Clinical Trials in COVID-19 Management & Prevention: A Meta-epidemiological Study examining methodological quality
Bibliographic record
Abstract
ABSTRACT Background The coronavirus disease (Covid-19) pandemic has produced a large number of clinical trial reports with unprecedented rapidity, raising concerns about methodological quality and potential for research waste. Objectives To describe the characteristics of randomized clinical trials (RCTs) investigating prophylaxis or treatment of Covid-19 infection and examine the effect of trial characteristics on whether the study reported a statistically significant effect on the primary outcome(s). Study Design Meta-epidemiological study of Covid-19 treatment and prophylaxis RCTs. Eligibility criteria English-language RCTs (peer-reviewed or preprint) that evaluated pharmacologic agents or blood products compared to standard care, placebo, or an active comparator among participants with suspected or confirmed Covid-19 or at risk for Covid-19. We excluded trials of vaccines or traditional herbal medicines. Information sources We searched 25 databases in the US Centre for Disease Control Downloadable Database from January 1 to October 21, 2020. Trial appraisal and synthesis methods We extracted trial characteristics including number of centres, funding sources (industry versus non-industry), and sample size. We assessed risk of bias (RoB) using the modified Cochrane RoB 2.0 Tool. We used descriptive statistics to summarize trial characteristics and logistic regression to evaluate the association between RoB due to the randomization process, centre status (single vs. multicentre), funding source, and sample size, and statistically significant effect in the primary outcome. Results We included 91 RCTs (46,802 participants) evaluating Covid-19 therapeutic drugs (n = 76), blood products (n = 9) or prophylactic drugs (n = 6). Of these, 40 (44%) were single-centre, 23 (25.3%) enrolled < 50 patients, and 28 (30.8%) received industry funding. RoB varied across trials, with high or probably high overall RoB in 75 (82.4%) trials, most frequently due to deviations from the intended protocol (including blinding) and randomization processes. Thirty-eight trials (41.8%) found a statistically significant effect in the primary outcome. RoB due randomization (odds ratio [OR] 3.77, 95% confidence interval [CI], 1.47 to 9.72) and single centre trials (OR 3.15, 95% CI, 1.25 to 7.97) were associated with higher likelihood of finding a statistically significant effect. Conclusions There was high variability in RoB amongst Covid-19 trials. RoB attributed to the randomization process and single centre status were associated with a three-fold increase in the odds of finding a statistically significant effect. Researchers, funders, and knowledge users should remain cognizant of the impact of study characteristics, including RoB, on trial results when designing, conducting, and appraising Covid-19 trials.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.398 | 0.898 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".