I have a dataset with 260 patients. I aim to study factors associated the certain finding in magnetic resonance imaging. I use logistic regression with six predictors. Regression yields to several significant predictors. I have philosophical concerns however.
Nagelkerke´s R is only 20%. My model is however significant compared to empty model. Only 7% of the positive outcomes can be predicted. However diagnostics show that just ten cases has normalized residuals outside 1.96 SD. Moreover leverage criteria of 3x mean value is met in 250 cases.
However I have been told that I do not have look too deeply to this fact with low R2 (http://www.theanalysisfactor.com/small-r-squared/
I would like to think so. My aim is not to construct a PREDICTIVE model but instead to study RELATIONSHIP between predictors and outcome. Therefore low R2 can be tolerated as far as my model is better than empty model. Moreover this is a highly clinical issue and it is impossible to include all relevant predictors. These most likely contribute much of the variance. My predictors are those which can be measured in reasonable manner. This fact highlights more the impossibility to construct a PREDICTIVE model.
Is it valid to draw any conclusions from my analysis with statistical findings stated above? And is there any basis to differentiate between predictive logistic regression versus logistic regression studying relationship?