Evidence map›Paper›PMID 38810931›Full record

ArticleContemporary clinical trials2024

A risk-based monitoring approach to source data monitoring and documenting monitoring findings.

Maryse Brulotte, Jessica S Alvey, T Charles Casper, Lawrence J Cook, Jamie P Dwyer, John M VanBuren

Abstract read
In one paragraph

Article in Contemporary clinical trials, 2024. 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

6 authors.

Maryse BrulotteUtah Data Coordinating Center, University of Utah, Salt Lake City, UT 84108, USA. Electronic address: maryse.brulotte@hsc.utah.edu.
Jessica S AlveyUtah Data Coordinating Center, University of Utah, Salt Lake City, UT 84108, USA.
T Charles CasperUtah Data Coordinating Center, University of Utah, Salt Lake City, UT 84108, USA.
Lawrence J CookUtah Data Coordinating Center, University of Utah, Salt Lake City, UT 84108, USA.
Jamie P DwyerUtah Data Coordinating Center, University of Utah, Salt Lake City, UT 84108, USA.
John M VanBurenUtah Data Coordinating Center, University of Utah, Salt Lake City, UT 84108, USA.

Funding

Utah Trial Innovation CenterU24TR001597 · NCATS · UNIVERSITY OF UTAH · PI DEAN, JONATHAN MICHAEL · 2016 to 2022
$39.6M
NCATS NIH HHS U24 TR001597
6 · The paper itself

Abstract

backgroundClinical trial monitoring is evolving from labor-intensive to targeted approaches. The traditional 100% Source Data Monitoring (SDM) approach fails to prioritize data by significance, diverting attention from critical elements. Despite regulatory guidance on Risk-Based Monitoring (RBM), its widespread implementation has been slow.

methodsOur study teams assess the study's overall risk, document heightened and critical risks, and create a study-specific risk-based monitoring plan, integrating SDM and Central Data Monitoring (CDM). SDM combines a fixed list of pre-identified variables and a list of randomly identified variables to monitor. Identifying variables follows a two-step approach: first, a random sample of participants is selected, second, a random set of variables for each participant selected is identified. Sampling weights prioritize critical variables. Regular team meetings are held to discuss and compile significant findings into a Study Monitoring Report.

resultsWe present a random SDM sample and a Study Monitoring Report. The random SDM output includes a look-up table for selected database elements. The report provides a holistic view of the study issues and overall health.

conclusionsThe proposed random sampling method is used to monitor a representative set of critical variables, while the Study Monitoring Report is written to summarize significant monitoring findings and data trends. The report allows the sponsor to assess the current status of the study and data effectively. Communicating and sharing emerging insights facilitates timely adjustments of future monitoring activities, optimizing efficiencies, and study outcomes.

Indexed as

Research DesignClinical Trials as TopicClinical Trials Data Monitoring CommitteesHumansRisk AssessmentCentral data monitoringRisk assessment risk managementRisk-based monitoringSource data monitoringStatistical samplingStudy monitoring report

Identifiers

PMID38810931
PMCPMC11283940

What OpenQuestion holds

Textmetadata
LicenceTDM
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.