Adaptation of Bayesian data mining algorithms to longitudinal claims data - Coxib safety as an example
Adaptation of Bayesian data mining algorithms to longitudinal claims data - Coxib safety as an example
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DOI：
10.1097/mlr.0b013e318179253b
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发表时间：
2008-09-01
期刊：
影响因子：
3
通讯作者：
DuMouchel, William
中科院分区：
文献类型：
--
作者：
Curtis, Jeffrey R.;Cheng, Hong;DuMouchel, William
简介：贝叶斯数据挖掘方法已被用于评价来自不良事件报告系统的药物安全性信号，并允许评价未预先指定的多个终点。他们的适应使用纵向数据，如行政索赔尚未进行评估或validated.Methods：在这项试点研究中，我们评估了适应数据挖掘方法的可行性，使用经验贝叶斯多项伽玛泊松收缩（MGPS）算法纵向行政索赔数据。Medicare当前受益调查用于确定1999年至2003年暴露于环氧合酶选择性（coxib）或非选择性非甾体抗炎药（NS-NSAID）的Medicare登记者队列。经验贝叶斯MGPS算法用于同时评价与目前使用昔布与NS-NSAID相关的259个结局，同时调整关键协变量和多重比较。为了比较，一个平行的分析使用传统的流行病学方法来评估与非甾体抗炎药的使用和急性心肌梗死之间的关系，与建立并发的数据挖掘approach.Results的有效性的目标：在9431医疗保险受益人使用非甾体抗炎药，并考虑所有2：59可能的结果，经验贝叶斯MGPS确定了一个当前塞来昔布的使用和急性心肌梗死（经验贝叶斯几何平均值比1.91），但不是其他结果之间的关联。罗非昔布的使用与急性脑血管事件（经验贝叶斯几何均值比1.85）和其他几种可能代表药物适应症的诊断相关。使用传统的流行病学方法的分析结果是相似的，并表明数据挖掘结果是valid.Discussion：贝叶斯数据挖掘方法似乎有用的管理数据来评估药物安全性。需要进一步开展工作，将这些发现扩展到不同类型的药物暴露和其他索赔数据库。
Introduction: Bayesian data mining methods have been used to evaluate drug safety signals from adverse event reporting systems and allow for evaluation of multiple endpoints that are not prespecified. Their adaptation for use with longitudinal data such as administrative claims has not been previously evaluated or validated.Methods: In this pilot study, we evaluated the feasibility of adapting data mining methods using the empirical Bayes Multi-item Gamma Poisson Shrinkage (MGPS) algorithm to longitudinal administrative claims data. The Medicare Current Beneficiary Survey was used to identify a cohort of Medicare enrollees who were exposed to cyclooxygenase selective (coxib) or nonselective nonsteroidal anti-inflammatory drugs (NS-NSAIDs) from 1999 to 2003. Empirical Bayes MGPS algorithm was used to simultaneously evaluate 259 outcomes associated with current use of coxibs versus NS-NSAIDs while adjusting for key covariates and multiple comparisons. For comparison, a parallel analysis used traditional epidemiologic methods to evaluate the relationship between coxib versus NS-NSAID use and acute myocardial infarction, with the goal of establishing the concurrent validity of the data mining approach.Results: Among 9431 Medicare beneficiaries using NSAIDs and considering all 2:59 possible outcomes, empirical Bayes MGPS identified an association between current celecoxib use and acute myocardial infarction (Empirical Bayes Geometric Mean ratio 1.91) but not other outcomes. Rofecoxib use was associated with acute cerebrovascular events (Empirical Bayes Geometric Mean ratio 1.85) and several other diagnoses that likely represented indications for the drug. Results from the analyses using traditional epidemiologic methods were similar and indicated that the data mining results were valid.Discussion: Bayesian data mining methods seem useful to evaluate drug safety using administrative data. Further work will be needed to extend these findings to different types of drug exposures and to other claims databases.