Innovation Adoption by Committee: Evaluating Decision-Making in the FDA
The Food and Drug Administration (FDA) is responsible for the approval of new drugs, biological products and medical devices in the United States. As part of this process, the FDA relies on advisory committees (ACs) to provide recommendations on the adoption of new products. In this project, the researchers will quantify the effectiveness of the advisory committee system, and evaluate whether possible institutional innovations would improve the quality of its decision-making. The project is composed of three papers. In the first paper, the researchers study whether advisory committees use available information about new products effectively. To do this, the authors combine a game-theoretic model of decision-making in ACs with deliberation and voting data from ACs’ meetings’ transcripts. The approach allows the researchers to conduct policy experiments examining how institutional changes to the approval process for new products by the FDA affect the likelihood of reaching correct recommendations. In the second paper, the researchers exploit exogenous variation in the composition of ACs to evaluate the effect of mandates for diverse representation on deliberative outcomes. In the third project, the authors study the quality of FDA advisory committee decision-making in the long run, using information on the ex-post performance of approved products. Altogether, the project will provide crucial new evidence to inform FDA’s decisions about the adoption of new products, as well as its institutional design. Moreover, the economic models and statistical methods developed in this project could be applied to study how information is used in other policy-making bodies, including regulatory bodies and central banks, as well as in boards of directors in corporations. The implementation of state-of-the-art statistical methods, data collection efforts, and development of new economic models will allow significant mentoring opportunities for students, who will engage related literature, explore results of simplified models under alternative assumptions, and aid in coding and data analysis.
The proposed project advances knowledge in various ways. At the outset, the proposal assembles a new microlevel dataset (from publicly available but dispersed and unprocessed information) that is useful for both research and policy, tracking the entire process of deliberation and voting for each advisory committee meeting in the past fifteen years. In the first paper, the researchers structurally estimate a dynamic model in which committee members with heterogeneous preferences collectively decide when to stop gathering information, and vote to recommend the approval or rejection of new products. This model carefully considers how such members may influence their peers’ behavior when gathering information. When taken to data, the empirical model provides a new approach to study collective deliberation and information acquisition in the field, which can be extended to other collective policy-making bodies. In the second paper, the researchers leverage exogenous variation in the composition of advisory committees and imperfect overlap in the network structure to evaluate the effect of mandates for diverse representation on deliberative outcomes. The analysis provides new insights of the effectiveness of such policies within the context of deliberative bodies, which are subject to collective learning. The third paper contributes to knowledge on long run outcomes from committee-made decisions with imperfect information. To do so, it explicitly incorporates the ex-post performance of approved products. As different devices may have different baseline error rates (e.g., it may be more difficult to make correct decisions as often with complex devices), the researchers will compare the long run outcomes under committee decision-making with a counterfactual policy experiment where the policy is set by a single decision-maker. This will further inform the long-run benefits of committee-based decisions relative to individual ones, keeping constant the information available in both environments.
Supported by the National Science Foundation grant #2214796
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