Mega-Trials in Heart Failure: Effects of Dilution in Examination of New Therapies
Bibliographic record
Abstract
AIMS: Over the last 30 years, many medicine development programmes in acute and chronic heart failure (HF) with preserved ejection fraction (HFpEF) have failed, in contrast to those in HF with reduced ejection fraction (HFrEF). We explore how the neutral results in larger HF trials may be attributable to chance and/or the dilution of statistical power. METHODS AND RESULTS: Using simulations, we examined the probability that a positive finding in a Phase 2 trial would result in the study of a truly effective medicine in a Phase 3 trial. We assessed the similarity of clinical trial and registry patient populations. We conducted a meta-analysis of paired Phase 2 and 3 trials in HFrEF and acute HF examining the associations of trial phase and size with placebo event rates and treatment effects for HF events and death. We estimated loss in trial power attributable to dilution with increasing trial size. Appropriately powered Phase 3 trials should have yielded ∼35% positive results. Patient populations in Phase 3 trials are similar to those in Phase 2 trials but both differ substantially from the populations of 'real-life' registries. We observed decreasing placebo event rates and smaller treatment effects with increasing trial size, especially for HF events (and less so for mortality). This was more pronounced in trials in acute HF patients. CONCLUSIONS: The selection of more positive Phase 2 trials for further development does not explain the failure of HFpEF and acute HF medicine development. Increasing sample size may lead to reduced event rates and smaller treatment effects, resulting in a high rate of neutral Phase 3 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 | 0.683 | 0.832 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".