Berkson's Bias (A selection bias type) in Research #research #bias #shorts #researchmethodology
Channel: SPM & Research with Dr Rock Britto
Date: 22/08/2023

Berkson's bias, also known as Berkson's paradox or Berkson's fallacy, is a type of selection bias that can occur in hospital-based studies when the study population consists of individuals who are in the hospital for reasons unrelated to the exposure or outcome being studied. This bias can lead to distorted associations between variables due to the way the sample is selected. Here's an explanation of Berkson's bias:

Description:
Berkson's bias arises when hospital-based studies focus on patients who are admitted to the hospital for specific medical conditions. Since these patients are not representative of the general population, associations between exposure and outcome might be distorted. This is because individuals with both the exposure and the outcome are more likely to be hospitalized, creating a spurious negative association between the two.

Example:
Consider a study investigating the relationship between diabetes and tuberculosis (TB) risk using hospital-based data. If the study only includes hospitalized patients with TB, it might appear that diabetes is negatively associated with TB because diabetic patients are more likely to be hospitalized. However, this association is not reflective of the general population, where the actual relationship between diabetes and TB might be different.

Correction Strategies:
To mitigate Berkson's bias, researchers can consider the following strategies:

Include Outpatients: Include both hospitalized and non-hospitalized individuals with the condition of interest to create a more representative sample.
Control Group: Include a control group without the outcome of interest to better assess the relationship between exposure and outcome.
Use Population-Based Data: Utilize population-based data to avoid the selection bias inherent in hospital-based studies.
Sensitivity Analysis: Conduct sensitivity analyses to assess the potential impact of Berkson's bias on study results.

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