基于树的扫描统计量用于具有时间至事件结局的数据库研究
A tree-based scan statistic for database studies with time-to-event outcomes
- The Ohio State University（俄亥俄州立大学）
- Brigham and Women’s Hospital and Harvard Medical School（布莱根妇女医院和哈佛医学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出三种基于树的扫描统计量方法，利用事件时间检测药物不良反应信号，并通过模拟和真实数据库研究验证其优于传统事件计数方法。
AI中文摘要：
基于树的扫描统计量（TBSSs）是用于不成比例分析的机器学习方法。它们同时扫描数千个层次相关的健康结局，以检测药物和疫苗潜在危害信号，并控制多重性。TBSSs已被广泛用于挖掘保险索赔数据库以检测潜在的药物不良事件。当前的TBSS实现不允许进行具有时间至事件结局的比较安全性评估。在分析中明确考虑事件时间可以提高检测信号的能力，相比仅使用事件计数的方法。我们提出了三种TBSS方法，明确利用事件时间检测药物的潜在有害效应。第一种假设结局层次中每个节点的比例风险率，并使用置换方案进行推断。第二种基于层次终端节点的指数生存模型，假设每个节点的风险率恒定，并使用参数自举进行推断。第三种使用风险率的稳健渐近近似，结合近似参数自举。我们在模拟场景中将所提出的方法与基于事件计数的标准TBSS进行比较。最后，我们展示了一项数据库研究的结果，该研究比较了2型糖尿病成人中两种降糖药物。
英文摘要：
Tree-based scan statistics (TBSSs) are machine learning methods for disproportionality analyses. They simultaneously scan for thousands of hierarchically related health outcomes to detect potential signals of harm from drugs and vaccines, controlling for multiplicity. TBSSs have been extensively used to mine insurance claims databases to detect potential drug adverse events. Current TBSS implementations do not allow comparative safety evaluations with time-to-event outcomes. Explicitly accounting for event timing in analyses can improve power to detect signals compared to methods that use only event counts. We propose three TBSS methods that explicitly leverage event timing to detect potentially harmful effects of drugs. The first assumes proportional hazard rates for each node of the outcome hierarchy and uses a permutation scheme for inference. The second builds on exponential survival models for the terminal nodes of the hierarchy, assuming constant hazard rates at each node, and uses a parametric bootstrap for inference. The third uses robust asymptotic approximations of the hazard rates in connection with an approximate parametric bootstrap. We compare the proposed methods with standard event count based TBSSs in simulation scenarios. Finally, we present results from a database study comparing two glucose-lowering medications among adults with type 2 diabetes.
