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Confounding and BiasAya Goto

Nguyen Quang Vinh

Key concepts Confounding Indicative of true

association. Can be controlled at the designing or analysis stage.

Bias Should be minimized at the designing stage.

Random errors Is the nature of quantitative data.

Non-differential (random) misclassification Is the nature of (inaccurate) measurement.

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EXAMPLES OF RANDOM ERROR, BIAS, MISCLASSIFICATION AND CONFOUNDING IN

THE SAME STUDY: In a cohort study, babies of women who bottle feed and women who breast feed are compared, and it is found that the incidence of gastroenteritis, as recorded in medical records, is lower in the babies who are breast-fed.

This is a revised version of an example given in a Supercourse lecture by Dr. Nigel Paneth from Michigan State University.http://www.pitt.edu/~super1/assist/topicsearch.htm

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EXAMPLE OF CONFOUNDING The mothers of breast-fed babies are of higher social class, and the babies thus have better hygiene, less crowding and perhaps other factors that protect against gastroenteritis. Crowding and hygiene are truly protective against gastroenteritis, but we mistakenly attribute their effects to breast feeding. This is called confounding because the observation is correct, but should be carefully interpreted to foresee the truth.

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EXAMPLE OF BIAS

The medical records of bottle-fed babies only are less complete (perhaps bottle fed babies go to the doctor less) than those of breast fed babies, and thus record fewer episodes of gastro-enteritis in them only. This is called bias because the observation itself is in error.

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EXAMPLE OF RANDOM ERROR

By chance, there are more episodes of gastroenteritis in the bottle-fed group in the study sample.

EXAMPLE OF RANDOM MISCLASSIFICATION

Lack of good information on feeding history results in some breast-feeding mothers being randomly classified as bottle-feeding, and vice-versa. If this happens, the study finding underestimates the true RR.

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Pregnancy intention

Maternal confidence

“Mothers with unintended pregnancy tend to lose confidence.”

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Pregnancy intention

Maternal confidence

Fist-time motherhood

Confounding

It occurs when there is a confounder, which is associated with both exposure and disease independently.

Exposure Disease

Confounder

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Does drinking coffee increase the risk of myocardial infarction?

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Coffee MI

Smoking

http://www.amazon.co.jp/Coffee-Cigarettes-Roberto-Benigni/dp/B0001XAO7U

Strategy Advantages DisadvantagesSpecification“Include only non-smokers.”

• Easily understood • Limits generalizability• May limit sample size

Matching“Match smoking status of cases and 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

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Control confounding at the designing stage

Strategy Advantages DisadvantagesStratification“Conduct analysis separately for smokers and non-smokers.”

• Easily understood• Reversible

• May be limited by sample size for each stratum• Difficult to control for multiple confounders

Statistical 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

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Control confounding at the analysis stage

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“Whichever method you choose, you have to know potential confounders reported in previous studies.”

Literature searching is important

BiasSelection biasMeasurement bias-Recall bias-Observer bias

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Selection biasEspecially in case-control study, it occurs when cases and controls are selected related to exposure status.

Cases (In-patients with thromboembolism)

Controls

Because physicians were already aware of a possible relationship between thromboembolism and OC use, patients with the disease were more likely admitted if they were using OC.

Example: Hospital-based case-control study on relationship of OC use and thromboembolism

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Obtained results: Relationship between thromboembolism and OC will be exaggerated.

Method to minimize this selection bias Prepare and follow an established

objective diagnostic criteria independent of exposure status for selecting cases.

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NOTE for advance learners: Sampling is a different issue

from selection bias.

Pregnant women In HCMC

Pregnant women delivering atTu Du Hosp.

Sampling influences generalizability (external validity) of the obtained results.

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Prevalence of postpartum depression at Tu Du = Prevalence in HCMC?

All patientsPatients at a hospital x

Selection bias influences internal validity of the obtained results.

Cases: Breast cancer patients

Controls: Patients at the same hospital. (Except who have cardiovascular diseases to which Reserpine is likely to be prescribed.)

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Is Reserpine a cause of breast cancer?

Horwitz RI, Feinstein AR. Exclusion bias and the false relationship of reserpine and breast cancer. Arch Intern Med. 1985;145(10):1873-5.

Recall biasEspecially in a case-control study, it occurs when disease status influences subjects’ recall of exposure status. Example: Case-control study on relationship of prenatal infections and congenital malformations.

Cases (mothers of babies with defect)

Controls (mothers of healthy babies)

They recall better about prenatal episode of infections since they tend to think about possible causes of their babies illness.

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Obtained results: Relationship between baby’s defect and prenatal infection will be exaggerated.

Method to minimize this recall biasConsider using a hospital control.

Observer biasEspecially in a case-control study, it occurs when knowledge of disease status influences observer’s recording of exposure status. Example: Case-control study on relationship of OC & thromboembolism

Cases (Out-patients with thromboembolism)

Controls

Physicians ask more carefully about OC use to women with symptoms of thromboembolism.

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Obtained results: Relationship between thromboembolism and OC will be exaggerated.

Method to minimize this observer bias • Hire interviewers.• If investigators themselves are doing the

interview, do it before diagnosing.• Do not analyze the data until you collect all data.

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