Confounding

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

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

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

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

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

6. Confounding • Confounders are generally correlates of other causal factors • HSV-2 Sexual activity • HPVCervical cancer • A confounder cannot be an intermediate link in the causal pathway between exposure and disease

7. Confounding • Delete sample documenticons and replace with working document icons as follows: • From Insert Menu, select Object... • Click “Create from File” • Locate File name in “File” box • Make sure “Display as Icon” is checked • Click OK • Select icon • From Slide Show Menu, Select “Action Settings” • Click “Object Action” and select “Edit” • Click OK • 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

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.

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

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

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

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

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

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)

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

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)

17. Coping with confounders In the Analysis • Stratification • Adjustment Stratification • Ensures that only cases and controls with similar level of a potential confounding variable are compared. • It involves segregating the subjects into strata.

18. Coping with confounders Stratification Advantages • Easily understood • Flexible and reversible • Can choose which variable to stratify upon after data collection

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

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

21. Coping with confounders Statistical Adjustment Advantages • Multiple confounders can be controlled simultaneously • Information in continuous variables can be fully used • Flexible and reversible

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