Presentation on theme: "Causation seminar 20 November 2014 Paradoxes - solved"— Presentation transcript:
1Causation seminar 20 November 2014 Paradoxes - solved
2Paradoxes:A paradox is a statement that apparently contradicts itself and yet may be true.3 will be addressedThe obesity paradoxThe smoking paradoxSimpsons paradox
3Obesity paradox – A survival advantage of being obese – in a population diagnosed with a medical condition (Epidemiology 2014; 25: )Better survival for obese has been demonstrated for diabetes, CVD, hypertension, LC, MI, and more.
4Collider stratification bias Obesity[Diabetes] MortalityUObesity is a less ‘dangerous’ cause of death in patients with diabetes than other causes of diabetes.It is better to have obesity as a cause of diabetes than pancreatic cancer!if you did not have diabetes because of a causal field including obesity, you had another causal filed leading to diabetes and mortality in the group may be higher. Diseases have causes.
5This paradox – and the next and many others - is the result of collider stratification bias. By conditioning on the collider you link the causes of the collider. You then compare obese with those having a different cause of diabetes like pancreatic cancer.A Berksonian bias modelDis A Dis B[Hospitalization]When studying hospital patients disease A and B will be associated even if they occur independently in the population.
6The birth weight paradox The Paradox:Low birth weight children to smoking mothers have lower infant mortality rates than low birth weight children of non smokers (Judea Pearl, unpublished manuscript 2014 – in Lord´s Paradox Revisited.LBW children have a MRR of 100 and smoking causes LBW.Smoking is not beneficial but again we see collider stratification bias.
8In counterfactual language: How would the mortality rate of babies of smoking mothers compare with that of non smokers had there been no pre-existing uncontrolled difference in birth weight?
9Simpsons paradox more white than black hats Next day All hats fell down on the floor now more black than white hats
10Simpsons´s paradox E = treatment, R = recovery, G = gender Str E Recovery rateA11+204050%-162440%M18123060%731070%F2820%92130%E = treatment, R = recovery, G = gender
11If you think data speak for themselves, call a shrink not a statistician. Data are passive vehicles of information that needs to be understood in the context driven by logical reasoning.It is not surprising that we see a change in association between 2 variables when a third variable is controlled for– we see this all the time– we call this effect measure modification– and even the direction of association can change.
12E G RE has a direct effect on RBut the E – R association isconfounded by G.G has a direct effect on R and E.
13’paradoxes’ in routine epidemiologic decision making – what should we adjust for??
14An example of analytical thinking guided by DAGs (after O. ARAH) Analyses of BW and adult BP.How to deal with current weight (CW).CWBW BPLook at BW – BP association no adjustment.If like thisNow adjustment for CW – but cause comes before the effect.
15More likelyCWUBW BPNow adjustment for CW.U1 U2No adjustments needed.
16CWU1 U2BW U3 BPBW has no effect but will be associated with BP (confounder, U3).UBW BPBW BP ass is biased – bias BW – CW – BP + BW – CW – U – BPCausal ass. BW – BP + BW – CW – BP but CW – U – BP should be closed.