Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Principal-Agent Hypothesis Testing (2205.06812v3)

Published 13 May 2022 in cs.GT, cs.LG, cs.MA, math.ST, stat.ME, and stat.TH

Abstract: Consider the relationship between a regulator (the principal) and an experimenter (the agent) such as a pharmaceutical company. The pharmaceutical company wishes to sell a drug for profit, whereas the regulator wishes to allow only efficacious drugs to be marketed. The efficacy of the drug is not known to the regulator, so the pharmaceutical company must run a costly trial to prove efficacy to the regulator. Critically, the statistical protocol used to establish efficacy affects the behavior of a strategic, self-interested agent; a lower standard of statistical evidence incentivizes the agent to run more trials that are less likely to be effective. The interaction between the statistical protocol and the incentives of the pharmaceutical company is crucial for understanding this system and designing protocols with high social utility. In this work, we discuss how the regulator can set up a protocol with payoffs based on statistical evidence. We show how to design protocols that are robust to an agent's strategic actions, and derive the optimal protocol in the presence of strategic entrants.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (43)
  1. Contracts with private cost per unit-of-effort. In Proc. 22nd ACM Conf. Econ. Comput., pp.  52–69. ACM.
  2. Combinatorial agency. In Proc. of the 7th ACM Conf. on Elec. Comm., pp.  18–28. ACM.
  3. Contract Theory. MIT Press.
  4. Carroll, G. (2015). Robustness and linear contracts. Am. Econ. Rev. 105(2), 536–63.
  5. Bayesian agency: Linear versus tractable contracts. Artif. Intell. 307.
  6. Selective trials: A principal-agent approach to randomized controlled experiments. Am. Econ. Rev. 102(4), 1279–1309.
  7. Persuasion bias in science: Can economics help? Econ. J. 127(605), F266–F304.
  8. Innovation in the pharmaceutical industry: new estimates of R&D costs. J. Health Econ. 47, 20–33.
  9. Simple versus optimal contracts. In Proc. 20th ACM Conf. Econ. Comput., pp.  369–387. ACM.
  10. The complexity of contracts. SIAM J. Comput. 50(1), 211–254.
  11. Biopharma’s biggest sellers – the oldies that just keep giving. URL https://www.evaluate.com/vantage/articles/insights/other-data/biopharmas-biggest-sellers-oldies-just-keep-giving.
  12. FDA (2019). Demonstrating substantial evidence of effectiveness for human drug and biological products draft guidance for industry. Fed. Reg.
  13. FDA Center for Drug Evaluation and Research (2020). Final summary minutes of the peripheral and central nervous system drugs advisory committee meeting. URL https://www.fda.gov/media/145690/download.
  14. Incentivizing exploration. In Proc. 15th ACM Conf. Econ. Comput., pp.  5–22. ACM.
  15. Safe testing. In 2020 Inf. Theory Appl. Workshop, pp.  1–54. IEEE.
  16. Contracts under moral hazard and adverse selection. In Proc. 22nd ACM Conf. Econ. Comput., pp.  563–582. ACM.
  17. The theory of contracts. In T. F. Bewley (Ed.), Advances in Economic Theory: Fifth World Congress. Cambridge: Cambridge Univ. Press.
  18. Confirmatory trials for drugs approved on a single trial. Circ. Cardiovasc. Qual. Outcomes 12(6), e005494.
  19. Adaptive contract design for crowdsourcing markets: Bandit algorithms for repeated principal-agent problems. J. Artif. Intell. Res. 55(1), 317–359.
  20. Designing Economic Mechanisms. Cambridge Univ. Press.
  21. Is the FDA too conservative or too aggressive?: A Bayesian decision analysis of clinical trial design. J. Econom. 211(1), 117–136.
  22. US Food and Drug Administration reasoning in approval decisions when efficacy evidence is borderline, 2013–2018. Ann. Intern. Med. 174(11), 1603–1611.
  23. Bayesian persuasion. Am. Econ. Rev. 101(6), 2590–2615.
  24. Ke, R. (2009). Essays on contract theory and organizational economics. Ph. D. thesis, Massachusetts Institute of Technology.
  25. The Theory of Incentives: The Principal-Agent Model. Princeton, NJ, USA: Princeton University Press.
  26. Love, J. (2019). The growing gap between what the public had been told R&D costs are, and the actual costs. URL https://www.keionline.org/30869.
  27. Min, D. (2023). Screening for experiments. Games Econom. Behav. 142, 73–100.
  28. Variation in the estimated costs of pivotal clinical benefit trials supporting the US approval of new therapeutic agents, 2015–2017: a cross-sectional study. BMJ open 10(6), e038863.
  29. Estimated costs of pivotal trials for novel therapeutic agents approved by the us food and drug administration, 2015-2016. JAMA Intern. Med. 178(11), 1451–1457.
  30. Characteristics of single pivotal trials supporting regulatory approvals of novel non-orphan, non-oncology drugs in the European Union and United States from 2012- 2016. Clin. Transl. Sci. 12(4), 361–370.
  31. Neyman, J. and E. S. Pearson (1933). On the problem of the most efficient tests of statistical hypotheses. Philos. Trans. Royal Soc. A 231(694-706), 289–337.
  32. Pub. L. (1997). Food and Drug Modernization Act. No. 105-115, 111 URL https://www.govinfo.gov/content/pkg/PLAW-105publ115/html/PLAW-105publ115.htm.
  33. Game-theoretic statistics and safe anytime-valid inference. Statist. Sci. 38(4), 576–601.
  34. Salanie, B. (2005). The Economics of Contracts: A Primer, 2nd Edition. MIT Press.
  35. How much does it cost to research and develop a new drug? A systematic review and assessment. PharmacoEconomics 39(11), 1243–1269.
  36. Schorfheide, F. and K. I. Wolpin (2012). On the use of holdout samples for model selection. Am. Econ. Rev. 102(3), 477–81.
  37. Schorfheide, F. and K. I. Wolpin (2016). To hold out or not to hold out. Res. Econ. 70(2), 332–345.
  38. Shafer, G. (2021). Testing by betting: a strategy for statistical and scientific communication. J. Roy. Statist. Soc. Ser. A 184(2), 407–478.
  39. Spiess, J. (2018). Optimal estimation when researcher and social preferences are misaligned. Working paper.
  40. Tetenov, A. (2016). An economic theory of statistical testing. CeMMAP working paper.
  41. (When) should you adjust inferences for multiple hypothesis testing? arXiv preprint arXiv:2104.13367.
  42. E-values: calibration, combination and applications. Ann. Statist. 49(3), 1736–1754.
  43. Estimated research and development investment needed to bring a new medicine to market, 2009-2018. JAMA 323(9), 844–853.
Citations (13)
View on Semantic Scholar

Summary

  • The paper demonstrates incentive-aligned contracts using e-values to discourage ineffective drug trials by ensuring agents face losses when pursuing lower efficacy treatments.
  • It employs dynamic programming to establish maximin optimal protocols applicable in both single-round and sequential clinical trial settings.
  • The research offers actionable insights for regulators by aligning statistical evidence with economic incentives to enhance the drug approval process.

Principal-Agent Hypothesis Testing

The paper "Principal-Agent Hypothesis Testing" by Bates, Jordan, Sklar, and Soloff provides a nuanced examination of the interactions between a regulator (the principal) and an experimenter (the agent), with an application to pharmaceutical clinical trials. This exploration is rooted in understanding how statistical protocols shape the incentives of self-interested agents. The authors develop a decision-theoretic framework that accounts for the strategic behavior of agents in proving the efficacy of new treatments.

Problem Setting

The paper addresses a scenario where a pharmaceutical company (agent) needs to prove a drug's efficacy to a medical regulator (principal) like the FDA. The agent incurs high costs to run trials for potential high-reward treatments, while the principal aims to ensure that only genuinely effective drugs reach the market. The challenge is that looser standards for statistical evidence may incentivize the agent to conduct trials for drugs with lower chances of efficacy, leading to poor social outcomes.

Theoretical Contributions

The core contribution of the paper is the design of statistical protocols or contracts that are robust to the strategic actions of agents. These statistical contracts are designed to be incentive-aligned, meaning an agent with a known ineffective product would not find it profitable to run trials. The authors employ concepts from contract theory and mechanism design, particularly focusing on e-values, a form of evidence that is robust to sequential betting interpretations and aligns well with the economic incentives of agents.

Key Findings

  1. Incentive-Aligned Contracts: A statistical contract is incentive-aligned if it employs e-values such that the expected net profit of any null agent (one with an ineffective drug) is less than or equal to their total investment. This deters agents from bluffing the regulator with ineffective drugs.
  2. Maximin Optimality: Incentive-aligned statistical contracts are found to be maximin optimal. This means these contracts yield the highest utility for the principal in the worst-case scenario concerning the distribution of agent types.
  3. Single-Round and Multi-Round Settings: The theoretical framework extends from single-round (one trial) to multi-round (sequential trials) settings, incorporating adaptive decision-making where the agent can choose to continue or abandon trials at each stage. The authors characterize the optimal protocols in these dynamic scenarios using dynamic programming techniques.
  4. FDA Analysis: The paper contextualizes the theoretical findings with an analysis of FDA approval protocols. It evaluates different statistical standards against the backdrop of varying drug market values and trial costs, highlighting instances where current FDA standards may or may not be incentive-aligned.

Practical Implications

The implications of this research are broad and significant:

  • Policy Design: Regulators designing approval pathways for new drugs can use these findings to set evidence thresholds that maximize social welfare while deterring ineffective drug submissions.
  • Sequential Trials: The adaptability of the framework to multi-round scenarios allows for practical applications in real-world settings where data accumulation and decision points are sequential.
  • Economic Mechanisms in Statistical Inference: By integrating economic theory with statistical protocols, this research opens avenues for more robust regulatory policies grounded in rigorous incentive-compatible designs.

Future Directions

The framework presented prompts several interesting avenues for future research:

  • Nonlinear Utilities: Investigating scenarios where the agent's utility function is nonlinear, capturing more complex decision-making behaviors.
  • Prior Information: Extending the model to settings where the principal has some prior information regarding the agent's type, potentially yielding more efficient protocols.
  • Broader Applications: Applying these principles beyond clinical trials to other regulated environments where strategic behavior based on statistical evidence plays a crucial role.

In conclusion, the interplay between statistical evidence and economic incentives constitutes a powerful approach in regulatory environments, addressing systemic issues while promoting effective solutions. The integration of e-values and robust contract designs stands out as a promising method for enhancing the reliability and efficacy of regulatory decisions.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets