Childrens Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Childrens Hospital, Harvard Medical School, Boston, MA, USA.
Drug Saf. 2012 May 1;35(5):395-406. doi: 10.2165/11596610-000000000-00000.
Recent withdrawals of major drugs have highlighted the critical importance of drug safety surveillance in the postmarketing phase. Limitations of spontaneous report data have led drug safety professionals to pursue alternative postmarketing surveillance approaches based on healthcare administrative claims data. These data are typically analysed by comparing the adverse event rates associated with a drug of interest to those of a single comparable reference drug.
The aim of this study was to determine whether adverse event detection can be improved by incorporating information from multiple reference drugs. We developed a pharmacological network model that implemented this approach and evaluated its performance.
We studied whether adverse event detection can be improved by incorporating information from multiple reference drugs, and describe two approaches for doing so. The first, reported previously, combines a set of related drugs into a single reference cohort. The second is a novel pharmacoepidemiological network model, which integrates multiple pair-wise comparisons across an entire set of related drugs into a unified consensus safety score for each drug. We also implemented a single reference drug approach for comparison with both multi-drug approaches. All approaches were applied within a sequential analysis framework, incorporating new information as it became available and addressing the issue of multiple testing over time. We evaluated all these approaches using statin (HMG-CoA reductase inhibitors) safety data from a large healthcare insurer in the US covering April 2000 through March 2005.
We found that both multiple reference drug approaches offer earlier detection (6-13 months) than the single reference drug approach, without triggering additional false positives.
Such combined approaches have the potential to be used with existing healthcare databases to improve the surveillance of therapeutics in the postmarketing phase over single-comparator methods. The proposed network approach also provides an integrated visualization framework enabling decision makers to understand the key high-level safety relationships amongst a group of related drugs.
最近一些主要药物的撤市突显了药物安全监测在上市后阶段的至关重要性。自发报告数据的局限性促使药物安全专业人员寻求基于医疗保健管理索赔数据的替代上市后监测方法。这些数据通常通过将与目标药物相关的不良事件发生率与单一可比参考药物的发生率进行比较来分析。
本研究旨在确定通过合并多个参考药物的信息是否可以提高不良事件的检测能力。我们开发了一种药理学网络模型来实现这种方法并评估其性能。
我们研究了通过合并多个参考药物的信息是否可以提高不良事件的检测能力，并描述了两种这样做的方法。第一种方法之前有报道，即将一组相关药物组合成一个单一的参考队列。第二种是一种新颖的药物流行病学网络模型，它将整个相关药物组中多个两两比较整合到每个药物的统一共识安全评分中。我们还实现了一种单一参考药物方法来与这两种多药物方法进行比较。所有方法都在一个顺序分析框架内实施，随着新信息的出现而不断纳入新信息，并解决随时间推移进行多次测试的问题。我们使用来自美国一家大型医疗保险公司的他汀类药物（HMG-CoA 还原酶抑制剂）安全数据（涵盖 2000 年 4 月至 2005 年 3 月）来评估所有这些方法。
我们发现，两种多参考药物方法都比单一参考药物方法更早地检测到（6-13 个月），而不会触发额外的假阳性。
这些组合方法有可能与现有的医疗保健数据库一起使用，以提高上市后阶段治疗药物的监测能力，优于单一比较方法。所提出的网络方法还提供了一个集成的可视化框架，使决策者能够理解一组相关药物之间的关键高级安全关系。
