Ændr 2. linje i overskriften til AU Passata Light 20 NOVEMBER 2014 CAUSATION SEMINAR. M.D., PHD JØRN OLSEN AARHUS UNIVERSITY DEPARTMENT OF PUBLIC HEALTH

20 NOVEMBER 2014CAUSATION SEMINAR.

M.D., PHDJØRN OLSENAARHUS

UNIVERSITYDEPARTMENT OF PUBLIC HEALTHAU

CAUSATION SEMINAR20 NOVEMBER 2014PARADOXES - SOLVED


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:




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.




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.




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.




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than low birth weight children of non smokers (Judea Pearl, unpublished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.




AU

Smoking

LBW Death

U




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?




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




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%




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.




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.


M.D., PHDJØRN OLSENAARHUS

UNIVERSITYDEPARTMENT OF PUBLIC HEALTHAU

’PARADOXES’ IN ROUTINE EPIDEMIOLOGIC DECISION MAKING – WHAT SHOULD WE ADJUST FOR??




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.




AU

More likely

CW

U

BW BP

Now adjustment for CW.

CW

U1 U2

BW BP

No adjustments needed.




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

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Ændr 2. linje i overskriften til AU Passata Light 20 NOVEMBER 2014 CAUSATION SEMINAR. M.D., PHD JØRN OLSEN AARHUS UNIVERSITY DEPARTMENT OF PUBLIC HEALTH