"Stopping Rules" for Drug Safety Monitoring Using Healthcare Databases

利用医疗保健数据库进行药品安全监测的“停止规则”

• 批准号: 7818882
• 负责人: Sebastian G. Schneeweiss
• 金额: $ 49.05万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7818882
• 关键词: Address; Adverse event; Algorithms; Amendment; Area; Bayesian Method; Clinical; Cohort Studies; Collaborations; Collection; Congresses; Data; Data Sources; Databases; Decision Making; Deposition; Detection; Development; Drug Exposure; Drug Interactions; Electronics; Environment; Event; Generations; Health; Health Benefit; Health Planning; Healthcare; Informatics; Information Technology; Institute of Medicine (U.S.); Learning; Life; Measures; Medical; Metric; Modeling; Monitor; Morbidity - disease rate; Negative Finding; Outcome; Patients; Performance; Pharmaceutical Preparations; Prevalence; Process; Property; Public Health; Publishing; Records; Reporting; Research; Risk; Risk Estimate; Safety; Sentinel; Services; Signal Transduction; Simulate; Structure; System; Testing; United States Food and Drug Administration; alternative treatment; base; cohort; cost; experience; health care service utilization; improved; member; mortality; population based; post-market; prospective; routine care; statistics; web page

Description (provided by applicant): Current FDA post-marketing drug safety monitoring relies principally on passive surveillance via MedWatch reports to the Adverse Event Report System. By contrast, active surveillance using population-based healthcare utilization databases could improve the validity and timeliness of signal detection. FDA's proposed Sentinel Initiative plans to utilize healthcare databases and their research environments for ongoing drug safety monitoring and highlights the importance of developing the capability to conduct active safety surveillance for signal generation and confirmation. However, much needs to be learned about how to implement and utilize such data. Signals are stronger-than-expected associations between a drug and adverse medical events. One common approach is to routinely accumulate drug exposure and medical event data as they become available (e.g. monthly) and regularly evaluate this cumulative cohort study. Despite the enormous potential public health benefit of ongoing drug safety monitoring, there is little understanding of when such a prospective cumulative monitoring activity should be stopped to warn the public, or to conclude that a medication is safe. Delaying such a decision could unnecessarily put patients at risk, but, conversely, falsely warning of a risk may reduce use of important medications. Both scenarios may result in increased morbidity and/or mortality as a consequence of inadequate decision making. Based on 25 years of experience with longitudinal claims databases and an ongoing close collaboration with WellPoint/HealthCore, a collection of 14 BlueCross health plans that records data on all drug dispensing and healthcare utilization for 30 million insured members, we propose to empirically evaluate published stopping rules and develop and test new algorithms to address this pressing question. Specifically, we will: --- Identify, characterize, and build empirical example studies for drug safety monitoring and develop a set of simulated data sources, --- Develop and apply a set of "stopping rules" for the data scenarios identified above that would terminate a cumulative cohort study based on epidemiologic and clinical metrics and sequential test statistics, --- Using a decision analytic framework we will combine the epidemiologic stopping rules above with information on the societal cost of false positive and false negative findings, --- SAS macros for all tested approaches will be deposited on a web-page. This 2-year project will greatly increase our understanding of cumulative drug safety monitoring based on empirical and simulated data and in combination with a decision analytic framework, leading to the development of enhanced, data-driven stopping rules for use with emerging pharmacoepidemiologic databases.

描述（由申请人提供）：目前 FDA 上市后药物安全监测主要依赖于通过 MedWatch 向不良事件报告系统报告的被动监测。相比之下，使用基于人群的医疗保健利用数据库进行主动监测可以提高信号检测的有效性和及时性。 FDA 提出的哨兵计划计划利用医疗保健数据库及其研究环境进行持续的药物安全监测，并强调开发进行主动安全监测以生成和确认信号的能力的重要性。然而，如何实施和利用这些数据还需要了解很多。信号是药物与不良医疗事件之间强于预期的关联。一种常见的方法是定期积累药物暴露和医疗事件数据（例如每月），并定期评估这种累积队列研究。尽管持续的药物安全监测具有巨大的潜在公共卫生益处，但人们对何时应该停止这种前瞻性累积监测活动以警告公众或得出药物安全的结论却知之甚少。推迟这样的决定可能会给患者带来不必要的风险，但相反，错误地警告风险可能会减少重要药物的使用。由于决策不充分，这两种情况都可能导致发病率和/或死亡率增加。基于 25 年的纵向索赔数据库经验以及与 WellPoint/HealthCore 的持续密切合作（WellPoint/HealthCore 是 14 个 BlueCross 健康计划的集合，记录了 3000 万受保会员的所有药物配发和医疗保健利用数据），我们建议对已发布的停止进行实证评估规则并开发和测试新算法来解决这个紧迫的问题。具体来说，我们将： --- 识别、描述和构建药品安全监测的实证实例研究，并开发一组模拟数据源， --- 为上述确定的数据场景开发并应用一组“停止规则”将终止基于流行病学和临床指标以及序贯测试统计的累积队列研究， --- 使用决策分析框架，我们将把上述流行病学停止规则与假阳性和假阴性结果的社会成本信息结合起来， ---适合所有人的 SAS 宏经过测试的方法将存放在网页上。这个为期两年的项目将极大地增进我们对基于经验和模拟数据并结合决策分析框架的累积药物安全监测的理解，从而开发出增强的、数据驱动的停止规则，以与新兴的药物流行病学数据库一起使用。