retrospectively, to inform understanding of issues at individual centres. We reviewed these statistics at regular trials unit team meetings. We notified centres whose data return rate appeared to be falling, even if they had not yet crossed the pre-defined acceptability threshold of an 80% data return rate. We developed a set method for agreeing targets for gradual improvement with centres having persistent data return problems. We formalised a detailed escalation policy to manage centres who failed to meet agreed targets. We conducted a post-hoc, descriptive analysis of the effectiveness of the new processes.

resultsThe new processes were used from April 2015 to September 2016. By May 2016, data return rates were higher than they had been at any time previously, and there were no centres with return rates below 80%, which had never been the case before. In total, 10 centres out of 35 were contacted regarding falling data return rates. Six out of these 10 showed improved rates within 6-8 weeks, and the remainder within 4 months.

conclusionsOur results constitute preliminary effectiveness evidence for novel methods in monitoring and managing data return rates in randomised controlled trials. We encourage other researchers to work on generating better evidence-based methods in this area, whether through more robust evaluation of our methods or of others.

Indexed as

Data AccuracyForms as TopicData ManagementForms and Records ControlHumansMagnetic Resonance ImagingMaleNeoplasm Recurrence, LocalOrchiectomyPredictive Value of TestsRadiation ExposureResearch DesignSeminomaTesticular NeoplasmsTime FactorsTomography, X-Ray ComputedCase Report Form returnsCentral monitoringData completenessData managementData return ratesTrial management

Identifiers

PMID31029148
PMCPMC6486995

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.