1. Confounding and effect modification
Preben Aavitsland

2. Can we believe the result?
Rice
Salmonellosis
OR = 3.9

3. Systematic error Does not decrease with increasing sample size
Selection bias
Information bias
Confounding

4. Confunding - 1
“Mixing of the effect of the exposure on disease with the effect of another factor that is associated with the exposure.”
Exposure
Disease
Confounder

5. Confounding - 2 Key term in epidemiology
Most important explanation for associations
Always look for confounding factors
Surgeon
Post op inf.
Op theatre I

6. Criteria for a confounder
1 A confounder must be a cause of the disease (or a marker for a cause)
2 A confounder must be associated with the exposure in the source population
3 A confounder must not be affected by the exposure or the disease
Umbrella
Less tub. 2 1
Class 3

7. Downs’ syndrome by birth order

8. Find confounders
“Second, third and fourth child are more often affected by Downs’ syndrome.”
Many children
Downs’
Maternal age

9. Downs’ syndrome by maternal age

10. Downs’ syndrome by birth order and maternal age groups

11. Find confounders
”The Norwegian comedian Marve Fleksnes once stated: I am probably allergic to leather because every time I go to bed with my shoes on, I wake up with a headache the next morning.”
Sleep shoes
Headache
Alcohol

12. Find confounders
“A study has found that small hospitals have lower rates of nosocomial infections than the large university hospitals. The local politicians use this as an argument for the higher quality of local hospitals.”
Small hosp
Few infections
Well patients

13. Controlling confounding
In the design
Restriction of the study
Matching
Before data collection!
In the analysis
Restriction of the analysis
Stratification
Multivariable regression
After data collection!

14. Restriction
Restriction of the study or the analysis to a subgroup that is homogenous for the possible confounder.
Always possible, but reduces the size of the study.
Umbrella
Less tub.
Lower
class
Class

15. Restriction We study only mothers of a certain age Many children
Downs’
35 year old mothers

16. Matching
“Selection of controls to be identical to the cases with respect to distribution of one or more potential confounders.”
Many children
Downs’
Maternal age

17. Disadvantages of matching
Breaks the rule: Control group should be representative of source population
Therefore: Special ”matched” analysis needed
More complicated analysis
Cannot study whether matched factor has a causal effect
More difficult to find controls

18. Why match? Random sample from source population may not be possible
Quick and easy way to get controls
Matched on ”social factors”: Friend controls, family controls, neighbourhood controls
Matched on time: Density case-control studies
Can improve efficiency of study
Can control for confounding due to factors that are difficult to measure

19. Should we match? Probably not, but may:
If there are many possible confounders that you need to stratify for in analysis

20. Stratified analysis Calculate crude odds ratio with whole data set
Divide data set in strata for the potential confounding variable and analyse these separately
Calculate adjusted (ORmh) odds ratio
If adjusted OR differs (> 10-20%) from crude OR, then confounding is present and adjusted OR should be reported

21. Procedure for analysis
When two (or more) exposures seem to be associated with disease
Choose one exposure which will be of interest
Stratify by the other variable
Meaning. Making one two by two table for those with and one for those without the other variable (for example, one table for men and one for women)
Repeat the procedure, but change the variables

22. Example Salmonella after wedding dinner
Disease seems to be associated with both chicken and rice
But many had both chicken and rice

23. Confounding
Is rice a confounder for the chicken  salmonellosis association?
Stratify: Make one 2x2 table for rice-eaters and one for non-rice-eaters (e.g. in Episheet)
Chicken
Salmonellosis
Rice

24. No confounding Because:
OR for chicken alone = ORmh for chicken ”controlled for rice”

25. Confounding
Is chicken a confounder for the rice  salmonellosis association?
Stratify: Make one 2x2 table for chicken-eaters and one for non-chicken-eaters (e.g. in Episheet)
Rice
Salmonellosis
Chicken

26. Confounding
Because:
OR for rice alone = ORmh for rice ”controlled for chicken”
Not 3,9

27. Conclusion Chicken is associated with salmonellosis
Rice is not associated with salmonellosis
confounding by chicken because many chicken-eaters also had rice
rice only appeared to be associated with salmonellosis
Stratification was needed to find confounding
Compare crude OR to adjusted OR (ORmh)
If > 10-20% difference  confounding!

28. Multivariable regression
Analyse the data in a statistical model that includes both the presumed cause and possible confounders
Measure the odds ratio OR for each of the exposures, independent from the others
Logistic regression is the most common model in epidemiology
But explore the data first with stratification!

29. Controlling confounding
In the design
Restriction of the study
Matching
In the analysis
Restriction of the analysis
Stratification
Multivariable methods

30. Effect modification
Definition: The association between exposure and disease differ in strata of the population
Example: Tetracycline discolours teeth in children, but not in adults
Example: Measles vaccine protects in children > 15 months, but not in children < 15 months
Rare occurence