Feinstein A R, Walter S D, Horwitz R I
J Chronic Dis. 1986;39(7):495-504. doi: 10.1016/0021-9681(86)90194-3.
The bias described by Berkson arises as a mathematical phenomenon, caused by the probabilistic union of different rates of hospitalization for people with different medical phenomena. When the concept is extended to case-control studies, these rates will occur as hd for people with the target disease, he for people with the control condition, and hc for the separate effect of exposure to the suspected etiologic agent. An algebraic analysis of patterns of hospitalization and case-control selection demonstrates that Berkson's bias will be avoided if both cases and controls are chosen from the community or if he = 0. When the cases are chosen from hospitalized patients, the odds ratio will be biased if, as in the usual clinical situation, he not equal to 0. The odds ratio will be falsely elevated if the control groups are chosen from a community population rather than from hospitalized patients, and falsely lowered if the controls are hospitalized patients who do not have the target disease. If the control groups are chosen from patients hospitalized with specific comparison conditions, the odds ratio will be falsely elevated or lowered, depending on the relative magnitudes of hd and hc. In Berkson's mathematical model, the probabilistic calculations depend on the assumption that each of the exposed or diseased clinical conditions has an independent additive effect on hospitalization rates. In reality, however, the concurrence of two or more conditions of disease and exposure may synergistically affect the examining physician's nosocomial decisions and may thereby substantially change the hospitalization rates from what is expected mathematically. In creating hospitalization bias in case-control studies, these selective clinical decisions about referral to hospital may be more cogent than the probabilistic distinctions described by Berkson.
伯克森所描述的偏倚是一种数学现象，由不同医学现象患者的不同住院率的概率性联合所导致。当这一概念扩展到病例对照研究时，这些比率将表现为患有目标疾病者的hd、对照状况者的he以及接触可疑病因的单独效应的hc。对住院模式和病例对照选择模式的代数分析表明，如果病例和对照均从社区中选取，或者he = 0，那么伯克森偏倚将得以避免。当病例从住院患者中选取时，如果像通常临床情况那样he不等于0，那么比值比将会产生偏倚。如果对照组从社区人群而非住院患者中选取，比值比将会被错误地升高；如果对照组是没有目标疾病的住院患者，比值比将会被错误地降低。如果对照组从因特定对照状况住院的患者中选取，比值比将会被错误地升高或降低，这取决于hd和hc的相对大小。在伯克森的数学模型中，概率计算依赖于这样一个假设，即每种暴露或患病的临床状况对住院率都有独立的累加效应。然而，在现实中，两种或更多疾病和暴露状况的同时存在可能会协同影响检查医师的医院决策，从而可能使住院率与数学预期有很大不同。在病例对照研究中造成住院偏倚时，这些关于转诊到医院的选择性临床决策可能比伯克森所描述的概率差异更有说服力。
