Drug safety data mining with a tree-based scan statistic
Drug safety data mining with a tree-based scan statistic
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DOI：
10.1002/pds.3423
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发表时间：
2013-05-01
影响因子：
2.6
通讯作者：
Brown, Jeffrey S.
中科院分区：
文献类型：
--
作者：
Kulldorff, Martin;Dashevsky, Inna;Brown, Jeffrey S.
目的在药品上市后安全监测中，数据挖掘可以发现罕见但严重的不良事件。传统上，评估整个药物事件对集合是在预定义的粒度级别上执行的。目前尚不清楚一种药物是否会引起一种特定的或一系列相关的不良事件，如二尖瓣疾病、所有瓣膜疾病或不同类型的心脏病。本文对基于树的扫描统计数据挖掘方法在加强药品安全监测中的应用进行了方法学评价。方法：我们使用来自HMO研究网络的300万成员的电子健康记录数据库。使用基于树的扫描统计，我们评估了选定的抗真菌和糖尿病药物的安全性，同时评估了不同粒度级别的重叠诊断组，并对多个测试进行了调整。预期和观察到的不良事件计数根据年龄、性别和健康计划进行调整，产生对数似然比检验统计量。结果在732个评估的疾病分组中，有24个具有统计学意义，分为10个不重叠的疾病类别。10个信号中有5个是已知的不良反应，4个可能是由于适应症引起的混淆，而一个可能需要进一步调查。结论基于树的扫描统计可以成功地作为一种数据挖掘工具应用于药品安全监测中。统计信号的总数不大，并不意味着存在因果关系。相反，数据挖掘结果应该用于生成候选药物事件对，以进行严格的流行病学研究，以评估药物的个体和比较安全性。版权所有：John Wiley & Sons， Ltd。
Purpose In post-marketing drug safety surveillance, data mining can potentially detect rare but serious adverse events. Assessing an entire collection of drugevent pairs is traditionally performed on a predefined level of granularity. It is unknown a priori whether a drug causes a very specific or a set of related adverse events, such as mitral valve disorders, all valve disorders, or different types of heart disease. This methodological paper evaluates the tree-based scan statistic data mining method to enhance drug safety surveillance. Methods We use a three-million-member electronic health records database from the HMO Research Network. Using the tree-based scan statistic, we assess the safety of selected antifungal and diabetes drugs, simultaneously evaluating overlapping diagnosis groups at different granularity levels, adjusting for multiple testing. Expected and observed adverse event counts were adjusted for age, sex, and health plan, producing a log likelihood ratio test statistic. Results Out of 732 evaluated disease groupings, 24 were statistically significant, divided among 10 non-overlapping disease categories. Five of the 10 signals are known adverse effects, four are likely due to confounding by indication, while one may warrant further investigation. Conclusion The tree-based scan statistic can be successfully applied as a data mining tool in drug safety surveillance using observational data. The total number of statistical signals was modest and does not imply a causal relationship. Rather, data mining results should be used to generate candidate drugevent pairs for rigorous epidemiological studies to evaluate the individual and comparative safety profiles of drugs. Copyright (c) 2013 John Wiley & Sons, Ltd.