Evidence map›Paper›PMID 34191981›Full record

ArticleStatistics in biopharmaceutical research2020

Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.

Nigel Stallard, Lisa Hampson, Norbert Benda, Werner Brannath, Thomas Burnett, Tim Friede, Peter K Kimani, Franz Koenig, Johannes Krisam, Pavel Mozgunov and 7 more

Abstract read
In one paragraph

Article in Statistics in biopharmaceutical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 2 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

49 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  13. Targeting the SARS-CoV-2 reservoir in long COVID.The Lancet. Infectious diseases · 2025
    Review
  14. Article
  15. Article
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  17. The Effect of Pomegranate Tea on the Clinical Outcomes and Symptom Alleviation in COVID-19 Patients: A Double-Blind Randomized Clinical Trial.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2025
    Article
  18. 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

17 authors.

Nigel StallardStatistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.
Lisa HampsonAdvanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland.
Norbert BendaThe Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany.
Werner BrannathInstitute for Statistics, University of Bremen, Bremen, Germany.
Thomas BurnettDepartment of Mathematics and Statistics, Lancaster University, Lancaster, UK.
Tim FriedeDepartment of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.ORCID https://orcid.org/0000-0001-5347-7441
Peter K KimaniStatistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.
Franz KoenigSection for Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria.ORCID https://orcid.org/0000-0002-6893-3304
Johannes KrisamInstitute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany.
Pavel MozgunovDepartment of Mathematics and Statistics, Lancaster University, Lancaster, UK.ORCID https://orcid.org/0000-0001-6810-0284
Martin PoschSection for Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria.ORCID https://orcid.org/0000-0001-8499-8573
James WasonPopulation Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.ORCID https://orcid.org/0000-0002-4691-126X
Gernot WassmerRPACT GbR, Sereetz, Germany.
John WhiteheadDepartment of Mathematics and Statistics, Lancaster University, Lancaster, UK.
S Faye WilliamsonDepartment of Mathematics and Statistics, Lancaster University, Lancaster, UK.
Sarah ZoharINSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France.
Thomas JakiDepartment of Mathematics and Statistics, Lancaster University, Lancaster, UK.ORCID https://orcid.org/0000-0002-1096-188X

Funding

Medical Research Council MC_UU_00002/14Medical Research Council MC_UU_00002/6Medical Research Council MR/N028171/1
6 · The paper itself

Abstract

The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.

Indexed as

Adaptive trialGroup sequential designMulti-arm multi-stagePandemic researchPlatform trialSARS-CoV-2

Identifiers

PMID34191981
PMCPMC8011600

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.