Data from Development, Review, and Activation of Thoracic Oncology Investigator-Initiated Trials
Bibliographic record
Abstract
AbstractPurpose: Investigator-initiated trials (IIT) may address important biological and clinical questions that may not be prioritized by pharmaceutical sponsors. However, little is known about the process by which IIT proposals are evaluated and activated. Experimental Design: We performed a retrospective study of IIT concepts submitted through the Academic Thoracic Oncology Medical Investigators Consortium, which comprises 13 institutions in the United States and Canada, from consortium inception in 2014 to 2024. We compared approved and disapproved concepts using χ2 tests, Fisher exact tests, and Wilcoxon rank-sum tests. Results: Among 68 presented IIT concepts, 60 (88%) received consortium approval a median of 30 days (IQR, 31–59 days) after submission. Concepts submitted by junior faculty were more likely to be approved than those from full professors (P = 0.003). Of the 60 concepts subsequently submitted to pharmaceutical sponsors, 15 (25%) were approved, 43 (72%) were disapproved, and 2 (3%) remain under review. The median time between concept submission to a sponsor and the sponsor’s decision was 61 days (IQR, 31–183 days). Concepts with shorter projected durations were more likely to be approved by the pharmaceutical sponsor (P = 0.05). For sponsor-approved IIT concepts, the median overall time from initial submission to trial activation was 18 months. Conclusions: Only a small proportion of proposed investigator-initiated cancer clinical trials are successfully activated following a prolonged development process. Given the importance of IITs in addressing real-world, practical questions and the growing professional challenges facing clinical research physician faculty, further attention to IIT development facilitators and barriers is warranted.
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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 | 0.131 | 0.471 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".