A Mixture Dose-Response Model for Identifying High-Dimensional Drug Interaction Effects on Myopathy Using Electronic Medical Record Databases.
A Mixture Dose-Response Model for Identifying High-Dimensional Drug Interaction Effects on Myopathy Using Electronic Medical Record Databases.
复制标题
使用电子病历数据库识别高维药物相互作用对肌病影响的混合剂量反应模型。
DOI：
10.1002/psp4.53
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Li,L
中科院分区：
文献类型：
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作者：
Zhang,P;Du,L;Wang,L;Liu,M;Cheng,L;Chiang,C-W;Wu,H-Y;Quinney,SK;Shen,L;Li,L
多种药物之间的相互作用可能产生过度的不良反应风险。尽管某些组合可能具有恒定的不良反应风险，但所有组合的风险增加并不一致。我们开发了一个统计模型，使用医疗记录数据来识别诱导肌病风险的药物组合。这种组合揭示了使用一种新的混合模型，包括一个恒定的风险模型和剂量反应风险模型。剂量代表药物组合的数量。使用经验贝叶斯估计方法，我们成功地确定了高维（2到6个）药物组合，这些药物组合与肌病风险过高相关，局部错误发现率显著较低。从剂量-反应模型曲线和高维药物相互作用数据中，我们观察到肌病风险随着药物相互作用维度的增加而增加。这是第一次观察到这种高维药物相互作用的剂量-反应关系，并从病历数据库中提取。
Interactions between multiple drugs may yield excessive risk of adverse effects. This increased risk is not uniform for all combinations, although some combinations may have constant adverse effect risks. We developed a statistical model using medical record data to identify drug combinations that induce myopathy risk. Such combinations are revealed using a novel mixture model, comprised of a constant risk model and a dose–response risk model. The dose represents the number of drug combinations. Using an empirical Bayes estimation method, we successfully identified high‐dimensional (two to six) drug combinations that are associated with excessive myopathy risk at significantly low local false‐discovery rates. From the curve of a dose–response model and high‐dimensional drug interaction data, we observed that myopathy risk increases as the drug interaction dimension increases. This is the first time that such a dose–response relationship for high‐dimensional drug interactions was observed and extracted from the medical record database.