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I would like if it was possible that the Lord would help me in a review, lest it incur in error.

If it were a simple logistic regression, I could use The Area Under an ROC Curve, but since I'm using a generalized linear mixed model with reply Binaria, can analyze the same way this output?

I'm using the GLMER function with two models. One with (1 / g) (Random intercept with fixed mean ) and the other with x + (x | g) (Correlated random intercept and slope )

If not, what would you suggest to check the adjustment of the model?

Thank you

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closed as unclear what you're asking by AdamO, Michael Chernick, kjetil b halvorsen, Peter Flom May 7 '18 at 12:20

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The AUC is interpreted as a probability that a randomly selected case is assigned higher risk than a randomly selected control. A mixed model combines fixed and random effects. If we characterize participants only by their fixed effects, participant A might receive higher risk than participant B, but the added contribution of the random effects might give participant B a higher risk than A. That is to say, $c = E[\hat{Y}_1 > \hat{Y}_2 | Y_1 = 1, Y_2=0]$

The obviously solution is far, far too computationally difficult: essentially you would have to marginalize the logistic model and calculate the U-statistic directly, i.e. for each participant, treat their linear predictor as a latent normally distributed variable and, rather than flagging each pair-wise comparison as "yes: the case had higher risk" vs "no: the case had equal or lower risk", you would need to obtain something like a $p$-value representing the risk that that particular case had higher risk than the control.

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  • $\begingroup$ I appreciate your attention in the response. But Sorry, I do not know implement in R, marginalize the logistic model and calculate the U-statistic directly. I apologize for the work but You would have a LInk as an example or advise any other way to validate the adjustment of this mixed model in Binomial in R? $\endgroup$ – Cleber Iack Dec 4 '16 at 12:46

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