# Should I go back to do conventional logistic regression after LASSO logistic regression?

I used to use simple (multivariable) logistic regression plus stepwise regression to select important predictive variables. Though, I saw many critics on this method. Thus, I plan to use LASSO and check whether it gives similar results compared to the stepwise regression.

I am primarily using the glmnet package from R.

After the cv.glmnet, I got the lambda.min and lambda.1se respectively. I am still fine at this point that I probably should choose the lambda.1se for the lambda.

Please comment on above workflow as I wonder whether I have any misunderstandings.

The main question is: I get the coefficients corresponding to lambda.1se by coef(cvfit, s = "lambda.1se"). How should I interpret these coefficients? I acknowledged that some of the unimportant variables have been dropped, thus only important variables are kept.

Should I put these selected (important) variables back into the conventional logistic regression? It is because, I am studying epidemiology, which I am interested in whether these variables are independently associated with the outcome. I am not sure whether I can deduce similar conclusion statement from LASSO's output. Can we get the OR with 95% CI for each coefficient?

Thanks!