Shi Yi, Sun Anna, Yang Yuedi, Xu Jing, Li Justin, Eadon Michael, Su Jing, Zhang Pengyue
Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN, USA.
Park Tudor School, Indianapolis, IN, USA.
Sci Rep. 2025 Apr 21;15(1):13693. doi: 10.1038/s41598-025-98528-5.
Drug-drug interaction-induced (DDI-induced) adverse drug event (ADE) is a significant public health burden. Risk of ADE can be related to timing of exposure (TOE) such as initiating two drugs concurrently or adding one drug to an existing drug. Thus, real-world data based DDI detection shall be expanded to investigate precise adverse DDI with a special awareness on TOE. We developed a Sensitive and Timing-awarE Model (STEM), which was able to optimize the probability of detection and control false positive rate for mining all two-drug combinations under case-crossover design, in particular for DDIs with TOE-dependent risk. We analyzed a large-scale US administrative claims data and conducted performance evaluation analyses. We identified signals of DDIs by using STEM, in particular for DDIs with TOE-dependent risk. We also observed that STEM identified significantly more signals than the conditional logistic regression model-based (CLRM-based) methods and the Benjamini-Hochberg procedure. In the performance evaluation, we found that STEM demonstrated proper false positive control and achieved a higher probability of detection compared to CLRM-based methods and the Benjamini-Hochberg procedure. STEM has a high probability to identify signals of DDIs in high-throughput DDI mining while controlling false positive rate, in particular for detecting signals of DDI with TOE-dependent risk.
药物相互作用诱导（DDI诱导）的药物不良事件（ADE）是一项重大的公共卫生负担。ADE的风险可能与暴露时间（TOE）有关，例如同时开始使用两种药物或在现有药物基础上添加一种药物。因此，应扩大基于真实世界数据的DDI检测，以调查精确的不良DDI，并特别关注TOE。我们开发了一种敏感且具有时间意识的模型（STEM），该模型能够在病例交叉设计下优化检测概率并控制假阳性率，以挖掘所有两种药物的组合，特别是对于具有TOE依赖性风险的DDI。我们分析了大规模的美国行政索赔数据并进行了性能评估分析。我们使用STEM识别了DDI信号，特别是对于具有TOE依赖性风险的DDI。我们还观察到，与基于条件逻辑回归模型（CLRM）的方法和Benjamini-Hochberg程序相比，STEM识别出的信号明显更多。在性能评估中，我们发现与基于CLRM的方法和Benjamini-Hochberg程序相比，STEM展示了适当的假阳性控制并实现了更高的检测概率。STEM在控制假阳性率的同时，有很高的概率在高通量DDI挖掘中识别DDI信号，特别是用于检测具有TOE依赖性风险的DDI信号。
