1 / 20

Confounding

Biost/Stat 579 . Confounding. David Yanez Department of Biostatistics University of Washington July 7, 2005. Information Bias. Measurement Errors Non-differential Error in assessing exposure or disease is similar between study groups Measure of effect tends toward 1 Differential

haven
Télécharger la présentation

Confounding

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Biost/Stat 579 Confounding David Yanez Department of Biostatistics University of Washington July 7, 2005

  2. Information Bias Measurement Errors • Non-differential • Error in assessing exposure or disease is similar between study groups • Measure of effect tends toward 1 • Differential • Error in assessing exposure (or disease) differs in different study groups • May increase or decrease measure of effect

  3. Information Bias Non-differential Misclassification Hypothetical Case-Control study D D 60 48 40 32 E E ˉ ˉ D D 40 52 68 60 Percent Exposure Misclassification: 20% 20% ˉ ˉ E E 100 100 100 100 OR = 60*60/40*40 = 2.25 OR = 48*64/36*52 = 1.96

  4. Information Bias Differential Misclassification Hypothetical Case-Control study D D 60 57 40 32 E E ˉ ˉ D D 40 43 68 60 Percent Exposure Misclassification: 5% 20% ˉ ˉ E E 100 100 100 100 OR = 60*60/40*40 = 2.25 OR = 57*68/43*32 = 2.81

  5. Confounding • The Idea: • Confounding is a confusion of effects. • Definition: • The apparent effect of the exposure of interest is distorted because the effect of an extraneous factor is mistaken for or mixed with the actual exposure effect.

  6. Confounding • Properties of a Confounder: • A confounder, C, must be causally related to the outcome, Y, OR associated with some predictor that is causally related to Y. • C must be associated with the predictor of interest, X, in the source population. • C must not be affected by X or Y. • The confounder cannot be an intermediate step in the causal path between X and Y.

  7. Confounding • Sources of confounding • Randomized clinical trials • Random differences between groups • Randomized clinical trials reduce confounding effect by balancing known and unknown confounding factors • Observational Studies • Random differences between groups • Factors associated with the exposure of interest

  8. Non-causal ConfoundingCausal Diagram Confounder Causal Predictor Outcome Confounder Predictor Outcome

  9. Country of Residence and Mortality

  10. Non-causal Confounding Ecologic study to determine whether country of residence is associated with mortality. Age Country Mortality Average age may be different among countries. Causal

  11. Country of Residence and Age-Adjusted Mortality

  12. Diet/lifestyle Non-causal Vitamin C Cancer Causal People who take vitamin C may eat a healthier diet and live a healthier lifestyle Confounding Case-control study to determine whether vitamin C intake is associated with colon cancer.

  13. Confounding • Design • Restriction • Matching • Individual matching • Group matching • Randomization • Analysis • Stratified analysis • Adjustment • Age-adjustment • Regression analysis

  14. Confounding • Detection • Biologic model or underlying theory should allow you to specify potential confounders in advance of study/analysis • Assess for confounding in a systematic way • Known of potential confounding factors • Other factors not previously known to be confounding factor

  15. ˉ ˉ ˉ ORc = ad/bc D D D D D D i a e b f j i+j e+f a+b E E E g k c d h l c+d g+h k+l Stratum 1 2 e+g i+k a+c f+h b+d j+l OR2 = il/kj OR1 = eh/fg ˉ ˉ ˉ E E E Stratified Analysis

  16. Confounding ORc = ad/bc ORa = f(OR1, OR2), Mantel Haenszel procedure If ORc = ORa no evidence of confounding If ORc≠ ORa, evidence of confounding

  17. D ˉ ˉ ˉ D D D 18 48 30 E ORc = ad/bc = 1.95 70 82 152 200 100 100 Age < 40 Age ≥ 40 D D 25 10 35 13 8 5 E E ˉ ˉ ˉ E E E 25 10 35 45 72 117 70 20 50 130 50 80 OR2 = il/kj = 1.0 OR1 = eh/fg = 1.0 Stratified Analysis

  18. Stratified Analysis D ˉ ˉ ˉ D D D 200 800 1000 E ORc = ad/bc = 4.75 50 950 1000 2000 250 1750 Stratum 1 2 D D 40 560 600 160 240 400 E E ˉ ˉ ˉ E E E 10 590 600 40 360 400 1200 50 1150 800 200 600 OR2 = il/kj = 4.2 OR1 = eh/fg = 6.0

  19. D 18 48 30 E ˉ ˉ D D 70 82 152 ORc = ad/bc = 1.95 200 100 100 ˉ E Is Confounder associated with Disease? Is Confounder associated with Exposure? D E ˉ E 20 70 50 ≥ 40 35 70 35 ≥ 40 < 40 50 80 130 < 40 13 117 130 200 100 100 200 48 152 OR = 4 OR = 9 Stratified Analysis

  20. Confounding • Analytic Criteria for Confounding • The crude estimate of effect differs from the adjusted estimate of effect • Steps to assess confounding • Calculate crude measure of effect (means, reg. Coeff., RR, OR) • Stratify and calculate stratum-specific measures of effect, or • Fit regression that adjusts for the potential confounders • Examine whether effects are similar. • Statistical significance should not be used as a criterion for assessing confounding.

More Related