Suicide is the tenth leading cause of death in the United States, yet evidence on medication-related risk or protection remains limited. Most post-marketing studies examine one drug class at a time or rely on empirical-Bayes shrinkage with conservative multiplicity corrections, sacrificing power to detect clinically meaningful signals. We introduce a unified Bayesian spike-and-slab framework that advances both applied suicide research and statistical methodology. Substantively, we screen 922 prescription drugs across 150 million patients in U.S. commercial claims (2003 to 2014), leveraging real-world co-prescription patterns to inform a covariance prior that adaptively borrows strength across pharmacologically related agents. Statistically, the model couples this structured prior with Bayesian false-discovery-rate control, illustrating how network-guided variable selection can improve rare-event surveillance in high dimensions. Relative to the seminal empirical-Bayes analysis of Gibbons et al. (2019), our approach reconfirms the key harmful (e.g., alprazolam, hydrocodone) and protective (e.g., mirtazapine, folic acid) signals while revealing additional associations, such as a high-risk opioid combination and several folate-linked agents with potential preventive benefit that had been overlooked. A focused re-analysis of 18 antidepressants shows how alternative co-prescription metrics modulate effect estimates, shedding light on competitive versus complementary prescribing. These findings generate actionable hypotheses for clinicians and regulators and showcase the value of structured Bayesian modeling in pharmacovigilance.

翻译：自杀是美国第十大死因，然而关于药物相关风险或保护作用的证据仍然有限。大多数上市后研究每次仅考察一类药物，或依赖于经验贝叶斯收缩结合保守的多重性校正，牺牲了检测具有临床意义信号的能力。我们引入了一个统一的贝叶斯尖峰-平板框架，该框架同时推进了应用自杀研究和统计方法论。在实质内容上，我们基于美国商业医保索赔数据（2003年至2014年）对1.5亿患者中的922种处方药进行了筛查，利用真实世界的联合处方模式构建协方差先验，从而在药理学相关的药物间自适应地借用信息。在统计方法上，该模型将这种结构化先验与贝叶斯错误发现率控制相结合，阐明了网络引导的变量选择如何能改进高维罕见事件监测。相较于Gibbons等人（2019）开创性的经验贝叶斯分析，我们的方法不仅再次确认了关键的有害（如阿普唑仑、氢可酮）和保护性（如米氮平、叶酸）信号，还揭示了额外的关联，例如一种高风险阿片类药物组合以及几种先前被忽视的、具有潜在预防益处的叶酸相关药物。对18种抗抑郁药物的重点再分析表明，不同的联合处方度量如何调节效应估计，从而阐明竞争性与互补性处方模式。这些发现为临床医生和监管者提供了可操作的假设，并展示了结构化贝叶斯建模在药物警戒中的价值。