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Confounding

Confounding. A plot of the population of Oldenburg at the end of each year against the number of storks observed in that year, 1930-1936. Ornitholigische Monatsberichte 1936;44(2). Question:

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Confounding

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  1. Confounding

  2. A plot of the population of Oldenburg at the end of each year against the number of storks observed in that year, 1930-1936. Ornitholigische Monatsberichte 1936;44(2)

  3. Question: Are people living in Costa Rica or Venezuela at lower risk of mortality than people in Canada or the US? Yes No Mortality rate in six countries in the Americas, 1986  (assuming vital statistics are correct)

  4. Yes No Mortality rate in six countries in the Americas, 1986 Next question: Is the observed association causal in nature, i.e., is there something about living in Costa Rica or Venezuela that makes the population have lower risk of death than the population of Canada or the US? 

  5. Country Age distribution ? Mortality

  6. N=14,054 middle age adults from 4 US communities Comparing risk profile according to known CVD risk factors:  Low Risk individuals (n=623): - Never smokers - Total cholesterol <200 mg/dL - HDL cholesterol >65mg/dL - LDL cholesterol <100 mg/dL - Triglycerides <170 mg/dL - Glycemia <140 mg/dL - BP<140/90 mm Hg, no Rx - No Hx of CVD, htn, diabetes, high cholesterol  Rest (n=13,431): at least one of the above.

  7. !?

  8. Exposure ? Confounder Disease Outcome Common feature of previous examples

  9. A variable can be a confounder if all the following conditions are met: • It is associated with the exposure of interest (causally or not). • It is causally related to the outcome. • AND ... It is not part of the exposure  outcomecausal pathway

  10. Ways to assess if confounding is present: 1) Does the variable meet the criteria to be a confounder (relation with exposure and outcome)? 2) If the effect of that variable (on exposure and outcome) is controlled for (e.g., by stratification or adjustment) does the association change?

  11. Question: Is male gender causally related to the risk of malaria? Yes No Further study is needed Strategy #1: Does the variable meet the criteria to be a confounder? Hypothetical case-control study of risk factors for malaria. 150 cases, 150 controls; gender distribution. OR= [88 x 82] ÷ [68 x 62] = 1.71 

  12. Confounder for a male gender-malaria association? Male gender ? ? Malaria

  13. Confounder for a male gender-malaria association? Male gender Outdoor occupation ? Malaria

  14. ? First criterion: Is the putative confounder associated with exposure? Male gender Outdoor occupation ? Malaria

  15. Question: Is outdoor occupation associated with male gender? Yes No First criterion: Is the putative confounder associated with exposure? . OR=7.8 

  16. ? Second criterion: Is the putative confounder associated with the outcome (case-control status)? Male gender Outdoor occupation ? Malaria

  17. Question: Is outdoor occupation (or something for which this variable is a marker of --e.g., exposure to mosquitoes) causally related to malaria? Yes No Second criterion: Is the putative confounder associated with case-control status? . Malaria OR=5.3 

  18. ? Yes, it could be Outdoor occupation ? Probably not Third criterion: Is the putative confounder in the causal pathway exposure  outcome? . Male gender  Malaria Note: Judgment and knowledge about the socio-cultural context are critical to answer this question

  19. Question: Provided that: • Crude association between male gender and malaria: OR=1.71 and • ... Outdoor occupation is more frequent among males, and • ... Outdoor occupation is associated with greater risk of malaria … What would be the expected magnitude of the association between male gender and malaria after controlling for occupation (i.e., assuming the same degree of outdoor occupation in males and females)?  The (adjusted) association estimate will be smaller than 1.71 The (adjusted) association estimate will =1.71 The (adjusted) association estimate will greater than 1.71

  20. Indoor occupation Outdoor occupation OR=1.00 OR=1.06 Strategy #2: Does controlling for the putative confounder change the magnitude of the exposure-outcome association? Malaria OR=1.71

  21. Ways to control for confounding • During the design phase of the study: • Randomized trial • Matching • Restriction • During the analysis phase of the study: • Stratification • Adjustment

  22. Malaria OR=1.71 LR Rest F 29.0 30.1 Indoor occupation Outdoor occupation M 16.8 19.1 OR=1.06 OR=1.00 Examples of stratification

  23. Note that confounding is present when: • RR/ORpooled different fromRR/ORstratified and • RR/OR1 = RR/OR2 = …= RR/ORz

  24. *Adjusted by direct method using the 1960 population of Latin America as the standard population. Examples of adjustment Malaria OR=1.71 Indoor occupation Outdoor occupation OR=1.00 OR=1.06 Adjusted OR*=1.01 *Using the Mantel-Haenszel method, to be discussed.

  25. Further issues for discussion • Types of confounding • Confounding is not an “all or none” phenomenon • Residual confounding • Confounder might be a “constellation” of variables or characteristics • Considering an intermediary variable as a “confounder” for examining pathways • Confounding: a type of bias? • Statistical significance and confounding

  26. Types of confounding • Positive confounding When the confounding effect results in an overestimation of the effect (i.e., the crude estimate is further away from 1.0 than it would be if confounding were not present). • Negative confounding When the confounding effect results in an underestimation of the effect (i.e., the crude estimate is closer to 1.0 than it would be if confounding were not present).

  27. 3.0 0.4 0.4 3.0 1 0.1 10 Type of confounding: PositiveNegative 3.0 UNCONFOUNDED  5.0 OBSERVED, CRUDE  2.0  0.3  0.7  ? 0.7 “Qualitative confounding” Relative risk

  28. Example of positive confounding Malaria OR=1.71 Indoor occupation Outdoor occupation OR=1.00 OR=1.06 Adjusted OR=1.01

  29. Example of negative confounding An occupational study in which workers exposed to a certain carcinogen are younger than those not exposed. If the risk of cancer increases with age, the crude association between exposure and cancer will underestimate the unconfounded (adjusted) association. Age: negative confounder.

  30. LR Rest F 29.0 30.1 M 16.8 19.1 *Adjusted by direct method using the 1960 population of Latin America as the standard population. Examples of qualitative confounding Rate ratioUS/Mex= 1.78 0.72

  31. Confounding is not an “all or none” phenomenon A confounding variable may explain the whole or just part of the observed association between a given exposure and a given outcome. • Crude OR=3.0 … Adjusted OR=1.0 • Crude OR=3.0 … Adjusted OR=2.0 • Residual confounding Controlling for one of several confounding variables does not guarantee that confounding is completely removed. Residual confounding may be present when: - the variable that is controlled for is an imperfect surrogate of the true confounder, - other confounders are ignored, - the units of the variable used for adjustment/stratification are too broad • The confounding variable may reflect a “constellation” of variables/characteristics • E.g., Occupation (SES, physical activity, exposure to environmental risk factors) • Healthy life style (diet, physical activity)

  32. Other factors? ? ERT (adjusted)* Low CHD *Adjusted for family history, type of menopause, smoking, hypertension, diabetes, OC use, high cholesterol, age, obesity.

  33. (Matthews KA et al. Prior to use of estrogen replacement therapy, are users healthier than nonusers? Am J Epidemiol 1996;143:971-978)

  34. Estrogen-Progestin Placebo Kaplan-Meier estimates of the cumulative incidence of primary coronary heart disease events. JAMA 1998;280:605-13.

  35. Circulation 1996;94:922-7.

  36. Treating an intermediary variable as a confounder (i.e., ignoring “the 3rd rule”) Under certain circumstances, it might be of interest to treat an hypothesized intermediary variable acting as a mechanism for the [risk factor  outcome] association as if it were a confounder (for example, adjusting for it) in order to explore the possible existence of additional mechanisms/pathways. This is done by comparing the adjusted with the unadjusted values.

  37. Hypertension EXAMPLE:It has been argued that obesity is not a risk factor of mortality. The observed association between obesity and mortality in many studies might just be the product of the confounding effect of hypertension. Obesity ? Mortality

  38. Hypertension HOWEVER,Hypertension is probably not a real confounder but rather a mechanism whereby obesity causes hypertension.* Obesity Mortality *Manson JE et al: JAMA 1987;257:353-8.

  39. alternative mechanism(s)? EVEN IF HYPERTENSION IS A MECHANISM LINKING OBESITY TO MORTALITY, it may be of interest to conduct analyses that control for hypertension, to assess whether alternative mechanisms may causally link obesity and mortality. Obesity Block by adjustment Hypertension Mortality

  40. EXAMPLE:Is maternal smoking a risk factor of perinatal death?Is the association confounded by low birth weight? Maternal smoking Low birth weight ? Perinatal mortality

  41. OR RATHER:Is low birth weight the reason why maternal smoking is associated to higher risk of perinatal death? Maternal smoking Low birth weight Perinatal mortality

  42. BUT THERE COULD BE AN ADDITIONAL QUESTION:Does maternal smoking cause perinatal death by mechanisms other than low birth weight? Maternal smoking Block by adjustment Direct toxic effect? Low birth weight Perinatal mortality

  43. Statistical significance should not be used to assess confounding effects Odds Ratio [age 56/age 55] = 60/40 ÷ 50/50 = 1.5 Age (years) 55 56

  44. Statistical significance should not be used to assess confounding effects Odds Ratio [cases/controls] = 60/40 ÷ 50/50 = 1.5 % post-menopausal Age (years) 55 56

  45. Statistical significance should not be used to assess confounding effects The main strategy must be to evaluate whether the difference in the confounder is large enough to explain the association.

  46. Control of Confounding Variables • Randomization • Matching • Adjustment • Direct • Indirect • Mantel-Haenszel • Multiple Regression • Linear • Logistic • Poisson • Cox Stratified methods

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