Despite extensive safety assessments of drugs prior to their introduction to the market, certain adverse drug reactions (ADRs) remain undetected. The primary objective of pharmacovigilance is to identify these ADRs (i.e., signals). In addition to traditional spontaneous reporting systems (SRSs), electronic health (EHC) data is being used for signal detection as well. Unlike SRS, EHC data is longitudinal and thus requires assumptions about the patient's drug exposure history and its impact on ADR occurrences over time, which many current methods do implicitly. We propose an exposure model framework that explicitly models the longitudinal relationship between the drug and the ADR. By considering multiple such models simultaneously, we can detect signals that might be missed by other approaches. The parameters of these models are estimated using maximum likelihood, and the Bayesian Information Criterion (BIC) is employed to select the most suitable model. Since BIC is connected to the posterior distribution, it servers the dual purpose of identifying the best-fitting model and determining the presence of a signal by evaluating the posterior probability of the null model. We evaluate the effectiveness of this framework through a simulation study, for which we develop an EHC data simulator. Additionally, we conduct a case study applying our approach to four drug-ADR pairs using an EHC dataset comprising over 1.2 million insured individuals. Both the method and the EHC data simulator code are publicly accessible as part of the R package https://github.com/bips-hb/expard.

翻译：尽管药物在上市前经过了广泛的安全性评估，某些药物不良反应（ADRs）仍未被发现。药物警戒的主要目标是识别这些ADRs（即信号）。除传统的自发报告系统（SRSs）外，电子健康（EHC）数据也被用于信号检测。与SRS不同，EHC数据具有纵向特征，因此需要基于患者药物暴露史及其随时间对ADR发生的影响做出假设，而当前许多方法对此是隐式处理的。我们提出了一种暴露模型框架，该框架显式地建模药物与ADR之间的纵向关系。通过同时考虑多个此类模型，我们可以检测其他方法可能遗漏的信号。这些模型的参数通过最大似然估计进行估计，并使用贝叶斯信息准则（BIC）选择最合适的模型。由于BIC与后验分布相关联，它同时服务于两个目的：识别最优拟合模型，并通过评估零模型的后验概率确定信号是否存在。我们通过一项模拟研究评估该框架的有效性，并为此开发了一个EHC数据模拟器。此外，我们开展了一项案例研究，使用包含超过120万名参保个体的EHC数据集，将我们的方法应用于四个药物-ADR配对。该方法及EHC数据模拟器代码均已公开，可作为R包的一部分获取：https://github.com/bips-hb/expard。