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Logistic Regression Analysis of Matched Data

Logistic Regression Analysis of Matched Data. THE GENERAL LOGISTIC MODEL. S. Logit form of logistic model: Logit P( X ) =  +   i X i. Logit form:. Special Case: No Interaction, I.e., all   = 0. EVW LOGISTIC MODEL FOR MATCHED DATA.

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Logistic Regression Analysis of Matched Data

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  1. Logistic Regression Analysis of Matched Data

  2. THE GENERAL LOGISTIC MODEL S Logit form of logistic model: Logit P(X) =  + iXi

  3. Logit form:

  4. Special Case: No Interaction, I.e., all  = 0.

  5. EVW LOGISTIC MODEL FOR MATCHED DATA Logit P(X) =  + E +1iV1i +2iV2i +EkWk E = (0, 1) exposure V1i’s denote dummy variables used to identify matching strata V2j’s denote potential confounders other than matching variables Wk’s denote potential effect modifiers (usually other than matching variables)

  6. Adjusted OR Comparing E=1 vs. E=0 Controlling for the V’s and W’s S Special Case: No Interaction, I.e., all  = 0.

  7. S

  8. S

  9. S exp

  10. S

  11. S

  12. S where the Di denote 62 dummy variables for the 63 matched sets

  13. S

  14. S

  15. S

  16. Note: Previous model was a no interaction model INTERACTION MODEL: 63 matched pairs 62 Logit P(X) = a + bE + Sg1iDi + g21GALL + d1EGALL

  17. 95% CI for OR involving interaction? e.g., What is the 95% CI for ?

  18. GENERAL 100(1-) CI FORMULA IN A LOGISTIC MODEL FOR MATCHED DATA S

  19. VARIANCE FORMULA S S S S S 

  20. Example: 95% CI formula

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