I work with data at an union. If you are member while studying your monthly fee is 0 or a small amount depending on the type of membership. When you end your studies the fee increases significantly since you become a ‘normal’ member.
I’m currently doing EDA (e.g. Kaplan Meier Survival Curves) trying to figure out whether there is a relationship between e.g. age, type of membership under the studies (the free or the almost free), gender etc and the survival time af becoming a 'normal' member.
The problem is, that for many of the students they continue their study after a while hence returning to the free or low-fee membership. That of course gives extremely high survival rates (since they return to the free or low-fee membership) and make it difficult to find a significant relationship.
I have three ideas for how to deal with this, but don’t know which one to those.
combining the periods if the break between studies is e.g. under 6 months
censoring them when they return to their studies
only use the last of the periods they study
What is pros and cons for each approach? I want to get it right from the start to avoid any p-hacking. Any thoughts or reference to good resources is appreciated. 😊