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]

301-314-6820

slide2
Plan
  • Students review modules on their own
  • Send questions by email to Katherine Feldman ([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
references
References
  • 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
slide5
Bias
  • 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.)
confounding
Confounding
  • 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
confounders
Confounders
  • 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)

Exposure

Outcome

Confounder

confounding1
Confounding
  • 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
causation
Causation
  • 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
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