4.2.4. confounding counterfactual
- 1. 49 Maldonado & Greenland, Int J Epi 2002;31:422-29 2014 Page 1 Fourth approach: Causality: counterfactual model ● Ideal “causal contrast” between exposed and unexposed groups: ● “A causal contrast compares disease frequency under two exposure distributions, but in one target population during one etiologic time period” ● If the ideal causal contrast is met, the observed effect is the “causal effect”
- 2. 52 2014 Page 2 What happens actually? RR = I / I causal exp unexp RR = I / I assoc exp substitute IDEAL ACTUAL
- 3. I exp Iunexp 2014 Page 3 Maldonado & Greenland, Int J Epi 2002;31:422-29 Exposed cohort Ideal counterfactual comparison to determine causal effects Counterfactual, unexposed cohort RR = I / I causal exp unexp “A causal contrast compares disease frequency under two exposure distributions, but in 5o0ne target population during one etiologic time period” “Initial conditions” are identical in the exposed and unexposed groups – because they are the same population!
- 4. 51 Iunexp Counterfactual, unexposed cohort Exposed cohort Substitute, unexposed cohort Isubstitute What happens actually? I exp counterfactual state is not observed 2014 Page 4 A substitute will usually be a population other than the target population during the etiologic time period - INITIAL CONDITIONS MAY BE DIFFERENT
- 5. 53 2014 Page 5 Maldonado & Greenland, Int J Epi 2002;31:422-29 Counterfactual definition of confounding ● “Confounding is present if the substitute population imperfectly represents what the target would have been like under the counterfactual condition” ● “An association measure is confounded (or biased due to confounding) for a causal contrast if it does not equal that causal contrast because of such an imperfect substitution” causal =/= RR RR assoc
- 6. Residual confounding • Confounding can persist, even after adjustment • Why? – All confounders were not adjusted for (unmeasured confounding) – Some variables were actually not confounders! – Confounders were measured with error (misclassification of confounders) – Categories of the confounding variable are improperly defined (e.g. age categories were too broad) 51 2014 Page 6
- 7. 55 Simulating the counter-factual comparison: Experimental Studies: RCT Randomization helps to make the groups “comparable” (i.e. similar initial conditions) with respect to known and unknown confounders Therefore confounding is unlikely at randomization - time t0 Eligible patients Treatment Randomization Placebo Outcomes Outcomes 2014 Page 50