Clinical Trials for New Drug Development: Optimal Investment and Application
Bibliographic record
Abstract
Phase III clinical trials are expensive and require enrolling and treating hundreds or thousands of patients at many sites. The time and cost required to do so are uncertain, as is the economic value of the drug upon completion. We consider the problem of determining when and how many test sites should be opened and the rate at which patients should be recruited. We model the problem as a discrete time, discounted dynamic program with the objective of maximizing the expected net present value of a drug based on the costs of conducting the trial and on the drug’s quality-moderated likelihood of approval and its subsequent expected revenue stream if approved. We show the optimal policy is characterized by a series of thresholds on the number of patients enrolled over time that indicate when additional test centers should be opened and how many patients should be targeted. We demonstrate using data from completed clinical trials that for low- to moderate-valued drugs, these thresholds are relevant to the firm’s decisions. We extend the problem to the case with multiple interim analyses and demonstrate that optimizing the clinical trial capacity and its utilization provides significant value in addition to the option value of stopping the trial early. The e-companion is available at https://doi.org/10.1287/msom.2017.0616 .
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".