Supporting evidence in phase 2 cancer trial protocols: a content analysis
Bibliographic record
Abstract
BACKGROUND: Phase 2 trials are instrumental for designing definitive efficacy trials or attaining accelerated approval. However, high attrition of drug candidates in phase 2 trials raises questions about their supporting evidence. METHODS: We developed a typology of supporting evidence for phase 2 cancer trials. We also devised a scheme for capturing elements that enable an assessment of the strength of such evidence. Using this framework, we content analyzed supporting evidence provided in protocols of 50 randomly sampled phase 2 cancer monotherapy trials between January 2014 and January 2019, available on ClinicalTrials.gov. RESULTS: Of the 50 protocols in our sample, 52% were industry funded. Most invoked supporting evidence deriving from trials against different cancers (n = 28, 56%) or preclinical studies (n = 48, 96%) but not from clinical studies involving the target drug-indication pairing (n = 23, 46%). When presenting evidence from models, only 1 (2%) protocol explained its translational relevance. Instead, protocols implied translatability by describing molecular (86%) and pathophysiological (84%) processes shared by model and target systems. Protocols often provided information for assessing the magnitude, precision, and risk of bias for supporting trials (n = 43; 93%, 91%, 47%, respectively). However, such information was often unavailable for preclinical studies (n = 49; 53%, 22%, 59%, respectively). CONCLUSIONS: Supporting evidence is key to justifying the commitment of scientific resources and patients to a clinical hypothesis. Protocols often omit elements that would enable critical assessment of supporting evidence for phase 2 monotherapy cancer trials. These gaps suggest the promise of more structured approaches for presenting supporting evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.577 | 0.855 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.065 | 0.051 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.006 | 0.007 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".