Confounding bias occurs when a third variable distorts the exposure-outcome relationship. Learn how to identify it with real-world examples critical for data science and pharma research.
Table of Contents
Question
What is an example of confounding bias?
A. Different interviewers administering the same survey differently
B. Using self-reported questionnaires that inaccurately record dietary intake
C. Including both healthy volunteers and hospitalized patients in a case-control study
D. The apparent effect of an exposure on an outcome is influenced by another variable that is related to both
Answer
D. The apparent effect of an exposure on an outcome is influenced by another variable that is related to both
Explanation
Confounding bias occurs when a third variable influences both the exposure and the outcome, distorting their apparent relationship.
Confounding bias occurs when an observed association between an exposure (e.g., a treatment) and an outcome is distorted by a third variable (a confounder) that independently influences both the exposure and outcome1711. This creates a false impression of causality or masks a true effect.
Example of Confounding Bias
Option D is correct because it directly describes this phenomenon. For instance:
- In a study linking coffee consumption to lung cancer, smoking acts as a confounder: smokers are more likely to drink coffee and develop lung cancer, creating a spurious association.
- Similarly, ice cream sales and common cold rates appear related, but warmer weather (the confounder) drives both variables independently.
Why Other Options Are Not Confounding Bias
Option A reflects interviewer bias (a type of information bias), where inconsistent data collection skews results.
Option B describes recall bias, where self-reported inaccuracies distort measurements.
Option C represents selection bias, arising from non-comparable groups (e.g., mixing healthy and hospitalized patients).
Key Characteristics of Confounding Bias
- The confounder must be a risk factor for the outcome and associated with the exposure.
- Failure to adjust for confounders (e.g., via stratification or regression) leads to biased effect estimates.
- Common confounders include age, socioeconomic status, and disease severity.
For example, in observational studies comparing medications, disease severity often confounds results because sicker patients receive stronger treatments. Without adjusting for severity, the treatment may falsely appear ineffective or harmful.
Confounding is distinct from bias: it reflects real but misleading associations, whereas bias stems from systematic errors in study design or measurement.
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