ArticleCommunications medicine2025
Systematic data capture reduces the need for source data verification: exploratory analysis from a phase 2 multicenter randomized controlled platform trial.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT04488081 (I-SPY COVID TRIAL), which is not on this map. Not yet cited in PubMed.
What it found
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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.
I-SPY COVID TRIAL: An Adaptive Platform Trial to Reduce Mortality and Ventilator Requirements for Critically Ill Patients
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Erratum issued
Authors and funding
14 authors.
Funding
Abstract
backgroundThe COVID-19 pandemic gave rise to clinical trials focused on systematic, accurate primary data capture, and reduced reliance on source data verification (SDV). Here, we report on a natural experiment that allowed us to assess the quality, cost, and impact of this approach compared to traditional SDV.
methodsThe I-SPY COVID trial (NCT04488081) was a multicenter, open-label, platform trial that employed a streamlined daily checklist, daily capture of labs and medications, and centralized monitoring to ensure accurate data collection in lieu of SDV. The trial enrolled 1,111 patients in 11 drug arms with severe COVID-19. After the trial arms were closed, extensive retrospective SDV was performed on 333 (30.0%) patients, including 10,101 of 44,486 (23%) electronic case report forms (eCRFs), allowing us to evaluate the impact of our strategy on data integrity, outcomes, and costs.
resultsWe find that retrospective SDV results in changes to 0.36% (1,234 / 340,532) of data fields. It results in no changes to the type of outcome recorded (death, recovery, or censored), but changes in the day of recovery in 9 instances, by a median of 2 days (range 1-7). Two additional AEs are added during SDV that had not previously been captured. Costs associated with retrospective SDV of 23% eCRFs are 61,073 person-hours at a cost of $6.1 M.
conclusionsExtensive SDV does not change any results or conclusions of the I-SPY COVID trial, which was designed with a systematic strategy for data capture, monitoring, and safety. This strategy could improve the efficiency of clinical trials and eliminate the need for manual SDV.
Identifiers
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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.