Evidence map›Paper›PMID 33535821›Full record

ArticleClinical trials (London, England)2021

Clinical trials in a COVID-19 pandemic: Shared infrastructure for continuous learning in a rapidly changing landscape.

Haley Hedlin, Ariadna Garcia, Yingjie Weng, Ziyuan He, Vandana Sundaram, Bryan Bunning, Vidhya Balasubramanian, Kristen Cunanan, Kristopher Kapphahn, Santosh Gummidipundi and 3 more

Abstract read
In one paragraph

Article in Clinical trials (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
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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

13 authors.

Haley HedlinQuantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Ariadna GarciaQuantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Yingjie WengQuantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Ziyuan HeSean N. Parker Center for Allergy and Asthma Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Vandana SundaramQuantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Bryan BunningQuantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.ORCID 0000-0003-3092-0038
Vidhya BalasubramanianQuantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Kristen CunananQuantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Kristopher KapphahnQuantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Santosh GummidipundiQuantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Natasha PuringtonQuantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Mary BoulosQuantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Manisha DesaiQuantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.ORCID 0000-0002-6949-2651

Funding

Translational Oncology Research Program (Project-005)P30CA124435 · NCI · STANFORD UNIVERSITY · PI Melinda L. Telli · 2007 to 2026
$71.4M
Stanford Center for Clinical & Translational Education and Research (Spectrum)UL1TR003142 · NCATS · STANFORD UNIVERSITY · PI O'HARA, RUTH M · 2019 to 2023
$45.0M
Spectrum Stanford Center for clinical and Translational Research and EducationUL1TR001085 · NCATS · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD, GREENBERG, HARRY BERNARD · 2013 to 2017
$37.1M
Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
2/1 Arrest Respiratory Failure due to Pneumonia (ARREST PNEUMONIA)U24HL141727 · NHLBI · STANFORD UNIVERSITY · PI DESAI, MANISHA · 2019 to 2023
$1.8M
NCATS NIH HHS UL1 TR001085NCATS NIH HHS UL1 TR003142NCI NIH HHS P30 CA124435NHLBI NIH HHS U24 HL141727NIDDK NIH HHS P30 DK116074
6 · The paper itself

Abstract

backgroundClinical trials, conducted efficiently and with the utmost integrity, are a key component in identifying effective vaccines, therapies, and other interventions urgently needed to solve the COVID-19 crisis. Yet launching and implementing trials with the rigor necessary to produce convincing results is a complicated and time-consuming process. Balancing rigor and efficiency involves relying on designs that employ flexible features to respond to a fast-changing landscape, measuring valid endpoints that result in translational actions and disseminating findings in a timely manner. We describe the challenges involved in creating infrastructure with potential utility for shared learning.

methodsWe have established a shared infrastructure that borrows strength across multiple trials. The infrastructure includes an endpoint registry to aid in selecting appropriate endpoints, a registry to facilitate establishing a Data & Safety Monitoring Board, common data collection instruments, a COVID-19 dedicated design and analysis team, and a pragmatic platform protocol, among other elements.

resultsThe authors have relied on the shared infrastructure for six clinical trials for which they serve as the Data Coordinating Center and have a design and analysis team comprising 15 members who are dedicated to COVID-19. The authors established a pragmatic platform to simultaneously investigate multiple treatments for the outpatient with adaptive features to add or drop treatment arms.

conclusionThe shared infrastructure provides appealing opportunities to evaluate disease in a more robust manner with fewer resources and is especially valued during a pandemic where efficiency in time and resources is crucial. The most important element of the shared infrastructure is the pragmatic platform. While it may be the most challenging of the elements to establish, it may provide the greatest benefit to both patients and researchers.

Indexed as

PandemicsClinical Trial Protocols as TopicClinical Trials as TopicClinical Trials Data Monitoring CommitteesCOVID-19Endpoint DeterminationHumansSARS-CoV-2core protocolCOVID-19Data Coordinating CenterData & Safety Monitoring Boardefficient infrastructuremaster protocolpandemicPlatform protocol

Identifiers

PMID33535821
PMCPMC8172421

What OpenQuestion holds

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LicenceCC BY
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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.