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Clinical trials in a COVID-19 pandemic: Shared infrastructure for continuous learning in a rapidly changing landscape.
Hedlin, Haley; Garcia, Ariadna; Weng, Yingjie; He, Ziyuan; Sundaram, Vandana; Bunning, Bryan; Balasubramanian, Vidhya; Cunanan, Kristen; Kapphahn, Kristopher; Gummidipundi, Santosh; Purington, Natasha; Boulos, Mary; Desai, Manisha.
  • Hedlin H; Quantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Garcia A; Quantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Weng Y; Quantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • He Z; Sean N. Parker Center for Allergy and Asthma Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Sundaram V; Quantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Bunning B; Quantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Balasubramanian V; Quantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Cunanan K; Quantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Kapphahn K; Quantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Gummidipundi S; Quantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Purington N; Quantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Boulos M; Quantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Desai M; Quantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Clin Trials ; 18(3): 324-334, 2021 06.
Article in English | MEDLINE | ID: covidwho-1063163
ABSTRACT

BACKGROUND:

Clinical 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.

METHODS:

We 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.

RESULTS:

The 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.

CONCLUSION:

The 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Clinical Trials as Topic / Pandemics / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Topics: Vaccines Limits: Humans Language: English Journal: Clin Trials Journal subject: Medicine / Therapeutics Year: 2021 Document Type: Article Affiliation country: 1740774520988298

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Clinical Trials as Topic / Pandemics / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Topics: Vaccines Limits: Humans Language: English Journal: Clin Trials Journal subject: Medicine / Therapeutics Year: 2021 Document Type: Article Affiliation country: 1740774520988298