Data Monitoring for the Chinese Clinical Trials of Remdesivir in Treating Patients with COVID-19 During the Pandemic Crisis
Bibliographic record
Abstract
Two phase-III, double-blind, randomized clinical trials of remdesivir plus SOC (standard of care) versus placebo plus SOC have been conducted in Wuhan hospitals by Chinese investigators during the urgent COVID-19 epidemic [ClincalTrials.gov NCT04257656 and NCT04252664]. These trials have been highly anticipated worldwide. We expect investigators of the trials will soon report the clinical and laboratory findings from the medical perspective. This manuscript provides documentary style information on the process of monitoring key data and making recommendations to the sponsor and investigators based on analytical insights when dealing with the emergent situation from the statistical viewpoint. Having monitored data sequentially from 237 patients, we comment on the strength and weakness of the study design and suggest the treatment effect of remdesivir on severe COVID-19 cases. Our experience with using the Dynamic Data Monitoring (DDM) tool has demonstrated its efficiency and reliability in supporting DSMB's instantaneous review of essential data during the emergent situation. DDM, when used properly by disciplined statisticians, has shown its capability of exploring the trial data flexibly and, in the meantime, protecting the trial's scientific integrity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.380 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".