A Network Science Approach to Conflicts of Interest: Metrics, Policies, and Communication Design

该项目的主要目的是为评估以下方面制定新的衡量标准和机制： 生物医学研究企业的利益冲突（COI）风险。COI风险的创新 评估和沟通将从把COI作为一个问题的概念化转变而来 个人研究人员对COI作为网络现象的理解。的紧迫问题 COI及其灌输的偏见不是来自个人，而是来自COI的聚合。 研究人员和资助者的网络。研究团队将利用机器学习和高 性能计算（1）跟踪生物医学决策网络中COI的循环，以及（2） 评估某些冲突网络配置文件预测患者伤害风险增加的程度。的 研究小组将根据美国食品药品监督管理局的不良事件报告测试候选COI指标 报告系统药物安全性数据。以这些数据为基础，本项目还将（3）开发和 评估沟通COI偏倚风险的策略，旨在防止不正当的 在当前的披露实践中取得的成果。这项研究的结果将支持新的循证医学， 生物医学研究中的COI政策建议以及更有效的建议 披露做法。为了实现这些目标，该项目将利用机器学习来识别系统 COI网络在生物医学研究企业内的特定药品。研究团队将 然后评估网络指标在多大程度上预测不良事件发生率和严重程度的相对增加 识别产品。

The primary purpose of this project is to develop new metrics and mechanisms for the evaluation of conflicts of interest (COI) risks in the biomedical research enterprise. The innovation in COI risk evaluation and communication will draw on a shift away from the conceptualization of COI as a problem of individual researchers toward an understanding of COI as network phenomena. The pressing problems of COI and the bias it inculcates stem not from individuals but from the aggregation of COI across networks of researchers and funders. The research team will leverage machine-learning and high performance computing to (1) track the circulation of COI within biomedical decision networks, and (2) evaluate the extent to which certain conflict network profiles predict increased risks of patient harm. The team will test candidate COI metrics with U.S. Food and Drug Administration's Adverse Events Reporting System drug safety data. Using these data as a foundation, this project will also (3) develop and evaluate strategies for communicating COI risks of bias designed to safeguard against the perverse outcomes in current disclosure practices. The results of this research will underwrite novel evidence-based recommendations for COI policies in biomedical research as well as recommendations for more effective disclosure practices. To achieve these aims, this project will leverage machine-learning to identify systemic COI networks within the biomedical research enterprises for specific drug products. The research team will then evaluate to what extent network metrics predict relative increases in adverse event rates and severity for identified products.