you don't indicate how many observations are in your data set. But my guess is that you have a large number of observations. The value of the AIC statistic increases with your sample size. You really cannot interpret the AIC value directly.

What is more important to consider is whether the AIC shows a significant decrease when comparing the model with an intercept only and the model with covariates. Since you are estimating only 19 parameters (including the intercept) and the value of -2LL decreases by approximately 7750, your model is doing a fairly good job of predicting the response.

I would further note that the value of R^2 for a binary response can be rather difficult to interpret. However, an R^2 of 0.1158 is really pretty decent. Also, an AUC value of 0.715 indicates a fairly decent model.

You are correct, however, that the Hosmer-Lemeshow statistic indicates that at least one of your continuous covariates is not parameterized as well as it could be. You might want to fit your model using the GENMOD procedure and use the ASSESS statement to determine better parameterizations of the continuous predictors.

Hi Dale,

at first thank you for answering.

I got a few more than 120 000 observations in my data set. I tried the GENMOD procedure, but it seems that the ASSESS statement doesn't work on my version 9.1.3 ?! Kind of weird because i find some papers on this statement accross the web... Maybe this statement is no more in use ? I just have one continuous predictor: age. I'll try figured out how to parameterize it better to fit the model.

Thanks again.

Alexis