Is Probabilistic Bias Analysis Approximately Bayesian?
Is Probabilistic Bias Analysis Approximately Bayesian?
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DOI：
10.1097/ede.0b013e31823b539c
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发表时间：
2012-01-01
期刊：
影响因子：
5.4
通讯作者：
Gustafson, Paul
中科院分区：
文献类型：
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作者：
MacLehose, Richard F.;Gustafson, Paul
当根据事件病例状态确定暴露状态时，病例对照研究特别容易受到差异暴露错误分类的影响。概率偏差分析方法已被开发为根据暴露错误分类的敏感性和特异性来调整标准效应估计的方法。概率偏差分析中提倡的迭代采样方法与贝叶斯调整有着明显的相似之处。然而，它并不相同。此外，如果没有正式的理论框架（贝叶斯或频率论），概率偏差分析的结果仍然难以解释。我们从理论上和经验上描述了概率偏差分析在多大程度上可以被视为近似贝叶斯分析。尽管概率偏差分析和贝叶斯错误分类方法之间的差异可能很大，但这些情况通常涉及不切实际的先验规范，并且相对容易检测。除了这些特殊情况之外，病例对照研究中的概率偏差分析和贝叶斯暴露错误分类方法似乎表现同样出色。
Case-control studies are particularly susceptible to differential exposure misclassification when exposure status is determined following incident case status. Probabilistic bias analysis methods have been developed as ways to adjust standard effect estimates based on the sensitivity and specificity of exposure misclassification. The iterative sampling method advocated in probabilistic bias analysis bears a distinct resemblance to a Bayesian adjustment; however, it is not identical. Furthermore, without a formal theoretical framework (Bayesian or frequentist), the results of a probabilistic bias analysis remain somewhat difficult to interpret. We describe, both theoretically and empirically, the extent to which probabilistic bias analysis can be viewed as approximately Bayesian. Although the differences between probabilistic bias analysis and Bayesian approaches to misclassification can be substantial, these situations often involve unrealistic prior specifications and are relatively easy to detect. Outside of these special cases, probabilistic bias analysis and Bayesian approaches to exposure misclassification in case-control studies appear to perform equally well.