Sensible approaches for reducing clinical trial costs
Bibliographic record
Abstract
BACKGROUND: Over the past decade, annual funding for biomedical research has more than doubled while new molecular entity approvals have declined by one third. OBJECTIVE: To assess the value of practices commonly employed in the conduct of large-scale clinical trials, and to identify areas where costs could be reduced without compromising scientific validity. METHODS: In the qualitative phase of the study, an expert panel recommended potential modifications of mega-trial designs and operations in order to maximize their value (cost versus scientific benefit tradeoff). In the quantitative phase, a mega-trial economic model was used to assess the financial implications of these recommendations. Our initial chronic disease trial design included 20,000 patients randomized at 1000 sites. Each site was assigned 24 monitoring visits and a $10,000 per patient site payment. The case report form (CRF) was 60 pages long, and trial duration was assumed to be 48 months. RESULTS: The total costs of the initial trial design were $421 million ($US 2007). Following the expert panel's recommendations, we varied study duration, CRF length, number of sites, electronic data capture (EDC), and site management components to determine their individual and combined effects upon total trial costs. The use of EDC and modified site management practices were associated with significant reductions in total trial costs. When reductions in all five trial components were combined in a streamlined pharmaceutical industry design, a 59% reduction in total trial costs resulted. When we assumed an even more streamlined trial design than has typically been considered for regulatory submissions in the past, there was a 90% reduction in total trial costs. CONCLUSION: Our results suggest that it is possible to reduce substantially the cost of large-scale clinical trials without compromising the scientific validity of their results. If implemented, our recommendations could free billions of dollars annually for additional clinical studies. Research in the setting of clinical trials should be conducted to refine these findings.
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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 | 0.422 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".