CONFOUNDER AND EFFECT MODIFICATION

Exposure Outcome

Third variable

Two aspects to consider

(1) Effect modifier(2) Confounding factors

- Useful information- Distortion of the effect

•Variation in the magnitude of measure of effect depending on a third variable

•Effect modification is not a bias but useful information

Effect modification/Interaction

Happens when RR or ORis different between subgroups of

population

Example:Oral contraceptives (OC) and myocardial infarction (MI): third variable smoking?

Case-control study, crude data Odds of exposure:

In cases: 693/307OC MI ControlsYes 693 320 In controls: 320/680

No 307 680Odds Ratio: 4.8

Total 10001000

Smokers

O C M I C o n t r o l s O R

Y e s 517 1 6 0 6 . 0N o 183 3 4 0T o t a l 7 0 0 5 0 0

N o n s m o k e r sO C M I C o n t r o l s O R

Y e s 176 1 6 0 3 . 0N o 124 3 4 0T o t a l 3 0 0 5 0 0

InterpretationWhat do those 2 different OR mean?

-Taking Oral Contraceptives is a risk factor for MyocardialInfarction, especially in people that smoke.

Among smokers, the odds of finding someone who takes OC among the cases is 6 times the odds of finding someone taking OC among the controls.

Among non smokers finding someone who takes OC among the cases is “only” 3 times the odds of finding someone taking OC among the controls.

TerminologyCrude OR= 4.8 are presented after univariate analysis: only looking at one variable, not taking other factors into account

Dividing the exposed and unexposed or cases and controls into subgroups for analysis is called ‘stratification’.

Stratum specific OR for smokers= 6.0

Stratum specific OR for non-smokers= 3.0

If two subgroups have (very) different OR or RR, there is ‘effect modification’ or ‘interaction’

Effect modifierIf effect modification is present: always show stratum specific OR or RR in order to be able to direct control measures

An effect modifier provides insight in how the disease works andis related to the ‘causal pathway’ of a disease

More examples:

Hypertension is more likely to cause myocardial infarction in people with hypercholesterolaemia then in people without hypercholesterolaemia•Malaria causes more deaths in pregnant women•Measles causes more deaths in malnourished children than in healthy children

Confounding

(Partially-) alternative explanation for an associationfound between an exposure and an outcome.

ExampleCase(permanentinjuries)

Control(nopermanent injuries)

Total

Exposed(Mercedes driver)

8 6 14

Non-exposed(other car)

5 7 12

Total13 13 26

Odds of exposure:

In cases: 8/5In controls: 6/7

Odds Ratio: 1.87

ExampleOdds Ratio: 1.87

The odds of finding someone who drives a Mercedes among the cases (with permanent injuries) is about 1.9 times (almost twice) the odds of finding someone that drives a Mercedes among the controls.

Assuming this OR is significant:

Would you advice Mercedes to urgently increase the safety fromtheir cars?

Example

Is there a difference in age between the cases and the controls?

Case Control Tot

<25 9(69%)

5(38%)

14

>25 4(31%)

8(62%)

12

Tot 13 13 26

Example

So perhaps age could be a confounder of the relationship between the exposure (Mercedes) and outcome (permanent injuries)

But how do we check if age was indeed a confounder?

Stratification

If you suspect confounding, you could control for it by analysing in 2 or more strata.

This is called stratification.

In the example this means that you would calculate odds and odds ratios separately for each value (or value group) in the variable “age”.

So we do separate analysis:in the people <25 years of age in the people >25 years of age

Stratification<25 years old: >25 years old:

case contr tot

Merc 8 4 12

Other 2 1 3

Tot 10 5 15

case contr tot

Merc 1 2 3

other 3 6 9

tot 4 8 12

Stratification<25 years >25 years

Odds in cases: 8/2 Odds in cases: 1/3

Odds in controls: 4/1 Odds in controls: 2/6

Odds Ratio = 1.0 Odds Ratio 1.0

These OR are called STRATUM SPECIFIC ODDS RATIOSThe OR in the whole population is called the CRUDE ODDS RATIO

What do the stratum specific OR mean?

StratificationInterpretation

The risk of permanent injuries in drivers <25 years is higher than in drivers >25 years, but it does not depend on the car type they are driving in (stratum specific odds ratios were 1).

Age was a confounder for the found relationship betweendriving a Mercedes and permanent injuries.

The ones driving the Mercedes were (on average) younger than the people driving another car. That made it appear as if car type and outcome were associated, where in fact it was age and outcome.

Confounding• Third variable

• Be associated with exposure• - without being the

consequence of exposure

• Be associated with outcome• - independently of

exposure• And to act as a confouder, the factor must be distributed unevenly between

cases and controls (case control study) or exposed and non exposed (cohort study)

To be a potential confounding factor, 2 conditions must be met:

Exposure Outcome

Examples of common potential confounders

Age SexSocio economic statusLiving conditions

Etc..

Depending on the relationship you are interested in you can guess which factors might be potential confounders but you cannot be sure beforehand..

Exposure

Hypercholesterolaemia

OutcomeMyocardial infarction

Third factorAtheroma

Any factor which is a necessary step in

the causal chain is not a confounder

Confounding(OR or RR)Distortion of measure of effect

because of a third factor

Should be prevented orNeeds to be controlled for

How to prevent/control confounding?

Prevention• Restriction to one stratum (cohort with only women, only

cases and controls between 30-50 years old)• Matching (if you have a male case, find a male control)

Control• Stratified analysis• Multivariate analysis (several confounding factors at the

same time)

Control for confounding

• But it is very important that you measure potential• confounders!! If we had not asked the drivers about• their age we could not have corrected for age group!

In contrast to bias: Even after data collection you can correct/adjust for confounding.

Effect modifierBelongs to natureDifferent effects in different strata SimpleUsefulIncreases knowledge of biological mechanismAllows targeting of PH action

Confounding factor Belongs to study

Stratefied OR different from crude OR

Distortion of effect Creates confusion in data

Prevent (in protocol phase <e.g. matching randomization>)

Control (in analysis phase <if you have measured it!!>)

To summarize:

To summarize: stratified analysis• Perform crude analysis• Measure the strength of association• List potential effect modifiers and confounders• Stratify data according to potential modifiers or

confounders• Check for effect modification• If effect modification present, show the data by

stratum• If no effect modification present, check for

confounding• If confounding, show adjusted data• If no confounding, show crude data

How to check:

Effect modifier: different effect in different strata

Confounding: different effect between the crudeand the ‘adjusted’ OR or RR

Overview tableCrude OddsRatio

Odds ratio instratum 1

Odds ratio instratum 2

Situation 1 4.0 4.0 4.0

Situation 2 4.0 1.0 1.1

Situation 3 4.0 2.0 6.0

Situation 4 4.0 2.0 3.0

Situation 5 4.0 2.5 2.5

Overview tableCrude OddsRatio

Odds ratio instratum 1

Odds ratio instratum 2 Confoun. Effect M

Situation 1 4.0 4.0 4.0

Situation 2 4.0 1.0 1.1 Situation 3 4.0 2.0 6.0 Situation 4 4.0 2.0 3.0 Situation 5 4.0 2.5 2.5