Confounding Flashcards
What is confounding
- Different explanations in the data cannot be differentiated
o 3rd factor has a relationship with exposure and outcome – ie people who drink are likely to smoke, and smoking actually causes the lunger cancer not alcohol
How is confounding fixed
- Can be adjusted for in the analysis (unlike bias)
Characteristics of confounding
o Must be associated with both the exposure and the outcome
o But must not be a path variable
o It can increase or decrease any observed effect
Possibilies for the relationship between exposure and outcome
- No association
- E is directly associated with O
- E is inversely associated with O
- Third variable partly explains the association
How do you identify confounders
- Literature search of similar studies
- Biological plausibility
- Univariate analysis to test association with exposure/outcome
o Usually table 1, sociodemographic comparison table
How do you control for confounders in design phase
o Restriction
Restricted inclusion criteria – homogenous study for potential confounder
o Randomisation
o Matching
Limited use outside of case control studies
How do you control for confounders in the analysis phase
o Stratification
Assess exposure-outcome relationship independently for different confounder strata
o Standardisation
Model exposure-outcome relationship weighted by the potential confounder
o Adjustment
Most commonly used procedure
Model relationship while controlling for potential confounders (multivariate modelling)
What is stratification
Assess exposure-outcome relationship independently for different confounder strata
What is standardisation
Model exposure-outcome relationship weighted by the potential confounder
What is adjustment
Model relationship while controlling for potential confounders (multivariate modelling)
What is residual confounding
Residual confounding = distortion between association between exposure and outcome even after controlling for confounding in design and analysis
Disadvantages of restriction
Limits generalisability
Difficult if more than 1 confounder
Disadvantages matching
Increasingly difficult with more confounders
Cannot examine effect of matched variables
Disadvantages randomisation
Limited use in observational studies