Clinical trials often lack power to identify rare adverse drug events (ADEs) and therefore cannot address the threat rare ADEs pose, thus motivating the need for new ADE detection techniques. Emerging national patient claims and electronic health record databases have inspired post‐approval early detection methods like the Bayesian self‐controlled case series (BSCCS) regression model. Existing BSCCS models do not account for multiple outcomes, where pathology may be shared across different ADEs. We integrate a pathology hierarchy into the BSCCS model by developing a novel informative hierarchical prior linking outcome‐specific effects. Considering shared pathology drastically increases the dimensionality of the already massive models in this field. We develop an efficient method for coping with the dimensionality expansion by reducing the hierarchical model to a form amenable to existing tools. Through a synthetic study we demonstrate decreased bias in risk estimates for drugs when using conditions with different true risk and unequal prevalence. We also examine observational data from the MarketScan Lab Results dataset, exposing the bias that results from aggregating outcomes, as previously employed to estimate risk trends of warfarin and dabigatran for intracranial hemorrhage and gastrointestinal bleeding. We further investigate the limits of our approach by using extremely rare conditions. This research demonstrates that analyzing multiple outcomes simultaneously is feasible at scale and beneficial. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016

中文翻译：

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使用大规模观察医疗数据库的多种罕见结果的分层模型。


临床试验通常缺乏识别罕见药物不良事件 (ADE) 的能力，因此无法解决罕见 ADE 带来的威胁，从而激发了对新 ADE 检测技术的需求。新兴的国家患者索赔和电子健康记录数据库激发了批准后的早期检测方法，例如贝叶斯自我对照病例系列（BSCCS）回归模型。现有的 BSCCS 模型无法考虑多种结果，其中病理学可能在不同的 ADE 之间共享。我们通过开发一种新颖的信息分层先验链接结果特定效应，将病理层次结构整合到 BSCCS 模型中。考虑到共享病理学极大地增加了该领域已经庞大的模型的维度。我们开发了一种有效的方法来应对维度扩展，通过将层次模型简化为适合现有工具的形式。通过一项综合研究，我们证明，当使用具有不同真实风险和不平等患病率的条件时，药物风险估计的偏差会减少。我们还检查了 MarketScan 实验室结果数据集中的观察数据，揭示了汇总结果导致的偏差，正如之前用于估计华法林和达比加群治疗颅内出血和胃肠道出血的风险趋势一样。我们通过使用极其罕见的条件进一步研究我们方法的局限性。这项研究表明，同时分析多个结果是大规模可行且有益的。 © 2016 Wiley periodicals, Inc. 统计分析和数据挖掘：ASA 数据科学杂志，2016 年