This question already has an answer here:
- Model Selection: Logistic Regression 3 answers
I have 3 possible "final" models in binary logistic regression (N=176, Number of events = 36).
Now I am trying to decide which one to select. It´s clear,"All models are wrong, but some are useful", but I have to decide. My goal is prediction and so parsimony over complexity.
Which criterion to use? Corrected AIC in combination with adjusted Pseudo-R2? And then AUC with calibration curve for each model in order to compare them?