Impact of Allocation Concealment and Blinding in Trials Addressing Treatments for COVID-19: A Methods Study
Bibliographic record
Abstract
We aimed to assess the impact of allocation concealment and blinding on the results of coronavirus disease 2019 (COVID-19) trials, using the World Health Organization COVID-19 database (to February 2022). We identified 488 randomized trials comparing drug therapeutics with placebo or standard care in patients with COVID-19. We performed random-effects meta-regressions comparing the results of trials with and without allocation concealment and blinding of health-care providers and patients. We found that, compared with trials with allocation concealment, trials without allocation concealment may estimate treatments to be more beneficial for mortality, mechanical ventilation, hospital admission, duration of hospitalization, and duration of mechanical ventilation, but results were imprecise. We did not find compelling evidence that, compared with trials with blinding, trials without blinding produce consistently different results for mortality, mechanical ventilation, and duration of hospitalization. We found that trials without blinding may estimate treatments to be more beneficial for hospitalizations and duration of mechanical ventilation. We did not find compelling evidence that COVID-19 trials in which health-care providers and patients are blinded produce different results from trials without blinding, but trials without allocation concealment estimate treatments to be more beneficial compared with trials with allocation concealment. Our study suggests that lack of blinding may not always bias results but that evidence users should remain skeptical of trials without allocation concealment.
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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 | 0.471 | 0.692 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.035 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".