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贝叶斯病例交叉设计 — 采用贝叶斯推断的自匹配流行病学研究

贝叶斯病例交叉设计是一种自匹配流行病学方法,用于估计时变暴露对急性事件风险的瞬时影响。每个病例都作为自己的对照,从而消除了时间稳定个体特征造成的混杂。贝叶斯推断取代或补充了经典的条件逻辑回归,使得能够纳入先验知识、在稀疏数据中实现更稳定的估计,并通过后验分布进行全面的不确定性量化。

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贝叶斯病例交叉设计
贝叶斯分层模型病例-交叉设计

来源

  1. Maclure, M. (1991). The case-crossover design: a method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153. DOI: 10.1093/oxfordjournals.aje.a115853
  2. Janes, H., Sheppard, L., & Lumley, T. (2005). Case-crossover analyses of air pollution exposure data: referent selection strategies and their implications for bias. Epidemiology, 16(6), 717–726. DOI: 10.1097/01.ede.0000181315.18836.9d

如何引用本页

ScholarGate. (2026, June 3). Bayesian Case-Crossover Study Design. ScholarGate. https://scholargate.app/zh/epidemiology/bayesian-case-crossover-design

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ScholarGateBayesian Case-Crossover Design (Bayesian Case-Crossover Study Design). 于 2026-06-15 检索自 https://scholargate.app/zh/epidemiology/bayesian-case-crossover-design · 数据集: https://doi.org/10.5281/zenodo.20539026