I'm using a GLM with logistic link function to try to predict Y (0 or 1) as a function of a ton of predictor variables (A, B, C, etc.). Some of the predictor variables (A*, B*, C*, etc.) have been shown in other studies to be significant predictors. I want to show essentially that Y is unrelated to all of the other predictor variables, and I thought the simplest way to do this would be to run the full model (Y ~ .) and the null model (Y ~ A* + B* + C* + ...), and then use anova() to compare the two and show that they aren't different (i.e. have equal predictive power).
However, anova() only outputs p-values (type I error), but I need a type II error rate here (since I want to show that the models are the same, I need a false negative rate for that). Any ideas on how to approach this?