An analysis of Berkson's bias in case-control studies

Abstract

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.