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.