There is no upper limit on the maximum effect that can be detected in randomized trials
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Randomized controlled trials (RCTs) are commonly viewed as essential for detecting small treatment benefits, yet they also identify large ("dramatic") effects. Forecasting the likelihood of future large effects helps guide resource allocation for conducting clinical trials. METHODS: We included consecutive cancer RCTs from 5 cohorts identified by funders or trial registries, minimizing publication bias. Between 1955 and 2018 (and published by 2022), 716 RCTs compared 984 experimental vs standard treatments in approximately 350,000 patients. We applied a generalized Pareto distribution (GPD) under Extreme Value Theory to predict future maximum treatment effects using data spanning 65 years. RESULTS: The GPD's positive shape parameter implies no upper limit on maximum treatment effects. Historically, the largest observed effect had an odds ratio (OR) of 45 (95% CI: 2-1008). If current patterns hold, the largest effect over the next 50 years is projected at OR = 23 (95% CI: 13-106). We estimated 20% probability of detecting new treatments with OR >50 within the same time frame. Increasing the number of RCTs from about 20 to 40 or 60 per year would double or triple the likelihood of detecting breakthrough treatments with dramatic effects. CONCLUSION: Our findings suggest there may be no absolute upper bound on discoverable treatment effects in cancer RCTs, although estimates will likely remain in the range observed between 1955 and 2022. Conducting more RCTs would boost the probability of identifying treatments with large effects, underscoring the importance of sustained or expanded trial activity to accelerate breakthrough discoveries. PLAIN LANGUAGE SUMMARY: New treatments cannot be discovered without individuals volunteering to participate in clinical studies. Among all types of human clinical studies, RCTs are considered the most reliable method for evaluating new medical treatments and are especially effective at detecting small beneficial effects. This study also demonstrates that conducting more RCTs would accelerate the discovery of treatments with both small and large effects. By increasing public participation in RCTs, we can drive faster advances in therapeutics and influence policy decisions to allocate more resources toward the conduct of these trials.
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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 | 0.376 | 0.718 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.026 | 0.013 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.022 | 0.031 |
| Insufficient payload (model declined to judge) | 0.009 | 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".