ArticleJournal of medical Internet research2021
Clinical Trial Data Sharing for COVID-19-Related Research.
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.
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.
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.
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
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Improving Participant Recruitment in Clinical Trials: Comparative Analysis of Innovative Digital Platforms.Journal of medical Internet research · 2024Pooled it
- Individual patient data meta-analysis and systematic review evaluating camostat mesilate to treat COVID-19.Journal of clinical and translational science · 2026Article
- Practice of data sharing plans in clinical trial registrations and concordance between registered and published data sharing plans: a cross-sectional study.BMC medicine · 2025Article
- The Impact of the Burden of COVID-19 Regulatory Reporting in a Small Independent Hospital and a Large Network Hospital: Comparative Mixed Methods Study.Online journal of public health informatics · 2025Article
- Now Is the Time to Strengthen Government-Academic Data Infrastructures to Jump-Start Future Public Health Crisis Response.JMIR public health and surveillance · 2024Article
- A Scalable Pseudonymization Tool for Rapid Deployment in Large Biomedical Research Networks: Development and Evaluation Study.JMIR medical informatics · 2024Article
- ISO/TS 21564:2019- based Evaluation of a Semantic Map between Variables in the ISARIC Freestanding Follow Up Survey and ORCHESTRA Studies.Journal of medical systems · 2023Article
- Industry Involvement and Transparency in the Most Cited Clinical Trials, 2019-2022.JAMA network open · 2023Review
- Totality of evidence of the effectiveness of repurposed therapies for COVID-19: Can we use real-world studies alongside randomized controlled trials?Clinical and translational science · 2023Article
- Global Trend on Machine Learning inGlobal health, epidemiology and genomics · 2023Article
- Four points regarding reproducibility and external statistical validity.Journal of evidence-based medicine · 2022Article
- Data capture and sharing in the COVID-19 pandemic: a cause for concern.The Lancet. Digital health · 2022Review
- Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method.JMIR public health and surveillance · 2022Article
- An Electronic Data Capture Tool for Data Collection During Public Health Emergencies: Development and Usability Study.JMIR human factors · 2022Article
- Smartphone-Enabled versus Conventional Otoscopy in Detecting Middle Ear Disease: A Meta-Analysis.Diagnostics (Basel, Switzerland) · 2022Article
- Data flow within global clinical trials: a scoping review.BMJ global health · 2022Article
- A Comprehensive COVID-19 Daily News and Medical Literature Briefing to Inform Health Care and Policy in New Mexico: Implementation Study.JMIR medical education · 2022Article
- Implementation of platform trials in the COVID-19 pandemic: A rapid review.Contemporary clinical trials · 2022Review
- Transparency of COVID-19-Related Research in Dental Journals.Frontiers in oral health · 2022Article
- Health science research barriers and facilitators in the midst of the COVID-19 crisis: scoping reviewRevista colombiana de obstetricia y ginecologia · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
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.