Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA.
Division of Gastroenterology, Duke University, Durham, North Carolina, USA.
BMC Med Res Methodol. 2023 Mar 27;23(1):71. doi: 10.1186/s12874-023-01885-w.
Drug toxicity does not affect patients equally; the toxicity may only exert in patients who possess certain attributes of susceptibility to specific drug properties (i.e., drug-host interaction). This concept is crucial for personalized drug safety but remains under-studied, primarily due to methodological challenges and limited data availability. By monitoring a large volume of adverse event reports in the postmarket stage, spontaneous adverse event reporting systems provide an unparalleled resource of information for adverse events and could be utilized to explore risk disparities of specific adverse events by age, sex, and other host factors. However, well-formulated statistical methods to formally address such risk disparities are currently lacking.
In this paper, we present a statistical framework to explore spontaneous adverse event reporting databases for drug-host interactions and detect risk disparities in adverse drug events by various host factors, adapting methods for safety signal detection. We proposed four different methods, including likelihood ratio test, normal approximation test, and two tests using subgroup ratios. We applied our proposed methods to simulated data and Food and Drug Administration (FDA) Adverse Event Reporting Systems (FAERS) and explored sex-/age-disparities in reported liver events associated with specific drug classes.
The simulation result demonstrates that two tests (likelihood ratio, normal approximation) can detect disparities in adverse drug events associated with host factors while controlling the family wise error rate. Application to real data on drug liver toxicity shows that the proposed method can be used to detect drugs with unusually high level of disparity regarding a host factor (sex or age) for liver toxicity or to determine whether an adverse event demonstrates a significant unbalance regarding the host factor relative to other events for the drug.
Though spontaneous adverse event reporting databases require careful data processing and inference, the sheer size of the databases with diverse data from different countries provides unique resources for exploring various questions for drug safety that are otherwise impossible to address. Our proposed methods can be used to facilitate future investigation on drug-host interactions in drug toxicity using a large number of reported adverse events.
药物毒性不会平等地影响患者；毒性可能仅在具有对特定药物特性（即药物-宿主相互作用）易感性的某些属性的患者中发挥作用。这个概念对于个性化药物安全性至关重要，但研究仍不充分，主要是由于方法学挑战和数据可用性有限。通过在上市后阶段监测大量不良事件报告，自发不良事件报告系统为不良事件提供了无与伦比的信息资源，并可用于探索特定不良事件的年龄、性别和其他宿主因素的风险差异。然而，目前缺乏正式解决这种风险差异的精心制定的统计方法。
在本文中，我们提出了一种统计框架，用于探索自发不良事件报告数据库中的药物-宿主相互作用，并通过各种宿主因素检测不良药物事件的风险差异，适应安全性信号检测方法。我们提出了四种不同的方法，包括似然比检验、正态逼近检验以及使用亚组比的两种检验方法。我们将我们的方法应用于模拟数据和食品和药物管理局（FDA）不良事件报告系统（FAERS），并探索了与特定药物类别相关的报告肝脏事件中的性别/年龄差异。
模拟结果表明，两种检验方法（似然比、正态逼近）可以在控制总体错误率的情况下检测与宿主因素相关的不良药物事件的差异。对药物肝毒性的真实数据的应用表明，该方法可用于检测具有宿主因素（性别或年龄）的肝毒性的药物差异异常高的药物，或者确定相对于药物的其他事件，不良事件相对于宿主因素是否显示出显著的不平衡。
尽管自发不良事件报告数据库需要仔细的数据处理和推断，但具有来自不同国家的多样化数据的数据库的巨大规模为探索药物安全性的各种问题提供了独特的资源，否则这些问题是不可能解决的。我们提出的方法可以用于促进使用大量报告的不良事件研究药物毒性中的药物-宿主相互作用。
