Same as before Marginal ModelsGEE Accounting for Correlation Model 1 This model

# Same as before marginal modelsgee accounting for

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Same as before!
Marginal Models/GEE: Accounting for Correlation Model 1 This model does NOT assume the observations are independent! We can show this using a correlation matrix for the J = 9 observations for subject i = 1. Note 1: “ corrw ” option printed this matrix for us. Note 2: “ type = exch ” forced all off -diagonal correlations to be the same (“exchangeable”) Correlation Matrix for Subject 1 Observation 1 2 3 4 5 6 7 8 9 1 1 0.4381 0.4381 0.4381 0.4381 0.4381 0.4381 0.4381 0.4381 2 1 0.4381 0.4381 0.4381 0.4381 0.4381 0.4381 0.4381 3 1 0.4381 0.4381 0.4381 0.4381 0.4381 0.4381 4 1 0.4381 0.4381 0.4381 0.4381 0.4381 5 1 0.4381 0.4381 0.4381 0.4381 6 1 0.4381 0.4381 0.4381 7 1 0.4381 0.4381 8 1 0.4381 9 1 Does this appropriately describe the correlation in our data?
Marginal Models/GEE: Accounting for Correlation Model 2 Now let’s try a more appropriate correlation structure with our GEEs : Logistic regression: 𝑙?𝑔𝑖?(𝑃(𝑌 ?? = 1|𝑿)) = 𝛽 0 + 𝛽 1 ???????? + 𝛽 2 ?????? + 𝛽 3 ???????? proc genmod data =infant descending ; model outcome = birthwgt gender diarrhea / dist =bin link =logit; repeated subject = idno / within = month type = ar(1) corrw ; run ; OR for diarrhea? e 0.22 = 1.25 95% CI (0.23, 6.68) Same calculation as in regular logistic regression!
Marginal Models/GEE: Accounting for Correlation Model 2 This model does NOT assume the observations are independent! We can show this using a correlation matrix for the J = 9 observations for subject i = 1. Note: “ type = ar(1) ” created an “autoregressive” correlation structure – what do you notice about the correlations? Correlation Matrix for Subject 1 Observation 1 2 3 4 5 6 7 8 9 1 1 0.5254 0.2760 0.1450 0.0762 0.0400 0.0210 0.0110 0.0058 2 1 0.5254 0.2760 0.1450 0.0762 0.0400 0.0210 0.0110 3 1 0.5254 0.2760 0.1450 0.0762 0.0400 0.0210 4 1 0.5254 0.2760 0.1450 0.0762 0.0400 5 1 0.5254 0.2760 0.1450 0.0762 6 1 0.5254 0.2760 0.1450 7 1 0.5254 0.2760 8 1 0.5254 9 1 Does this appropriately describe the correlation in our data?