Description: Berkson’s bias is a type of selection bias that occurs in statistical and epidemiological studies when the sample is chosen based on the outcome of interest, which can lead to erroneous conclusions. This bias typically arises in situations where individuals with a specific condition, such as a disease, are selected, while those without the condition are ignored. As a result, the relationship between exposure and outcome may be distorted, as the sample does not adequately represent the general population. This phenomenon is particularly relevant in studies involving data from hospitals or clinics, where patients seeking medical attention may have different characteristics than those who do not. Berkson’s bias can affect the internal validity of a study, as it may lead to overestimating or underestimating the association between variables. Therefore, it is crucial for researchers to be aware of this bias when designing studies and interpreting results, ensuring that the sample selection is as representative as possible of the general population to avoid misleading conclusions.
History: Berkson’s bias was named after epidemiologist Edward L. Berkson, who described it in a paper published in 1946. Berkson observed that when studying the relationship between disease and certain risk factors, data obtained from hospitalized patients might not reflect the reality of the general population. His work helped raise awareness about the importance of sample selection in epidemiological studies and the need to consider how the way participants are chosen can influence results.
Uses: Berkson’s bias is primarily used in the fields of epidemiology and medical research to identify and correct errors in study design. Researchers must consider this bias when selecting population samples, especially in studies involving data from hospitals or clinics. By recognizing the potential for this bias, researchers can apply appropriate statistical methods to adjust results and improve the validity of their conclusions.
Examples: An example of Berkson’s bias can be seen in studies analyzing the relationship between smoking and lung cancer using data from hospitalized patients. If only patients with lung cancer are included in the study, the relationship between smoking and the disease may be overestimated, as non-smokers without cancer are not represented in the sample. Another example could be a study investigating diabetes in hospital patients, where the results may not be applicable to the general population that does not seek medical attention.
