Development of a protocol complexity tool: a framework designed to stimulate discussion and simplify study design
Bibliographic record
Abstract
BACKGROUND: Over the last decade, the pharmaceutical industry has witnessed longer, more complex, and expensive clinical trials. This complexity contributes to delays in clinical trial implementation, execution, monitoring, recruitment, data cleaning, and interpretation. Our aim was to develop a protocol complexity tool (PCT) to simplify clinical trial execution without compromising science or quality. METHODS: Using a collaborative design process, a taskforce comprising 20 cross-functional experts in clinical trial design and execution developed a PCT, between June 2021 and December 2022 and comprising 26 questions across 5 domains (operational execution, regulatory oversight, patient burden, site burden and study design). Individual domain scores and total complexity score (TCS) were calculated, and agreed by consensus, for 16 pre-identified phase II-IV difficult clinical trials across 3 therapeutic areas. Change in score was assessed post-PCT pass through. The relationship between TCS and key trial indicators (i.e. time-to-site activation and participant enrolment) was assessed for 26 studies by correlation analysis. RESULTS: Post-PCT pass through, the TCS was reduced in 12 trials (75%), remained the same in 3 trials (18.8%) and increased in 1 trial (6.2%). Complexity was most notably decreased in the operational execution and site burden domains, decreasing in 50% and 43.8% of assessed trials, respectively. Time-to-site activation and participant enrolment positively correlated with TCS, reaching statistical significance at 75% site activation (rho = 0.61; p = 0.005; n = 19) and 25% participant recruitment (rho = 0.59; p = 0.012; n = 17). CONCLUSIONS: We have developed a Protocol Complexity Tool to objectively measure the complexity of a study protocol consistently and transparently. The PCT is capable of driving simplification, enhancing collaboration, and creating additional confidence in trial designs. In the future, we envision the tool will support earlier discussions to develop protocols that are simpler to execute and more cost-effective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.173 | 0.897 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".