Flanders W D, Boyle C A, Boring J R
Centers for Disease Control, Division of Chronic Disease Control, Atlanta, GA 30333.
J Clin Epidemiol. 1989;42(5):395-401. doi: 10.1016/0895-4356(89)90127-3.
Berkson's bias reflects a statistical phenomenon in which differential hospitalization rates create an exposure distribution among hospitalized cases that differs from that among other cases. Importantly, previous work on Berkson's bias has not explicitly addressed the possibility of excluding prevalent or previously diagnosed cases--exclusions that are key features of many study designs. We indicate that the classically described bias differs from the corresponding bias in studies, such as incidence density studies, in which cases are restricted to those with recent diagnoses. We present methods that may be used to assess the magnitude of Berkson's bias in incidence-density studies. In many, though not all, situations the bias should be small and of little practical concern.
伯克森偏倚反映了一种统计现象，即不同的住院率导致住院病例中的暴露分布与其他病例中的暴露分布有所不同。重要的是，先前关于伯克森偏倚的研究并未明确探讨排除现患病例或先前已诊断病例的可能性——而这些排除是许多研究设计的关键特征。我们指出，经典描述的偏倚与某些研究（如发病率密度研究）中的相应偏倚不同，在发病率密度研究中，病例仅限于近期诊断的患者。我们提出了可用于评估发病率密度研究中伯克森偏倚大小的方法。在许多（尽管不是所有）情况下，这种偏倚应该较小，实际影响不大。
