Epidemiology and applied statistics review module 4 causation bias confounding
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Epidemiology and Applied Statistics Review Module 4 – Causation, Bias & Confounding. American College of Veterinary Preventive Medicine Review Course Katherine Feldman, DVM, MPH, DACVPM [email protected] 301-314-6820. Plan. Students review modules on their own

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Epidemiology and applied statistics review module 4 causation bias confounding

Epidemiology and Applied Statistics ReviewModule 4 – Causation, Bias & Confounding

American College of Veterinary Preventive Medicine Review Course

Katherine Feldman, DVM, MPH, DACVPM

[email protected]



  • Students review modules on their own

  • Send questions by email to Katherine Feldman (kfe[email protected]) by Friday March 23 a.m.

  • Conference call Friday March 23 2-3 p.m.

    • Watch email and Blackboard for conference call details


  • Gordis L. Epidemiology, 3rd ed. Elsevier Saunders, Philadelphia, 2004.

    • $47.95 from Amazon.com

  • Norman GR, Streiner DL. PDQ statistics, 3rd ed. BC Decker Inc., Hamilton, 2003.

    • $17.79 from Amazon.com

Explanations for an association
Explanations for an Association

  • If we determine an association between an exposure and an outcome exists (i.e., there is a significant OR or RR), there are a number of possibilities to explain the association

    • It’s REAL

    • SELECTION bias (who gets into the study) accounts for it

    • INFORMATION bias (how information is collected for the study) accounts for it

    • It’s CONFOUNDING the association between another risk factor and disease

    • It’s due to error in conducting the study

    • It’s due to CHANCE


  • Any systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure’s effect on the risk of disease

  • Two major categories

    • Selection bias

    • Information bias

Selection bias
Selection Bias

  • Can occur whenever identification of individual subjects for inclusion results in a mistaken estimate of the measure of effect

  • More simply, selection bias is a problem with who is in the study

  • Some examples

    • Detection (akasurveillanceordiagnostic) bias – persons followed more closely by health care providers because of some exposure are more likely to be diagnosed as a case

    • Self-selection bias – subjects differentially self-refer

    • Non-response bias – subjects differentially do not participate

    • Inappropriate comparison group – comparison group does not appropriately represent the population from which cases arose

Information bias
Information Bias

  • Systematic error in the collection of exposure or outcome data that results in a mistaken estimate of an exposure’s effect on the risk of disease

  • More simply, a problem with the information you collect

  • Some examples

    • Questionnaire faults

    • Interviewer bias – An interviewer interjects his or her bias into interview

    • Bias from surrogate interviews – e.g., parent or family member

    • Respondent errors – recall bias and other issues with recall

    • Bias from abstracting records

    • Misclassification bias – when either exposure or disease outcome is misclassified (cases are misclassified as controls or controls as cases, etc.)


  • The distortion of an exposure-disease association by the effect of some third factor – the confounder

  • The association between the exposure and outcome is distorted by the association between the exposure and the confounder, and the association between the disease and the confounder

  • Confounding results in a mistaken estimate of an exposure’s effect on the risk of disease

  • Unlike most other types of bias, confounding can sometimes be eliminated during data analysis


  • To be a confounder, a variable must be

    • Associated with the outcome (i.e., is a risk factor for the disease)

    • Associated with the exposure, but is not a result of the exposure (i.e., is not on the causal pathway from exposure to outcome)





  • An apparently strong association between exposure and outcome observed in a ‘crude’ analysis can be partially or even wholly due to confounding

  • Less commonly, confounding can mask an association

  • In epidemiology, confounding is a nuisance and one tries to eliminate the effects of (‘control for’) confounding

Effect modification
Effect Modification

  • Aka interaction

  • The association between exposure and disease is different for different levels of a third variable

  • In other words, the effect of the exposure on the disease is modified by the third variable

  • Effect modification is a finding to be reported, not a bias to be eliminated; it is a “natural phenomenon” that exists independently of the study design

Confounding vs interaction
Confounding vs. Interaction

  • Confounding is extremely common, because it is an artifact of the data

  • True effect modification usually represents a biologic phenomenon and is much less common


  • Having determined that an association between exposure and outcome is real, the next step is to consider if it is causal - does the exposure cause disease?

  • Numerous criteria and theories on causality

  • Causal criteria as proposed by US Dept of Health, Education and Welfare: Smoking and Health: Report of the Advisory Committee to the Surgeon General. 1964.

Guidelines for assessing causality
Guidelines for Assessing Causality

  • Temporal relationship – If a factor is believed to be the cause of a disease, then exposure to that factor must occur before disease develops

  • Strength of association – Measured by the relative risk or odds ratio. The stronger the association, the more likely that the relation is causal

Guidelines for assessing causality1
Guidelines for Assessing Causality

  • Dose-response relationship – As the dose of exposure increases, the risk of disease also increases. If a dose-response relationship is present, it is strong evidence of a causal relationship. However, the absence of a dose-response relationship does not rule out a causal relationship.

  • Replication of findings –Expect to see the same findings in different studies and in different populations

Guidelines for assessing causality2
Guidelines for Assessing Causality

  • Biologic plausibility – The findings should be consistent with existing biologic knowledge

  • Consideration of alternate explanations –Important to report that alternative explanations (bias or confounding) have been considered

  • Cessation of exposure – If a factor is a cause of disease, we would expect the risk of disease to decline when exposure to the factor is reduced or eliminated

Guidelines for assessing causality3
Guidelines for Assessing Causality

  • Consistency with other knowledge – Causal relationships have findings consistent with other data

  • Specificity of association – When a certain exposure is associated with only one disease

    • Relatively weak criterion; cigarette manufacturers use it to argue that diseases attributed to cigarette smoking do not meet the causal criteria because smoking has been linked many diseases

    • When specificity is found, however, it can provide additional support for a causal inference

    • Absence of specificity does not negate a causal relationship