I am involved with a medical research that analyzes Coronary Artery Disease. The dataset has a couple of predictors such as age, gender, race, certain symptons and medical standard procedures to be diagnosed as CAD disease. Most of them are binary, (like whether the patient smokes, etc.) and the rest are continuous (like blood pressure, or certain hormone levels) The outcome variable is whether the patient has CAD disease or not (binary).
The research question is to build a model to find variables of the most interest and better predict. My idea is to perform a Lasso Logistic Regression to select the variables and look at the prediction. I did some research online and find a very useful tutorial by Trevor Hastie and Junyang Qian. Click the link here.
However, the total valid observation here is around 150 and at least 4/5 of patients don't have CAD diseases. In other words, the outcome variable in the data show extreme cases for "yes". I am not sure the number of observation is large enough to perform Lasso, either. Under this circumstance, in addition to the general proceduce above, do I need to set up anything else (such as weight adjustment or more penalties for "Yes") for model construction? If so, are there any methods to handle such problem?
Thanks in advance!