Thus in this example, submission rate for diagnosis was related to both age and diagnosis and biased the association between these two variables. Hence, only diseases whose frequency increased with age more rapidly than the average of other diseases were observed to have a significant association with age in the case-control study. The difference in results was due to the fact that many diseases of dairy cows increase in frequency with age, and thus the population of cows with diseases (the hospital population) was older than the average age in the source population. No association between age and mastitis was found in the case-control study yet in a subsequent longitudinal study in the population of cows giving rise to the data for the case-control study, the rate of mastitis was found to increase significantly with the cow’s age. It was later found that the autopsy series contained a disproportionately large number of tuberculosis cases because the latter were more likely to be autopsied, and when this was taken into account the association between tuberculosis and cancer disappeared.ĭocumented instances of Berkson’s fallacy in veterinary medicine are rare however, the effects of differential admission rates may have been observed, using hospital records, in a case-control study of the relationship between clinical mastitis and age of dairy cows. The initial study results indicated less tuberculosis in autopsied cancer victims than in autopsied people dying from diseases other than cancer thus suggesting a sparing effect of tuberculosis on cancer. A classic example of Berkson’s fallacy occurred in a study of the association between cancer and tuberculosis based on human autopsies. ![]() This phenomenon is often called Berkson’s fallacy after the person initially describing it. When these records are used in a subsequent study, the differential admission rate acts as a confounding variable and can bias the true association between the factor(s) and disease. Unfortunately, in practice only qualitative data are readily available to test how representative the groups are, and these deficits should be borne in mind when interpreting and extrapolating the results.Ī particular form of unrepresentative sample that gives rise to biased estimates of association arises when the rate of admission to the laboratory or clinic is associated with both the factor(s) of interest and the disease status. If there is doubt about the representativeness of the cases and/or controls, additional data should be obtained to help evaluate the situation. In particular, the prevalence of the factor(s) of interest in the available controls may not reflect its prevalence in the source population as it ought to, particularly if valid estimates of the importance of the association are desired. ![]() When both of the study groups are obtained by purposive selection from laboratory or clinic records, the cases and/or controls may not be representative of all cases and non-cases in the source population. ![]() When sampling from a large number of potential controls, random or random systematic selection is preferred, provided no matching of cases and controls is to be used. Whether explicit sampling of non-cases is used depends on the time and expense required to obtain the factor status for each unit selected. If little or no effort is required to obtain the history of exposure to the factor(s) of interest, then all non-cases or all non-cases with specified other diseases may be used as controls. Usually there are a very large number of potential controls.
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