Relative risk reduction: Misinformative measure in clinical trials and COVID-19 vaccine efficacy
Bibliographic record
Abstract
Treatment and vaccine efficacy in clinical trials are often reported in the media and medical journals as the relative risk reduction. The present article explains why the relative risk reduction is a misinformative measure that promotes disinformation when reporting efficacy in clinical research studies such as randomized controlled trials for COVID-19 vaccines. The relative risk reduction is based on the relative risk, a proportional measure or ratio used in epidemiologic studies to estimate the probability of a disease associated with an exposure. The present article demonstrates how the relative risk reduction and relative risk obscure the magnitude of disease risk reduction in clinical research. The absolute risk reduction is shown to be a more precise and reliable measure of treatment and vaccine efficacy in clinical research studies. The absolute risk reduction reciprocal also measures the number needed to treat or vaccinate, and is a more accurate measure than the relative risk reduction for comparing risk reductions of clinical studies. Additionally, the present article reviews consequences of COVID-19 vaccine efficacy misinformation disseminated through media reports. The article concludes that relative risk reduction should not be used to measure treatment and vaccine efficacy in clinical trials. What is new?: •Unreliability of relative measures in clinical trials is graphically illustrated, demonstrating constant relative measures as absolute measures change.•Misuse of relative measures in clinical research is historically linked to misinterpretation of Jerome Cornfield's advice on measuring causative and associative effects.•Consequences of disinformation and misinformation related to COVID-19 vaccine efficacy and modern clinical medicine are described.•The proper use of absolute measures in meta-analyses is explained.
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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 | 0.313 | 0.604 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".