Drug Safety Risk-Benefit Models

药物安全风险-收益模型james C. bennean机械与工业工程东北大学波士顿，maa摘要该基金为开发新的分析药物安全模型提供资金，以帮助评估药物的风险-收益权衡，这是一个越来越大的公众和监管关注的问题。将开发和整合三种数学方法来评估药物安全性和风险-收益权衡，基于治疗比率分数规划模型，使用基于效用的度量的矩和分布的概率和均值-方差模型，以及概率前沿模型来识别随机优先药物或以最小风险不确定性最大化平均净收益的药物。本研究的一个重要和独特的焦点是，虽然传统方法本质上是基于人口预期效用的，但开发的算法将提供更全面的以患者为中心的信息，考虑风险不确定性。每种建模方法都将为这些类型的应用中普遍存在的几个技术和数学问题提供解决方案，并将与临床和药物警戒同事一起开发，他们将提供项目指导、试验数据和内容领域的专业知识。如果成功，这项研究的结果将有助于改进评估药物相对风险与收益的方法，无论是在开发中还是在监管审批过程中。开发的模型将在抗感染、抗凝血剂、处方止痛剂和在这项工作中确定的其他应用中进行测试。这些模型可以单独或一起使用，以帮助评估风险-收益权衡，了解非最佳产品的局限性，并做出批准或开发决策。更广泛地说，虽然这里的动机是药物安全问题，但设想的结果也可能为在广泛的医疗保健和非医疗应用中评估风险-收益权衡提供根本的新方法。所建议的工作也将对效用理论、数据包络和随机前沿分析方法做出重要的方法论贡献。

Drug Safety Risk-Benefit ModelsJames C. BenneyanMechanical and Industrial EngineeringNortheastern UniversityBoston, MAAbstractThis grant provides funding to develop new analytic drug safety models to help evaluate risk-benefit tradeoffs of pharmaceutical drugs, a large and growing public and regulatory concern. Three mathematical approaches will be developed and integrated for evaluating drug safety and risk-benefit tradeoffs, based on therapeutic ratio fractional programming models, probabilistic and mean-variance models using moments and distributions of utility-based measures, and probabilistic frontier models to identify stochastically preferential drugs or those maximizing mean net benefits with minimal risk uncertainty. An important and unique focus of this research is that while conventional methods essentially are population expected-utility based, the developed algorithms will provide more comprehensive patient-centered information accounting for risk uncertainty. Each modeling approach will develop solutions to several technical and mathematical issues prevalent in these types of applications and will be developed working with clinical and pharmacovigilence colleagues who will provide project guidance, pilot data, and content area expertise. If successful, the results of this research will contribute improved methods for evaluating the relative risks versus benefits of pharmaceutical drugs while, both under development and undergoing regulatory approval processes. The developed models will be tested on anti-infectives, anticoagulants, prescription analgesics, and other applications identified during this work. These models could be used separately or together to help assess risk-benefit tradeoffs, understand limitations of non-optimal products, and make approval or development decisions. More broadly, while motivated here by drug safety problems, the envisioned results potentially also will offer fundamentally new approaches for evaluating risk-benefit tradeoffs in a wide range of healthcare and non-medical applications. The proposed work also will make important methodological contributions to utility theory, data envelopment, and stochastic frontier analysis methods.