Let's say I have a binary variable to explain using "some" logistic regression approach. The set of variables I have at disposal are exhaustive for a given period of time and for a longer period a subset of those data are missing.
I think this is a very common issue and was looking for some references about the classic (or maybe more exotic) methodologies available to deal with this kind of situation.
Ideally, I would like to fit the model to all the available data without filling the missing value with best estimates. I was thinking that maybe nesting model or conditionning appriopriately would be possible.