Bias, Confounding and interaction

Bias

Confounding

Random error / chance

What is epidemiological error?

Any trend in the collection, analysis, interpretation, publication or

review of data that can lead to conclusions that are systematically

different from the truth

What is Bias?

Errors can be differential (systematic) or non-differential (random)

Random error: use of invalid outcome measure that equally misclassifies cases and controls

Differential error: use of an invalid measures that misclassifies cases in one direction and misclassifies controls in another

Term 'bias' should be reserved for differential or systematic error

Bias is systematic error

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Chance vs Bias

Chance is caused by random errorBias is caused by systematic error

Errors from chance will cancel each other out in the long run (large sample size)

Errors from bias will not cancel each other out whatever the sample size

Chance leads to imprecise resultsBias leads to inaccurate results

Selection biasUnrepresentative nature of sample

Information (misclassification) biasErrors in measurement of exposure of disease

Confounding biasDistortion of exposure - disease relation by some other factor

(effect modification is not bias)

Types of Bias

Bias creates an association that is not true, but confounding describes an association that is true, but potentially misleading.

Selection Bias

Selective differences between comparison groups that impacts on relationship between exposure and outcome

Usually results from comparative groups not coming from the same study base and not being representative

of the populations they come from

Selection Bias Examples

Case-control study:Controls have less potential for exposure than cases

Outcome = brain tumour; exposure = overhead high voltage power linesCases chosen from province wide cancer registryControls chosen from rural areasSystematic differences between cases and controls

Case-Control Studies: Potential Bias

Selection Bias Examples

Cohort study:Differential loss to follow-up

Especially problematic in cohort studiesSubjects in follow-up study of multiple sclerosis may differentially drop out due to disease severity

Differential attrition selection bias

Selection Bias Examples

Self-selection bias:- You want to determine the prevalence of HIV infection- You ask for volunteers for testing- You find no HIV- Is it correct to conclude that there is no HIV in this location?

Selection Bias Examples

Healthy worker effect:Another form of self-selection bias“self-screening” process – people who are unhealthy “screen” themselves out of active worker population

Selection Bias Examples

Diagnostic or workup bias:Also occurs before subjects are identified for studyDiagnoses (case selection) may be influenced by physician’s knowledge of exposure

Example:- Case control study – outcome is pulmonary disease, exposure is smoking- Radiologist aware of patient’s smoking status when reading x-ray –may look more carefully for abnormalities on x-ray and differentially select cases

Selection biasUnrepresentative nature of sample

** Information (misclassification) bias **Errors in measurement of exposure of disease

Types of Bias

Information / Measurement / Misclassification Bias

Method of gathering information is inappropriate and yields systematic errors in measurement of exposures or outcomes

If misclassification of exposure (or disease) is unrelated to disease (or exposure) then the misclassification is non-differential

If misclassification of exposure (or disease) is related to disease (or exposure) then the misclassification is differential

Distorts the true strength of association

Information / Measurement / Misclassification Bias

Sources of information bias:

Subject variationObserver variationDeficiency of tools

Technical errors in measurement

Differential Misclassification Bias

Recall bias: Those exposed have a greater sensitivity for recalling exposure (reduced specificity)

- specifically important in case-control studies- when exposure history is obtained retrospectivelycases may more closely scrutinize their past history looking for ways to explain their illness- controls, not feeling a burden of disease, may less closely examine their past history

Those who develop a cold are more likely to identify the exposure than those who do not – differential misclassification- Case: Yes, I was sneezed on - Control: No, can’t remember any sneezing

Information / Measurement / Misclassification Bias

Reporting bias: Individuals with severe disease tends to have complete records therefore more complete information about exposures and greater association found

Individuals who are aware of being participants of a study behave differently (Hawthorne effect)

Controlling for Information Bias

- Blinding prevents investigators and interviewers from knowing case/control or exposed/non-exposed status of a given participant

- Form of survey mail may impose less “white coat tension” than a phone or face-to-face interview

- Questionnaireuse multiple questions that ask same information acts as a built in

double-check

- Accuracymultiple checks in medical records gathering diagnosis data from

multiple sources

Selection biasUnrepresentative nature of sample

Information (misclassification) biasErrors in measurement of exposure of disease

** Confounding bias **Distortion of exposure - disease relation by some

other factor

Types of Bias

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Birth order

Cases per 100 000 live births

EPIET (www)

Cases of Down Syndrome by Birth Order

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< 20 20-24 25-29 30-34 35-39 40+

Age groups

Cases per 100000 live

births

EPIET (www)

Cases of Down Syndrome by Age Groups

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Birth order Age groups

EPIET (www)

Cases of Down Syndrome by Birth Orderand Maternal Age

• A third factor which is related to both exposure and outcome, and which accounts for some/all of the observed relationship between the two

• Confounder not a result of the exposure– e.g., association between child’s birth rank (exposure) and Down

syndrome (outcome); mother’s age a confounder?– e.g., association between mother’s age (exposure) and Down

syndrome (outcome); birth rank a confounder?

Confounding

Exposure Outcome

Third variable

To be a confounding factor, two conditions must be met:

Be associated with exposure- without being the consequence of exposure

Be associated with outcome- independently of exposure (not an intermediary)

Confounding

Birth Order Down Syndrome

Maternal Age

Confounding

Maternal age is correlated with birth order and a risk factor even if birth order

is low

Birth Order

Down SyndromeMaternal Age

Confounding ?

Birth order is correlated with maternal age but not a risk factor in younger mothers

Coffee CHD

Smoking

Confounding

Smoking is correlated with coffee drinking and a risk factor even for those

who do not drink coffee

Coffee

CHDSmoking

Confounding ?

Coffee drinking may be correlated with smoking but is not a risk factor in non-

smokers

Alcohol Lung Cancer

Smoking

Confounding

Smoking is correlated with alcohol consumption and a risk factor even for

those who do not drink alcohol

Diet CHD

Cholesterol

Confounding ?

On the causal pathway

ConfoundingImagine you have repeated a positive finding of birth order association in Down syndrome or association of coffee drinking with CHD in another sample. Would you be able to replicate it? If not why?

Imagine you have included only non-smokers in a study and examined association of alcohol with lung cancer. Would you find an association?

Imagine you have stratified your dataset for smoking status in the alcohol - lung cancer association study. Would the odds ratios differ in the two strata?

Imagine you have tried to adjust your alcohol association for smoking status (in a statistical model). Would you see an association?

ConfoundingImagine you have repeated a positive finding of birth order association in Down syndrome or association of coffee drinking with CHD in another sample. Would you be able to replicate it? If not why?

You would not necessarily be able to replicate the original finding because it was a spurious association due to confounding.

In another sample where all mothers are below 30 yr, there would be no association with birth order.

In another sample in which there are few smokers, the coffee association with CHD would not be replicated.

ConfoundingImagine you have included only non-smokers in a study and examined association of alcohol with lung cancer. Would you find an association?

No because the first study was confounded. The association with alcohol was actually due to smoking. By restricting the study to non-smokers, we have found the truth. Restriction is one way of preventing confounding at the time of study design.

ConfoundingImagine you have stratified your dataset for smoking status in the alcohol - lung cancer association study. Would the odds ratios differ in the two strata?

The alcohol association would yield the similar odds ratio in both strata and would be close to unity. In confounding, the stratum-specific odds ratios should be similar and different from the crude odds ratio by at least 15%. Stratification is one way of identifying confounding at the time of analysis.

If the stratum-specific odds ratios are different, then this is not confounding but effect modification.

Confounding

If the smoking is included in the statistical model, the alcohol association would lose its statistical significance. Adjustment by multivariable modelling is another method to identify confounders at the time of data analysis.

Imagine you have tried to adjust your alcohol association for smoking status (in a statistical model). Would you see an association?

Confounding

For confounding to occur, the confounders should be differentially represented in the comparison groups.

Randomisation is an attempt to evenly distribute potential (unknown) confounders in study groups. It does not guarantee control of confounding.

Matching is another way of achieving the same. It ensures equal representation of subjects with known confounders in study groups. It has to be coupled with matched analysis.

Restriction for potential confounders in design also prevents confounding but causes loss of statistical power (instead stratified analysis may be tried).

Confounding

Randomisation, matching and restriction can be tried at the time of designing a study to reduce the risk of confounding.

At the time of analysis:Stratification and multivariable (adjusted) analysis can achieve the same.

It is preferable to try something at the time of designing the study.

Obesity Mastitis

Age

Confounding

In cows, older ones are heavier and older age increases the risk for mastitis. This association may appear as an obesity

association

Confounding

(www)

If each case is matched with a same-age control, there will be no association (OR for old age = 2.6, P = 0.0001)

No Confounding

(www)

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25-29

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Birth order Age groups

EPIET (www)

Cases of Down Syndrome by Birth Orderand Maternal Age

If each case is matched with a same-age control, there will be no association. If analysis is repeated after stratification by age, there will

be no association with birth order.

Confounding or Effect Modification

Birth Weight Leukaemia

Sex

Can sex be responsible for the birth weight association in leukaemia? - Is it correlated with birth weight?- Is it correlated with leukaemia independently of

birth weight?- Is it on the causal pathway? - Can it be associated with leukaemia even if birth

weight is low? - Is sex distribution uneven in comparison groups?

Confounding or Effect Modification

Birth Weight Leukaemia

Sex

Does birth weight association differ in strength according to sex?

Birth Weight Leukaemia

Birth Weight Leukaemia/ /

BOYS

GIRLS

OR = 1.8

OR = 0.9

OR = 1.5

Effect Modification

In an association study, if the strength of the association varies over different categories of a third variable, this is called effect modification.

The third variable is changing the effect of the exposure.

The effect modifier may be sex, age, an environmental exposure or a genetic effect.

Effect modification is similar to interaction in statistics.

There is no adjustment for effect modification. Once it is detected, stratified analysis can be used to obtain stratum-specific odds ratios.

Effect modifierBelongs to natureDifferent effects in different strataSimpleUsefulIncreases knowledge of biological mechanismAllows targeting of public health action

Confounding factorBelongs to studyAdjusted OR/RR different from crude OR/RRDistortion of effectCreates confusion in dataPrevent (design)Control (analysis)

HOW TO CONTROL FOR CONFOUNDERS?

• IN STUDY DESIGN…

– RESTRICTION of subjects according to potential confounders (i.e. simply don’t include confounder in study)

– RANDOM ALLOCATION of subjects to study groups to attempt to even out unknown confounders

– MATCHING subjects on potential confounder thus assuring even distribution among study groups

HOW TO CONTROL FOR CONFOUNDERS?

• IN DATA ANALYSIS…

– STRATIFIED ANALYSIS using the Mantel Haenszel method to adjust for confounders

– IMPLEMENT A MATCHED-DESIGN after you have collected data (frequency or group)

– RESTRICTION is still possible at the analysis stage but it means throwing away data

– MODEL FITTING using regression techniques

Cause-and-Effect Relationship

Grimes & Schulz, 2002 (www) (PDF)