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20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSENAARHUS
UNIVERSITYDEPARTMENT OF PUBLIC HEALTHAU
CAUSATION SEMINAR20 NOVEMBER 2014PARADOXES - SOLVED
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
A paradox is a statement that apparently contradicts itself and yet may be true.
• 3 will be addressed• The obesity paradox• The smoking paradox• Simpsons paradox
PARADOXES:
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
Obesity paradox – A survival advantage of being obese – in a
population diagnosed with a medical condition (Epidemiology
2014; 25: 454-61)
Better survival for obese has been demonstrated for diabetes,
CVD, hypertension, LC, MI, and more.
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
COLLIDER STRATIFICATION BIAS
Obesity
[Diabetes] Mortality
U
Obesity 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.
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
This 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 model
Dis A Dis B
[Hospitalization]
When studying hospital patients disease A and B will be associated even if
they occur independently in the population.
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
THE 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than low birth weight children of non smokers (Judea Pearl, unpublished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.
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
Smoking
LBW Death
U
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
In 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?
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
SIMPSONS PARADOX
more white than black hats
more white than black hats
Next day
All hats fell down on the floor now more black than white hats
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
E = treatment, R = recovery, G = gender
SIMPSONS´S PARADOX
Str E Recovery N Recovery rate
A11 + 20 20 40 50%
- 16 24 40 40%
M + 18 12 30 60%
- 7 3 10 70%
F + 2 8 10 20%
- 9 21 30 30%
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
If 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.
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
E
G
R
E has a direct effect on R
But the E – R association is
confounded by G.
G has a direct effect on R and
E.
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSENAARHUS
UNIVERSITYDEPARTMENT OF PUBLIC HEALTHAU
’PARADOXES’ IN ROUTINE EPIDEMIOLOGIC DECISION MAKING – WHAT SHOULD WE ADJUST FOR??
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
An example of analytical thinking guided by DAGs (after O. ARAH)
Analyses of BW and adult BP.
How to deal with current weight (CW).
CW
BW BP
Look at BW – BP association no adjustment.
If like this
CW
BW BP
Now adjustment for CW – but cause comes before the effect.
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
More likely
CW
U
BW BP
Now adjustment for CW.
CW
U1 U2
BW BP
No adjustments needed.
20 NOVEMBER 2014CAUSATION SEMINAR.
M.D., PHDJØRN OLSEN
DEPARTMENT OF PUBLIC HEALTHAARHUSUNIVERSITY
AU
CW
U1 U2
BW U3 BP
BW has no effect but will be associated with BP (confounder, U3).
CWU
BW BP
BW BP ass is biased – bias BW – CW – BP + BW – CW – U – BP
CWU
BW BP
Causal ass. BW – BP + BW – CW – BP but CW – U – BP should be closed.
AARHUSUNIVERSITYAU