1. 1 Causation in epidemiology, confounding and bias Imre Janszky Faculty of Medicine NTNU

2. 2 General notes on causation in epidemiology Groups vs. individuals Comparing an exposed population to the same population at the same time but without the exposure is a ”dream” and a useful theoretical framework for epidemiologists (Counterfactual outcomes)

3. 3 General notes on causation in epidemiology Causes could be: –Necessary or unnecessary –Sufficient or component Most causes: neither necessary nor sufficient

4. 4 Error is the difference between an observed value and the true (causal) value A. Random error or lack of precision: – Fluctuation in data caused by any factors that randomly affect the result of your measurement – Decreases when sample size increases Error in epidemiology

5. 5 B. Systematic error or lack of (internal) validity –Any process or effect that leads to nonrandom deviation of results from the “truth” –Nothing to do with the sample size NOTE: The concept of “external validity” refers to the generalizability of the findings, not related to errors in epidemiology. Error in epidemiology

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7. 7 Mixture of effects “C” can be a confounder for the association between the exposure and the disease if: –1. “C” is associated with the exposure, but not influenced by it –2. “C” is associated with the disease, but not influenced by it –Both criteria must be met There are alternative definitions Systematic error I. Confounding

8. 8 Randomization Restriction Stratification Statistical modeling etc Control of confounding

9. 9 Not an error! Mediation refers to the mechanism of a causal relationship (the exposure influences an intermediatory variable which in turn influences the disease occurrence) Interlude – 1 / mediation

10. 10 Not an error! Interaction happens if a 3rd factor is modifying the effect of exposure on disease occurrence (effect modification) Interaction is synergistic or antagonistic Interaction has a different meaning in epidemiology and in statistics Interlude – 2 / interaction

11. 11 Synergistic Interaction - example Example Alcohol (A), driving (D) and incidence rate for injuries (per 100 000 person hours)

12. 12 Synergistic Interaction - example Example Alcohol (A), driving (D) and incidence rate for injuries (per 100 000 person hours) A-: RR D =10, RD D =9

13. 13 Synergistic Interaction - example Example Alcohol (A), driving (D) and incidence rate for injuries (per 100 000 person hours) A-: RR D =10, RD D =9 A+: RR D =100, RD D =990

14. 14 Antagonistic interaction – example Use of strong acids (A), use of strong base (B) and rate for fatal suicide (per 10000 attempts)

15. 15 Antagonistic interaction – example Use of strong acids (A), use of strong base (B) and rate for fatal suicide (per 10000 attempts) B-: RR A =1000, RD A =999

16. 16 Antagonistic interaction – example Use of strong acids (A), use of strong base (B) and rate for fatal suicide (per 10000 attempts) B-: RR A =1000, RD A =999 B+: RR A =0.001, RD A = -999

17. 17 Stratification The process of arranging persons into groups (strata) based on their certain characteristics –Could be used for both checking for interaction –And for checking and dealing with confounding

18. 18 Systematic error II./1 Selection Bias Bias introduced by erroneous selection of participants Examples: Healthy worker bias Biased loss to follow up (discussed later with cohort studies) Inappropriate selection of controls in case- control studies (discussed later with case- control studies)

19. 19 Systematic error II./2 Information Bias The collected information is erroneous (misclassified) If misclassification of the disease does not depend on the exposure, or the misclassification of the exposure does not depend on the disease: non-differential misclassification Otherwise: differential misclassification

20. 20 Systematic error II./2 Information Bias Non-differential misclassification typically leads to underestimation of effects, rarely leads to overestimation and sometimes it does not even create a bias Differential misclassification can lead to over or underestimation of effects Confounders, effect modifiers or mediators can also be misclassified