Spontaneous reporting system databases are key resources for post-marketing surveillance, providing real-world evidence (RWE) on the adverse events (AEs) of regulated drugs or other medical products. Various statistical methods have been proposed for AE signal detection in these databases, flagging drug-specific AEs with disproportionately high observed counts compared to expected counts under independence. However, signal detection remains challenging for rare AEs or newer drugs, which receive small observed and expected counts and thus suffer from reduced statistical power. Principled information sharing on signal strengths across drugs/AEs is crucial in such cases to enhance signal detection. However, existing methods typically ignore complex between-drug associations on AE signal strengths, limiting their ability to detect signals. We propose novel local-global mixture Dirichlet process (DP) prior-based nonparametric Bayesian models to capture these associations, enabling principled information sharing between drugs while balancing flexibility and shrinkage for each drug, thereby enhancing statistical power. We develop efficient Markov chain Monte Carlo algorithms for implementation and employ a false discovery rate (FDR)-controlled, false negative rate (FNR)-optimized hypothesis testing framework for AE signal detection. Extensive simulations demonstrate our methods' superior sensitivity -- often surpassing existing approaches by a twofold or greater margin -- while strictly controlling the FDR. An application to FDA FAERS data on statin drugs further highlights our methods' effectiveness in real-world AE signal detection. Software implementing our methods is provided as supplementary material.

翻译：自发报告系统数据库是上市后监测的关键资源，可为监管药物或其他医疗产品的不良事件（AEs）提供真实世界证据（RWE）。已有多种统计方法被提出用于此类数据库中的AE信号检测，这些方法通过标记在独立性假设下观测计数显著高于预期计数的药物特异性AE来实现。然而，对于罕见AE或较新药物，由于观测计数和预期计数均较小，导致统计效能降低，信号检测仍面临挑战。在此类情况下，对药物/AE的信号强度进行规范化的信息共享对于增强信号检测至关重要。然而，现有方法通常忽略AE信号强度在药物间的复杂关联，从而限制了其检测信号的能力。我们提出了一种基于局部-全局混合狄利克雷过程（DP）先验的新型非参数贝叶斯模型来捕捉这些关联，可在实现药物间规范化信息共享的同时，平衡各药物的灵活性与收缩性，从而增强统计效能。我们开发了高效的马尔可夫链蒙特卡洛算法进行实现，并采用受错误发现率（FDR）控制且优化假阴性率（FNR）的假设检验框架进行AE信号检测。大量模拟实验表明，我们的方法具有优越的灵敏度——通常比现有方法高出两倍以上——同时严格控制了FDR。应用于FDA FAERS他汀类药物数据的案例进一步凸显了该方法在真实世界AE信号检测中的有效性。实现该方法的软件作为补充材料提供。