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Spring 2008

Spring 2008. Bias, Confounding, and Effect Modification STAT 6395. Filardo and Ng. Confounding. Suppose we have observed an association between an exposure and disease in a cohort study or case-control study that:

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Spring 2008

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  1. Spring 2008 Bias, Confounding, and Effect Modification STAT 6395 Filardo and Ng

  2. Confounding Suppose we have observed an association between an exposure and disease in a cohort study or case-control study that: We are confident was not a biased result due to a flaw in the design or execution of the study

  3. Confounding Suppose we have observed an association between an exposure and disease in a cohort study or case-control study that: We are confident was not a random association due to chance variation (95% confidence interval for the estimate does not include 1.0)

  4. Confounding Suppose we have observed an association between an exposure and disease in a cohort study or case-control study that: How do we now distinguish between a noncausal association due to confounding and a causal association?

  5. Hypothetical example of confounding: comparison of prostate cancer mortality rate in 2 geographic areas • The exposure of interest is geographic area • Annual mortality rate from prostate cancer: • Region A : 50 per 100,000 • Region B: 20 per 100,000 • Relative risk = 50/20 = 2.5 Do these data show that living in Region A is a risk factor for prostate cancer?

  6. Prostate cancer mortality rate in 2 geographic areas *per 100,000 per year Unadjusted (crude) RR = 50/20 = 2.5 Age-adjusted RR = 66.25/85 = 0.78

  7. Age as a confounder The large discrepancy between the age-adjusted RR (0.78) and the unadjusted RR (2.5) means that age confounded the observed association between geographic area and prostate cancer mortality

  8. Age as a confounder Age was a confounder because: • Age is a  risk factor for prostate cancer • Age was  associated with geographic region • Age is not an intermediate step in a causal pathway between residence in a geographic region and prostate cancer mortality

  9. Age as a confounder • Age is a common confounder in observational epidemiology because it is associated with many diseases and many exposures As distinct from a biased association, which is erroneous, the confounded association between geographic region and prostate cancer mortality, though not causal, is real

  10. Causal association (?) Geographic area Prostate cancer RR(unadj)=2.5 RR(adj)=0.78 + association + association Age Age confounded the relationship between geographic area and prostate cancer

  11. Case-control study: alcohol consumption and lung cancer OR(unadj) = (390x175)/(325x110) = 1.91 Note: 90% of the 500 cases in the study were smokers 25% of the 500 controls in the study were smokers 80% of the smokers drank

  12. Case-control study: alcohol consumption and lung cancer –table for Smokers OR = (360x25)/(100x90) = 1.00

  13. Case-control study: alcohol consumption and lung cancer –table for NON Smokers OR = (30x150)/(225x20) = 1.00

  14. Smoking  and lung cancer OR = (450x375)/(125x50) = 27.0

  15. Alcohol consumption  and smoking OR = (460x170)/(255x115) = 2.67

  16. Alcohol consumption and lung cancer (summary) • Unadjusted OR = 1.91 • Stratify by smoking status (2 strata -- smokers and nonsmokers) • OR = 1 for the relationship between alcohol consumption and lung cancer among both smokers and non smokers • Smoking-adjusted OR (weighted average of the stratum-specific ORs) = 1.00

  17. Smoking confounded the relationship between alcohol consumption and lung cancer Large discrepancy between the smoking-adjusted OR (1.00) and the unadjusted OR (1.91) shows smoking was a confounder

  18. Smoking confounded the relationship between alcohol consumption and lung cancer Smoking was a confounder because: • Smoking is  a strong risk factor for lung cancer • Smoking is  associated with alcohol consumption • Smoking is notan intermediate step in a causal pathway between alcohol consumption and lung cancer

  19. Causal association = NO Alcohol consumption Lung cancer OR(unadj)=1.91 OR(adj)=1.00 + association + association Smoking Smoking confounded the relationship between alcohol consumption and lung cancer

  20. Confounding: definition Confounding is a distortion of the association between exposure and outcome brought about by the association …of another, extraneous exposure (confounder) with both the disease and the exposure of interest

  21. Confounding: definition As distinct from a biased association, which is erroneous, a confounded association, though not causal, is real

  22. Properties of confounders A confounder must be associatedwith the exposure under study

  23. Properties of confounders Causal association (?) Lung cancer Alcohol consumption RR(unadj)= RR(adj) ? association Electomagnetic fields Exposure to electromagnetic fields cannot confound the relationship between alcohol consumption and lung cancer

  24. Properties of confounders For an extraneous exposure to be a confounder, it is necessary, but not sufficient to just be associated with the exposure of interest

  25. Properties of confounders Causal association (?) Lung cancer Alcohol consumption RR(unadj)= RR(adj) + association Read meat Red meat consumption cannot confound the relationship between alcohol consumption and lung cancer

  26. Properties of confounders A confounder must also be a risk factor for the disease

  27. Properties of confounders Causal association = NO Alcohol consumption Lung cancer OR(unadj)=1.91 OR(adj)=1.00 + association + association Smoking Smoking confounds the relationship between alcohol consumption and lung cancer

  28. Properties of confounders A confounder cannot  be an intermediate variable in the causal pathway between the exposure of interest and the disease

  29. Properties of confounders Willingness to get HIV testing A: Predictors / Confounders HIV-related knowledge Direct effect on HIV-related knowledge Direct effect on willingness to get HIV testing Mediated effect of A on willingness to get HIV testing

  30. Properties of confounders Causal association ? Exposure Disease + / - association + / - association Confounder

  31. Avoiding confounding with appropriate study design • Randomization • Restriction • Matching

  32. Randomization done in experimental studies ONLY Subjects are randomly allocated between n groups ...’ensuring’ that known and unknown potential confounder distributions are similar across groups

  33. Restriction Restrict the selection criteria for subjects to a single category of an exposure that is a potential confounder …in the cohort study of alcohol consumption and lung cancer, restrict the cohort to persons who have never smoked. Enhances internal validity, but could hurt external validity

  34. Matching In a case-control study, selection of controls who are identical to, or nearly identical to, the cases with respect to the distribution of one or more potential confounding factors Matching is intuitively appealing, but its implications, particularly in case-control studies, are much more complicated than one might at first suppose

  35. Assessing the presence of confounding during analysis Is the potential confounder related to both the exposure and the disease? Stratification: Is the unadjusted OR or RR similar in magnitude to the ORs or RRs observed within strata of the potential confounder? Adjustment: Is the unadjusted OR or RR similar in magnitude to the OR or RR adjusted for the presence of the potential confounder?

  36. Assessing the presence of confounding during analysis Is the potential confounder related to both the exposure and the disease? Confounding is judged to occur when the adjusted and unadjusted values differ meaningfully.

  37. Pandey DK et al. Dietary vitamin C and beta-carotene and risk of death in middle-aged men. The Western Electric study. • Concurrent cohort study • Hypothesis: intake of vitamin C and beta carotene (both anti-oxidants) are protective against all-cause mortality • Potential confounder: cigarette smoking

  38. Unadjusted mortality rates and RRs according to vitamin C/beta-carotene intake index *deaths per 1,000 person-years

  39. Percentage distribution of vitamin C/beta-carotene intake index by smoking status at baseline

  40. Mortality rates and RRs by current smoking at baseline *deaths per 1,000 person-years

  41. Mortality rates and RRs for vitamin C/beta-carotene intake index, stratified by current smoking at baseline *deaths per 1,000 person-years

  42. Unadjusted and smoking-adjusted mortality RRs according to vitamin C/beta carotene intake index *Adjusted for smoking using the direct method with the total cohort as the standard population

  43. Vitamin C/ beta-carotene Causal association (?) Mortality Medium intake: RR(unadj)=0.82 RR(adj)=0.85 High intake: RR(unadj)=0.79 RR(adj)=0.81 - association + association Smoking Smoking did not confound the association between vitamin C/beta carotene intake and all-cause mortality

  44. Methods of adjusting for (controlling for) confounding in the analysis • Adjustment methods based on stratification • Mathematical models (multivariable analysis)

  45. Adjustment methods based on stratification Stratify by the confounder Calculate a single estimate of effect across the strata (adjusted OR or adjusted RR), which is a weighted average of the RRs or ORs across the strata

  46. Adjustment methods based on stratification Stratify by the confounder Calculate the RR or OR for the association between the exposure and disease within each stratum of the confounder

  47. 3 methods of obtaining a weighted average • Direct adjustment (used in cohort studies) -- weights are based on the distribution of the confounder in a standard population

  48. 3 methods of obtaining a weighted average • Indirect adjustment (mainly used in occupational retrospective cohort studies) -- weights are based on the distribution of the confounder in the study population

  49. 3 methods of obtaining a weighted average • Mantel-Haenszel method (most common adjustment method based on stratification; used in case-control or cohort studies) -- weights are approximately proportional to the reciprocals of the variances of the ORs or RRs within each stratum

  50. Shapiro S et al. Oral-contraceptive use in relation to myocardial infarction –a case-control study • Hypothesis: recent use of oral contraceptives is associated with risk of myocardial infarction • Cases: 234 premenopausal women with a definite first myocardial infarction (median age 43) • Controls: 1,742 premenopausal women admitted for musculoskeletal conditions, trauma, abdominal conditions, and many miscellaneous conditions (median age 36)

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