Prevalence and Impact of Bypassing or Overriding Phase 2 Trials in Neurologic Drug Development
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Pivotal trials for neurologic drugs in clinical development are often initiated without a phase 2 trial ("bypass") or despite a negative phase 2 efficacy result ("override"). Such practices may degrade the risk/benefit ratio of phase 3 trials. The aim of this study is to estimate the proportion of phase 3 trials for 10 neurologic diseases started without a positive phase 2 trial, to identify factors associated with this practice, and to investigate any association with unfavorable phase 3 trial outcomes. METHODS: We searched ClinicalTrials.gov for phase 3 trials completed during 2011-2021, with at least 1 research site in the United States, Canada, the European Union, the United Kingdom, or Australia, and investigating drugs or biologics for treatment of 10 neurologic conditions. Our primary objective was to assess the prevalence of phase 2 bypass/override by searching for preceding phase 2 trials. We used Fisher exact tests to determine whether phase 3 trial characteristics and trial results were associated with phase 2 bypass/override. RESULTS: = 0.65). DISCUSSION: Almost half of the neurologic disease phase 3 trials were initiated without the support of a positive phase 2 trial. Although our analysis does not establish harm with bypass/override, its prevalence and the scientific rationale for phase 2 trial testing favor development of criteria defining when phase 2 bypass/override is justified.
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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 | 0.324 | 0.568 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".