I'm comparing two models of classification of a binary medical condition. number of patients = 125 (95 and 30 for each condition)
- model 1 = radiologist : obtained with logistic regression with "visual predictors" (5, in which 2 multimodal categorical variables)
- model 2 = radiologist+ : obtained with logistic regression using the 5 previous "visual predictors" and 7 quantitative predictors (image textural and histogram variables). The 7 histo/textural predictors were obtained from Lasso logistic regression (best lambda after 5 fold cross validation)
Do you think it's reasonable to have this approach ? I read in p158 of Statistical Learning with Sparsity that is possible to make post-selection inference on stepwise forward regression, but it seems to be kludge to have my approach.
I used this approach also because I'd never did a grouped Lasso, and as I have manny categorical multimodal variable, it was easier to manage them with log reg. If you have the code R for groupes Lasso logistic regression than I would be very grateful if any one share it with me.
Also, the aim behind using Lasso only for histo/textural quantitative parameters is because I wanted to keep the same baseline "usual radiologist predictors" as I want to analyse the effect of this addition to the "usual" diagnostic performances.
Thanks for your help