Evidence map›Paper›PMID 28359194›Full record

ArticleClinical trials (London, England)2017

Data monitoring committees: Promoting best practices to address emerging challenges.

Thomas R Fleming, David L DeMets, Matthew T Roe, Janet Wittes, Karim A Calis, Amit N Vora, Alan Meisel, Raymond P Bain, Marvin A Konstam, Michael J Pencina and 8 more

Open access · greenAbstract read
In one paragraph

Article in Clinical trials (London, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
6.3field-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

36 citing papers in PubMed, 81 citations in OpenAlex.

  1. Trial
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  3. A python's embrace? Insurance and the global clinical trial.Health research policy and systems · 2026
    Article
  4. Article
  5. Article
  6. Review
  7. Data Monitoring Committee Reports: Telling the Data's Story.Therapeutic innovation & regulatory science · 2025
    Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Review
  13. Article
  14. Review
  15. The Data Monitoring Committee: A Collective or a Collection?Therapeutic innovation & regulatory science · 2023
    Article
  16. Article
  17. Article
  18. Guidance on interim analysis methods in clinical trials.Journal of clinical and translational science · 2023
    Review
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors at 16 institutions in 2 countries.

Thomas R Fleming1 Department of Biostatistics, University of Washington, Seattle, WA, USA.
David L DeMets2 University of Wisconsin-Madison, Madison, WI, USA.
Matthew T Roe3 Duke Clinical Research Institute (DCRI), Duke University Medical Center, Durham, NC, USA.
Janet Wittes4 Statistics Collaborative, Inc., Washington, DC, USA.
Karim A Calis5 Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA.
Amit N Vora3 Duke Clinical Research Institute (DCRI), Duke University Medical Center, Durham, NC, USA.
Alan Meisel7 University of Pittsburgh, Pittsburgh, PA, USA.
Raymond P Bain8 Merck Research Laboratories, North Wales, PA, USA.
Marvin A Konstam9 Tufts Medical Center, Boston, MA, USA.
Michael J Pencina3 Duke Clinical Research Institute (DCRI), Duke University Medical Center, Durham, NC, USA.
David J Gordon10 National Heart, Lung and Blood Institute (NHLBI), NIH, Bethesda, MD, USA.
Kenneth W Mahaffey11 Stanford University School of Medicine, Stanford University, Stanford, CA, USA.
Charles H Hennekens12 Florida Atlantic University, Boca Raton, FL, USA.
James D Neaton13 University of Minnesota, Minneapolis, MN, USA.
Gail D Pearson10 National Heart, Lung and Blood Institute (NHLBI), NIH, Bethesda, MD, USA.
Tomas Lg Andersson14 AstraZeneca, Mölndal, Sweden.
Marc A Pfeffer15 Brigham and Women's Hospital, Boston, MA, USA.
Susan S Ellenberg16 University of Pennsylvania, Philadelphia, PA, USA.
Duke Medical Center · USNational Heart Lung and Blood Institute · USAstraZeneca (Sweden) · SEBrigham and Women's Hospital · USClinical Research Institute · USEunice Kennedy Shriver National Institute of Child Health and Human Development · USFlorida Atlantic University · USMerck & Co., Inc., Rahway, NJ, USA (United States) · USStanford University · USStatistics Collaborative · USTufts Medical Center · USUniversity of Minnesota · USUniversity of Pennsylvania · USUniversity of Pittsburgh · USUniversity of Washington · USUniversity of Wisconsin–Madison · US

Funding

STATISTICAL ISSUES IN AIDS RESEARCHR37AI029168 · NIAID · UNIVERSITY OF WASHINGTON · PI JAMES P HUGHES · 1999 to 2026
$9.8M
Postdoctoral Training in Cardiovascular Clinical ResearchT32HL069749 · NHLBI · DUKE UNIVERSITY · PI MARK, DANIEL B · 2003 to 2023
$6.7M
STATISTICAL ISSUES IN AIDS RESEARCHR01AI029168 · NIAID · UNIVERSITY OF WASHINGTON · PI HUGHES, JAMES P · 1989 to 2018
$5.3M
NHLBI NIH HHS L30 HL124592NHLBI NIH HHS T32 HL069749NIAID NIH HHS R37 AI029168Wellcome Trust
6 · The paper itself

Abstract

background and purposeData monitoring committees are responsible for safeguarding the interests of study participants and assuring the integrity and credibility of clinical trials. The independence of data monitoring committees from sponsors and investigators is essential in achieving this mission. Creative approaches are needed to address ongoing and emerging challenges that potentially threaten data monitoring committees' independence and effectiveness.

methodsAn expert panel of representatives from academia, industry and government sponsors, and regulatory agencies discussed these challenges and proposed best practices and operating principles for effective functioning of contemporary data monitoring committees. RESULTS AND

conclusionsProspective data monitoring committee members need better training. Options could include didactic instruction as well as apprenticeships to provide real-world experience. Data monitoring committee members should be protected against legal liability arising from their service. While avoiding breaches in confidentiality of interim data remains a high priority, data monitoring committees should have access to unblinded efficacy and safety data throughout the trial to enable informed judgments about risks and benefits. Because overly rigid procedures can compromise their independence, data monitoring committees should have the flexibility necessary to best fulfill their responsibilities. Data monitoring committee charters should articulate principles that guide the data monitoring committee process rather than list a rigid set of requirements. Data monitoring committees should develop their recommendations by consensus rather than through voting processes. The format for the meetings of the data monitoring committee should maintain the committee's independence and clearly establish the leadership of the data monitoring committee chair. The independent statistical group at the Statistical Data Analysis Center should have sufficient depth of knowledge about the study at hand and experience with trials in general to ensure that the data monitoring committee has access to timely, reliable, and readily interpretable insights about emerging evidence in the clinical trial. Contracts engaging data monitoring committee members for industry-sponsored trials should have language customized to the unique responsibilities of data monitoring committee members rather than use language appropriate to consultants for product development. Regulatory scientists would benefit from experiencing data monitoring committee service that does not conflict with their regulatory responsibilities.

Indexed as

Clinical Trials Data Monitoring CommitteesPractice Guidelines as TopicConfidentialityHumansInsuranceapprenticeshipcharterconfidentialityexperienceindemnificationIndependenceoperating principlesregulatorytraining

Identifiers

PMID28359194
PMCPMC5380168
OpenAlexW2592372544

What OpenQuestion holds

Textmetadata
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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.