基于贝叶斯潜在因子建模与偏差校正的多信号安全监测
Multi-Signal Safety Surveillance with Bayesian Latent Factor Modeling and Bias Correction
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中文总结 AI 辅助
该研究针对观察性医疗数据多信号安全监测的复杂问题，提出结合经验偏差校正与低秩潜在因子建模的多信号贝叶斯序贯监测框架，经美国保险理赔数据库的疫苗安全监测研究验证有效。
中文摘要 AI 辅助
安全监测日益涉及对观察性医疗保健数据中大量暴露-结局信号的重复监测，其中稀疏信息、相关信号间的依赖性及系统误差会使推断变得复杂。现有框架通常要么使用阴性对照校正残余偏差，要么在暴露-结局对间借用信息，但不会同时兼顾两者。我们提出一种多信号贝叶斯序贯监测框架，将经验偏差校正与低秩潜在因子建模相结合。在每次分析时间，分层贝叶斯模型从假设具有零潜在效应的阴性对照结局中学习暴露特异性偏差分布；基于这些分布，在感兴趣的暴露与结局间估计低秩潜在因子，以在相关信号间共享信息。随着新数据积累，后验推断会序贯更新，生成多个被监测信号效应量的经偏差校正的后验摘要。我们使用美国大型保险理赔数据库，在上市后疫苗安全监测研究中验证了该方法。
英文摘要
Safety surveillance increasingly involves repeated monitoring of many exposure-outcome signals in observational healthcare data, where sparse information, dependence across related signals, and systematic error can complicate inference. Existing frameworks typically focus on either correcting residual bias using negative controls or borrowing information across exposure-outcome pairs, but not both. We propose a multi-signal Bayesian sequential surveillance framework that integrates empirical bias correction with low-rank latent factor modeling. At each analysis time, a hierarchical Bayesian model learns exposure-specific bias distributions from negative control outcomes assumed to have null latent effects. Conditional on these distributions, low-rank latent factors are estimated across exposures and outcomes of interest to share information across correlated signals. As new data accrue, posterior inference is updated sequentially, yielding bias-corrected posterior summaries of effect sizes across multiple monitored signals. We illustrate the method in a postmarket vaccine safety surveillance study using a large US insurance claims database.
