The area under the ROC curve (AUC) assesses the discrimination ability of the model as described in this note. A given model might discriminate well (or poorly) regardless of the sample size. For example, a given model might discriminate poorly because it is missing one or more important predictors of the response event or might not properly specify an important predictor (such as by omitting interactions or other higher-order terms). Simply adding more data will not improve the model's discriminating ability. So, I don't believe you can do a power analysis to determine the sample size needed to achieve a sufficiently large AUC. That will be achieved by defining a model that includes the important predictors and has them properly specified.
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