2014 Confounders (Dodik B).ppt

8/10/2019 2014 Confounders (Dodik B).ppt

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CONFOUNDERS

DALAM PENELITIAN

MK. EPIDEMIOLOGI GIZI

DEPT. GIZI MASYARAKAT, FEMA, IPB

2014


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Association between birth order and Down Syndrome

Data from Stark and Mantel (1966)


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Association between maternal age and Down Syndrome



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Association between maternal age and Down Syndrome,

stratified by birth order



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Bias

Confounding

Random error / chance

The three major threats to

internal validity:


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Causal Inference

One of the most important aspects

in clinical research is the inferencethat an association between an

exposure and outcome represents

a cause-effect relationshipSalah satu aspek yang paling

penting dalam penelitian klinis


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7

Criteria for causal inference

1. Strength of the association2. Consistency - replication

3. Specificity of the association

4. Temporality

5. Biological gradient

6. Plausibility7. Coherence

Kekuatan asosiasi


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Confounders


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Confounding is confusion, or mixing,

of effects; the effect of the exposure ismixed together with the effect of

another variable, leading to bias

"Confounding kebingungan, ataupencampuran, efek; efek paparan

dicampur bersama-sama dengan

pengaruh variabel lain, yang mengarah

untuk bias "Rothman KJ. Epidemiology. An introduction. Oxford: Oxford University Press, 2002

Latin: confundere = to mix together


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Confounder

It occurs when there is a confounder, which isassociated with both exposure and diseaseindependently. Hal ini terjadi ketika adaperancu, yang berhubungan dengan kedua

paparan dan penyakit secara mandiri.Exposure Disease

Confounder


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1. A confounder must be causally or non-causallyassociated with the exposurein the source population (studybase) being studied; 1. perancu harus kausal atau non-kausal yang berhubungan dengan paparan pada populasi

sumber (studi dasar) sedang dipelajari;

C

E

2. A confounder must be a causal risk factor (or a surrogatemeasure of a cause) for the diseasein the unexposedcohort; and 2. confounder harus menjadi faktor risikopenyebab (atau ukuran pengganti sebab a) untuk penyakitdalam kelompok tidak terpapar; dan

3. A confounder must not be an intermediate cause(not anintermediate step in the causal pathway between theexposure and the disease)

C

D

C DE X

A factor is a confounder if 3 criteriaare met:


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Why concern about confounding?


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Confounding pulls the observed association away

from the true associationIt can either exaggerate/over-estimatethe true

association (positive confounding), Example:

ORtrue= 1.0ORobserved= 3.0

or

It can hide/under-estimatethe true association

(negative confounding), Example:

ORtrue= 3.0

ORobserved= 1.0

Direction of Confounding


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Coffee CHD

Smoking

Examples of confounding

Smoking is correlated with coffee drinkingand a risk factor CHD even for those who

do not drink coffee


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Coffee

CHDSmoking

Confounding ?

Coffee drinking may be correlated with smokingbut is not a risk factor in non-smokers


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Alcohol Lung Cancer

Smoking

Confounding

Smoking is correlated with alcohol consumptionand a risk factor LC even for those who do not

drink alcohol


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Confounding Example

Case-control to study to examine theeffect of alcohol used on lung cancer

Lung Ca No lung Ca

Alcohol 90 60

No Alcohol 60 90

OR for Ca =

(a x d)/(b x c) =

(90 x90)/(60 x 60) =

2.25


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Confounding Example

Alcohol No

Alcohol

Smokers 120 30

Non

smokers

30 120

OR for alcohol andsmoking=(120x120)/(30x30) = 16.0

OR for lung cancer andsmoking=(100x100)/(50x50) = 4.0

Therefore smoking is related

to both lung cancer andalcohol use and thus maybea confounder

Lung Ca No Lung

Ca

Smokers 100 50

Nonsmokers

50 100

C f di E l


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Confounding Example

OR for lung cancer and

alcoholIn smokers =

(80x10)/(20x40) = 1.0

In non-smokers =

(10x80)/(20x40) = 1.0

So its safe to drink alcohol

(but not smoke) if youre

worried about lung cancer

Smokers Non-

smokersCa No

Ca

Ca No

Ca

Alcohol 80 40 10 20

No

Alcohol

20 10 40 80


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How to control confounders?


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Controlling for Confounding

1. Study design

Randomization

Restriction

Matching

2. Analysis

StratificationMultivariable analysis


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I. Confounding: study design

1. RandomizationA. Evenly distributes known and

unknown confounders

B. This is really why everyoneconsiders RCTs the gold standard

study design


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Exposure Disease (outcome)

Confounder

Randomization breaks any links

between treatment and prognostic factors

E D

CRandomization

X


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Randomization

Only for intervention studies

Definition: random assignment of study subjects toexposure categories

To control/reduce the effect of confounding variablesabout which the investigator is unaware (i.e. both

known and unknown confounders get distributed evenlybecause of randomization)

Randomization does not always eliminate confounding

Covariate imbalance in small trials

Misdistribution of potentially confounding variablesafter randomization

C f di d d i


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Confounding: study design

2. Restriction

Limits entrance into the study to

individuals who fall within a specified

or categories of the confounder

e.g., only including smokers in your study

on alcohol use and lung cancer

Obviously must know ahead of time whatis a confounder

Restriction


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Restriction

The distribution of the potential confounding factors doesnot vary across exposure or disease categories

An investigator may restrict study subjects to onlythose falling with specific level(s) of a confoundingvariable

Advantages of restriction

straightforward, convenient, inexpensive (but, reducesrecruitment!)

Disadvantages of restriction

Limits number of eligible subjects

Limits ability to generalize the study findings Residual confounding

Impossible to evaluate the relationship of interest atdifferent levels of the confounder

C f di t d d i


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Confounding: study design3. Matching

Match cases and controls on known confounders(one or more)

Example - have one smoker in the controls forevery smoker in the cases

Makes it harder to identify controls but may beuseful when the confounder would otherwise bevery rare in one of the groups (increasesstatistical power)

Involves selection of a comparison group that isforced to resemble the index group with respectto the distribution of one or more potentialconfounders


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Matching

Matching is commonly used in case-controlstudies

Match on strong confounder

Types: Pair (individual) matching

Frequency matching

The use of matching usually requires

special analysis techniques (e.g. matchedpair analyses and conditional logisticregression)

II Confounding: control at the analysis stage


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Confounding is one type of bias that can be adjustedin the analysis (unlike selection and information

bias)

Options at the analysis stage:Stratification

Multivariate methods

To control for confounding in the analyses,confounders must be measured in the study

II. Confounding: control at the analysis stage

Confounding: Adjusted in the Analysis


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Confounding: Adjusted in the Analysis

Stratified analysis

Like we did above with smokers vs. non-smokersand looking at differences between the ORs of thetwo groups

Cant be done easily if you have lots ofconfounders

Multivariable analysisVery commonly done

Not without its issues as well

How many confounders to include

Which to include

Which model of analysis

M lti i te A l sis


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Multivariate Analysis

Stratified analysis works best only in the presence of

1 or 2 confounders

If the number of potential confounders is large,

multivariate analyses offer the only real solution

Can handle large numbers of confounders(covariates) simultaneously

Based on statistical regression models

E.g. logistic regression, multiple linear regressionAlways done with statistical software packages

Control of confounding


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Control of confounding

hard to control unknown risk factors

These methods can control only known

potential confounders.

Only random assignment of exposurecan control for unknown potential

confounders.


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Whichever method you choose, you

have to know potential confoundersreported in previous studies.

Literature searching is important


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Common confounders

Age -- e.g., exposed persons are older

Sex -- e.g., more exposure in men

Risk factors - more exposed persons (orunexposed) smoke(-), exercise(+), eat

vegetables(+), use drugs(-), . . .

Effective control of confounding


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Effective control of confounding

requires:

Knowing the causal pathwaysKnowing all relevant causal factors

Measuring all relevant causal factors

accurately


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Crude vs. Adjusted Effects

Crude:does not take into account the effect of the confounder

Adjusted:

accounts for the confounderMantel-Haenszel method estimator

Multivariate analyses (e.g. logistic regression)

Confounding is likely when:

RRcrude =/= RRadjustedORcrude =/= ORadjusted

Stratified Analysis


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Crude 2 x 2 table

Calculate Crude OR (or RR)

Stratify by Confounder

Calculate ORs

for each stratum

If stratum-specific ORs are similar,

calculate adjusted OR (e.g. MH)

Crude

Stratum 1 Stratum 2

If Crude OR =/= Adjusted OR,

confounding is likely

If Crude OR = Adjusted OR,

confounding is unlikely

ORCrude

OR1 OR2

Stratified Analysis


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Conclusion


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ConclusionBias is a systematic error in collecting or

interpreting dataBias is a flaw in design and cannot be

analyzed away

Confounders are extraneous factors that

distort the relationship between theexposure and the outcome

Confounders may be adjusted away if theyare measured

Confounding can sometimes be preventedby proper study design


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Control confounding at the designingstage


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Strategy Advantages Disadvantages

Specification

Include onlynon-smokers.

Easily understood Limits generalizability

May limit sample size

Matching

Match smoking

status of casesand controls

Useful for

eliminating

influence of strong

constitutional

confounders like age

and sex

Decision to match must

be made when designing

and can have irreversible

adverse effects on analysis

Time consuming

Can not analyze

associations of matched

variables with the outcome

Control confounding at the analysisstage


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Strategy Advantages Disadvantages

Stratification

Conduct analysis

separately for

smokers and non-

smokers.

Easily understood

Reversible

May be limited by

sample size for eachstratum

Difficult to control

for multiple

confoundersStatistical

adjustment

Conduct

multivariate analysis

controlling

(adjusting) for

smoking status.

Multiple

confounders can be

controlled.

Reversible

Need advanced

statistical techniques

Results may be

difficult to understand

Documents

2014 Confounders (Dodik B).ppt