Epidemiology and Applied Statistics Review Module 4 – Causation, Bias & Confounding

1. Epidemiology and Applied Statistics ReviewModule 4 – Causation, Bias & Confounding American College of Veterinary Preventive Medicine Review Course Katherine Feldman, DVM, MPH, DACVPM kfeldman@umd.edu 301-314-6820

2. Plan • Students review modules on their own • Send questions by email to Katherine Feldman (kfeldman@umd.edu) by Friday March 23 a.m. • Conference call Friday March 23 2-3 p.m. • Watch email and Blackboard for conference call details

3. 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

4. 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

5. 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

6. 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

7. 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.)

8. 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

9. 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

10. 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

11. How to Control for Confounding

12. 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

13. 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

14. 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.

15. 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

16. 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

17. 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

18. 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