Using Rich Data on Comorbidities in Case-Control Study Design with Electronic Health Record Data Improves Control of Confounding in the Detection of Adverse Drug Reactions.
Using Rich Data on Comorbidities in Case-Control Study Design with Electronic Health Record Data Improves Control of Confounding in the Detection of Adverse Drug Reactions.
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DOI：
10.1371/journal.pone.0164304
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Wei Y
中科院分区：
文献类型：
--
作者：
Backenroth D;Chase H;Friedman C;Wei Y
最近的研究表明，与自我对照研究设计不同，病例对照研究设计在控制从行政索赔和电子健康记录（EHR）数据中检测药物不良反应（ADR）的混杂方面表现不佳，导致药物对关注的健康结局（HOI）的因果效应的偏倚估计和不准确的置信区间。在这里，我们表明，使用丰富的合并症数据和自动变量选择策略选择混杂因素，可以更好地控制病例对照研究设计中的混杂因素，并为药物对HOI的因果影响的推断提供更坚实的基础。检查四种HOI：急性肾损伤、急性肝损伤、急性心肌梗死和胃肠道溃疡住院。对于这些HOI中的每一个，我们使用先前公布的阳性和阴性对照药物的参考集来评估我们的方法的性能。我们的方法的AUC通常显著高于仅使用人口统计学特征进行混杂控制的基线方法的AUC。我们的方法还给出了因果效应参数的置信区间，这些参数比基线方法更经常地覆盖预期的无效应值。与自身对照研究设计不同，病例对照研究设计可用于EHR数据库中相当典型的设置，而无需患者的纵向信息。使用我们的变量选择方法，这些数据库可以更有效地用于检测ADR。
Recent research has suggested that the case-control study design, unlike the self-controlled study design, performs poorly in controlling confounding in the detection of adverse drug reactions (ADRs) from administrative claims and electronic health record (EHR) data, resulting in biased estimates of the causal effects of drugs on health outcomes of interest (HOI) and inaccurate confidence intervals. Here we show that using rich data on comorbidities and automatic variable selection strategies for selecting confounders can better control confounding within a case-control study design and provide a more solid basis for inference regarding the causal effects of drugs on HOIs. Four HOIs are examined: acute kidney injury, acute liver injury, acute myocardial infarction and gastrointestinal ulcer hospitalization. For each of these HOIs we use a previously published reference set of positive and negative control drugs to evaluate the performance of our methods. Our methods have AUCs that are often substantially higher than the AUCs of a baseline method that only uses demographic characteristics for confounding control. Our methods also give confidence intervals for causal effect parameters that cover the expected no effect value substantially more often than this baseline method. The case-control study design, unlike the self-controlled study design, can be used in the fairly typical setting of EHR databases without longitudinal information on patients. With our variable selection method, these databases can be more effectively used for the detection of ADRs.
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