Optimal co-development contracts for companion diagnostics
Bibliographic record
Abstract
The market for companion diagnostics is expected to be a US$10.07 billion by 2026. Companion diagnostics have the potential to make expensive drugs cost-effective by identifying patients who would benefit from them. We consider the contract design problem between a pharmaceutical company which owns a drug that is effective for a particular subset of the patient population and a biotech company which owns some technology that could facilitate the development of a companion diagnostic. We obtain theoretical and practical results. We determine when both parties enter such a contract and fully characterize the optimal solutions in closed-form. We find sufficient conditions under which the optimal contract exhibits a particular structure. We show that the first-best can be achieved in some cases and identify sufficient conditions under which the biotech company would not work alone but participates in the project with the pharmaceutical company’s subsidy. We find that heuristics based on practical preferences could be costly to the pharmaceutical company and hence the principal should use the second-best solution; and contract type depends heavily on the biotech company’s workforce level, unit cost of workforce and information level.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".