I have been analysing some survival datasets for lung transplant patients for the past several months. I have found some variables that are statistically significant - such as year of transplant and transplant centre - but which aren't appropriate to include in a predictive model for predicting outcomes with new data.
For example, if I've modelled using data from 2012 - 2022, moving forward I can't just plug in 2023, 2024, 2025 and so on for the year of transplant.
Also, there are five transplant centres labelled A - E, they are included in the Cox model as factors. If one transplant centre has better outcomes, putting the centre in the model would likely result in a feedback loop where that centre receives even more donor offers, which is unfair for patients at other centres.
I feel like it isn't correct to just ignore these variables, but I'm not sure exactly how to include these variables in a predictive/prognostic model. Should I include them but for all future patients just set the value to the reference level? Or is there some other way of accounting for them that I am missing?