Evidence map›Paper›PMID 33684053›Full record

ArticleJournal of medical Internet research2021

Clinical Trial Data Sharing for COVID-19-Related Research.

Louis Dron, Alison Dillman, Michael J Zoratti, Jonas Haggstrom, Edward J Mills, Jay J H Park

Registry-linked trialAbstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04727424 (A Multicenter, Prospective, Adaptive, Double-blind, Randomized, Placebo-controlled Study to Evaluate the Effect of Fluvoxamine Plus Budesonide, Fluoxetine Plus Budesonide and Spirulin Platensis, in High Risk Patients With Mild COVID-19), which is not on this map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 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.

NCT04727424 phase3unknown statusnot on this map

A Multicenter, Prospective, Adaptive, Double-blind, Randomized, Placebo-controlled Study to Evaluate the Effect of Fluvoxamine Plus Budesonide, Fluoxetine Plus Budesonide and Spirulin Platensis, in High Risk Patients With Mild COVID-19

TypeinterventionalSponsorCardresearchRan2021 to 2024Enrolled7,819ConditionsCovid19, SARS-Associated CoronavirusArmsSpirulin Platensis, Budesonide Powder, Fluoxetine 20 MG, Placebo
3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  10. Global Trend on Machine Learning inGlobal health, epidemiology and genomics · 2023
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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

6 authors.

Louis DronCytel Canada Inc., Vancouver, BC, Canada.ORCID 0000-0002-3276-3948
Alison DillmanSchool of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom.ORCID 0000-0002-5061-2916
Michael J ZorattiCytel Canada Inc., Vancouver, BC, Canada.ORCID 0000-0003-3069-1159
Jonas HaggstromInternational COVID-19 Data Alliance, London, United Kingdom.ORCID 0000-0001-8304-9528
Edward J MillsCytel Canada Inc., Vancouver, BC, Canada.ORCID 0000-0003-3120-9694
Jay J H ParkDepartment of Experimental Medicine, University of British Columbia, Vancouver, BC, Canada.ORCID 0000-0001-5454-014X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper aims to provide a perspective on data sharing practices in the context of the COVID-19 pandemic. The scientific community has made several important inroads in the fight against COVID-19, and there are over 2500 clinical trials registered globally. Within the context of the rapidly changing pandemic, we are seeing a large number of trials conducted without results being made available. It is likely that a plethora of trials have stopped early, not for statistical reasons but due to lack of feasibility. Trials stopped early for feasibility are, by definition, statistically underpowered and thereby prone to inconclusive findings. Statistical power is not necessarily linear with the total sample size, and even small reductions in patient numbers or events can have a substantial impact on the research outcomes. Given the profusion of clinical trials investigating identical or similar treatments across different geographical and clinical contexts, one must also consider that the likelihood of a substantial number of false-positive and false-negative trials, emerging with the increasing overall number of trials, adds to public perceptions of uncertainty. This issue is complicated further by the evolving nature of the pandemic, wherein baseline assumptions on control group risk factors used to develop sample size calculations are far more challenging than those in the case of well-documented diseases. The standard answer to these challenges during nonpandemic settings is to assess each trial for statistical power and risk-of-bias and then pool the reported aggregated results using meta-analytic approaches. This solution simply will not suffice for COVID-19. Even with random-effects meta-analysis models, it will be difficult to adjust for the heterogeneity of different trials with aggregated reported data alone, especially given the absence of common data standards and outcome measures. To date, several groups have proposed structures and partnerships for data sharing. As COVID-19 has forced reconsideration of policies, processes, and interests, this is the time to advance scientific cooperation and shift the clinical research enterprise toward a data-sharing culture to maximize our response in the service of public health.

Indexed as

Clinical Trials as TopicCOVID-19Data ManagementHumansInformation DisseminationPandemicsResearch DesignSARS-CoV-2assessmentbiaschallengeclinical trialscooperationCOVID-19datadatasetdata-sharingerrorfeasibilityinteroperabilityinterventionprivacyrecruitmentregistryresearchsecurity

Identifiers

PMID33684053
PMCPMC7958972

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.