Causal Inference, Bias and Confounding

Causal Inference

  • Causal Inference: Inferring or concluding the cause of a disease from evidence.
  • Evidence of a causal relationship between exposure and disease includes:
    • Temporality: The cause must precede the effect in time.
    • Consistency: The relationship is consistently seen across numerous studies.
    • Strength of Association: A strong relationship between exposure and disease increases the likelihood of causality.
    • Biological Coherence: Biological evidence supports the exposure causing the disease (e.g., mesothelioma caused by asbestos).

Consistency Example

  • Studies showing second-hand cigarette smoke increases lung cancer risk for non-smokers.
  • Takeshi Hirayama's 1981 study demonstrated increased lung cancer risk in non-smoking Japanese women married to smokers, compared to those married to non-smokers.
  • Since Hirayama’s original paper, 37 studies of passive smoking and lung cancer have been published.

Strength of Association Example

  • A dose-response relationship exists between latitude of sunlight exposure and skin melanoma.
  • Skin melanoma risk is greatest at the equator and decreases towards the North and South poles.

Relationships May Only Appear Causal

  • Reasons why a relationship between exposure and disease may appear causal, but in fact is not:
    • Chance variations between samples of a population.
    • Bias: Influence or prejudice of the data.
      • Selection bias: Participants selectively opt out of a study.
      • Measurement bias: Inaccurate data measurement (e.g., from faulty equipment).
      • Information bias: Data collected inconsistently (e.g., varying questions).
      • Recall bias: Incomplete or inaccurate recall by study participants.
    • Confounding: A third factor distorts the association between the studied cause and effect.
      • Example: Age is a confounder in carcinogen studies because cancer likelihood increases with age.

Prevent Making False Associations

  • Methods to prevent false associations between exposure and disease by reducing chance, bias, and confounding:
    • Reduce error by chance: Increase sample size.
    • Minimize bias:
      • Improve questionnaire response rates (e.g., by making questionnaires less time-consuming).
      • Ensure quality control of monitoring equipment (e.g., comparing results to a standard).
      • Use a standardized set of questions in outbreak investigations.
      • Aid recall by asking questions promptly after an event.

Confounding May Be Minimized By

  • Matching: Select controls with similar characteristics to the cases.
    • Example: Selecting controls with the same age as the cancer cases.
  • Stratification: Create subsets of case and control groups to separate out the third factor effects.
    • Example: Studying lung cancer due to radon exposure in underground miners, where smoking is a confounder.
    • Create subsets:
      • Non-smoking miners with lung cancer.
      • Smoking miners with lung cancer.
    • Separate out the confounder (smoking); lung cancer among non-smokers is likely due to radon.
  • Randomization: Ensure samples are randomly selected.