Continued investigator engagement: Reasons principal investigators conduct multiple FDA-regulated drug trials
Bibliographic record
Abstract
BACKGROUND/AIMS: Numerous reasons have been identified for why U.S.-based principal investigators choose to not continue participating in FDA-regulated trials. However, unexplored are reasons why a substantial number of principal investigators, facing the same challenges, remain engaged in clinical research. This study aimed to both describe barriers and identify factors that contribute to active investigators' success in conducting multiple FDA-regulated trials. METHODS: We conducted qualitative in-depth interviews (IDIs) with "active" multi-trial investigators. Interviews focused on investigators' experiences with FDA-regulated drug trials, challenges faced, and factors contributing to success. Investigators also reflected on previously identified barriers and shared advice for new investigators. Narratives were analyzed using applied thematic analysis. RESULTS: We interviewed 23 experienced investigators, representing a variety of backgrounds. Most reported that demonstrated ability to conduct a trial led to being approached again by sponsors. Investigators cited infrastructure, staff support, advance planning, and personal qualities as key factors in successfully conducting multiple trials. Nearly all cited difficulties related to trial finances. Three-quarters pointed to challenges with patient recruitment; others described challenges related to data and safety reporting and to the time that trial implementation takes away from other activities. Aspiring investigators were advised to engage in research-specific training and seek out mentorship opportunities. CONCLUSION: Investigators in our sample faced many of the same challenges identified in previous research, yet they had evolved strategies to overcome them. The amount and type of support to which investigators have access may represent a crucial difference between "active" investigators and principal investigators who leave FDA-regulated 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 | 0.168 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".