1. The Aga Khan University
Confounding
Dr. Sunita Dodani
Assistant Professor
Family Medicine, CHS
The Aga Khan University
Pakistan
2/23/2019

2. Learning objectives To understand the role of confounders in a study
To learn relationship between an exposure, disease and potential confounding factors
To understand difference between confounding and effect modification
To learn methods to control confounding in study designs and in data analysis
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3. Performance objectives
After this lecture the student will be able to:
Differentiate the role of a confounder and a exposure in a study
Use methods to control effects of confounders in research projects
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4. Confounding
Confounding occurs when two factors are associated with each other, or “travel together” and the effect of one is confused with or distorted by the effect of the other.
A confounder is a variable which is associated with the exposure, and independent of that exposure is a risk factor of the disease
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5. Confounding Examples:
Study one: found an association with smoking and loss of hairs.
The study was confounded by age
Study two: found improved outcome for maternal centers when compared to hospitals
Study might be confounded by highly motivated volunteers that may have selected these centers as an option
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6. Confounding
Confounders are generally correlates of other causal factors
HSV-2
Sexual activity
HPV Cervical cancer
A confounder cannot be an intermediate link
in the causal pathway between exposure and disease
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7. Confounding
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In other words, confounding is a variable that is associated with the predictor variable and is a cause of the outcome variable
Aside from bias, confounding is often the likely alternative explanation to cause-effect and the most important one to try to rule out.
In contrast to bias, confounding can be controlled at several levels of a study
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8. Effect modification Effect modification is a type of interaction
When the strength of the relationship between two variables is different with respect to some third variable called effect modifier.
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9. Effect modification EXAMPLES 1
relationship between dose of thiazide and risk of sudden death.addition of K sparing drug modifies the effect at several doses.
effect modifier…….. K sparing drug
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10. Effect modification Example 2
People who take monoamine oxidase inhibitors (MAOI) are at risk of stroke if they eat certain foods such as cheese.
effect modifier………. MAOI
MAOI is not associated with eating cheese. This is not a confounder
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11. Coping with confounders
In the design phase
Investigators should be aware of confounders and able to control them
First list the variables (like age & sex) that may be associated with the predictor variable of interest as well as cause of the outcome
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12. Coping with confounders
Two design phase strategies
Specification
Matching
Both sampling strategies
Specification:
Design inclusion criteria that specify a value of the potential confounder and exclude everyone with a different value
e.g In coffee and MI , only non smokers could be included in the study.if an association observed b/w coffee and MI, it obviously could not be due to smoking
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13. Coping with confounders
Specification:
Advantages
Easily understood
Focuses only on subjects for the research question at hand
Disadvantages
Limits generalizability
May make it difficult to acquire adequate sample size
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14. Coping with confounders
Matching (mostly in case control studies)
Selection of cases and controls with matching values of the confounding variable
Pair wise matching
e.g in coffee drinking study as a predictor of MI, each case (a patient with MI) could be matched with one or more controls that smoked roughly the same amount as the case (10-20 cigarettes/day)
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15. Coping with confounders
Matching
Advantages:
Can eliminate influence of strong confounders
Can increase precision (power) by balancing the number of cases and controls in each stratum
May be sampling convenience making it easier to select controls
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16. Coping with confounders
Matching
Disadvantages
Time consuming
Requires early decision as to which variables are predictors and which are confounders
Requires matched analysis
Creates the danger of over matching( matching on a factor which is not a founder, thereby reducing power)
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17. Coping with confounders
In the Analysis
Stratification
Adjustment
Ensures that only cases and controls with similar level of a potential confounding variable are compared.
It involves segregating the subjects into strata.
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18. Coping with confounders
Stratification
Advantages
Easily understood
Flexible and reversible
Can choose which variable to stratify upon after data collection
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19. Coping with confounders
Stratification
Disadvantages
Number of strata limited by sample size needed for each stratum
Few co variables can be considered
Few strata per co variable leads to less complete control of confounding
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20. Coping with confounders
Statistical Adjustment
Several statistical techniques are available to adjust for confounders.
These techniques model the nature of the associations among the variable to isolate the effects of predictor variables and confounders
This require software for multivariate analysis
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21. Coping with confounders
Statistical Adjustment
Advantages
Multiple confounders can be controlled simultaneously
Information in continuous variables can be fully used
Flexible and reversible
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22. Coping with confounders
Statistical Adjustment
Disadvantages
Model may not fit
Inaccurate estimates of strength of effect (if model does not fit predictor-outcome relationship)
Results may be hard to understand
Relevant co variables must have been measured
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