Separate imputation models for separate substantive models on the same dataset? I am working with a dataset for which I have generated three hypotheses, i.e. I built three substantive models (two logistic regressions and one cox regression with a different dependent variable). The dataset contains some missing values and I decided to fill the gaps with multiple imputation.
Should I generate a separate imputation model for each substantive model or would it be better practice to create a single imputation model to be used with every substantive model? Or does it not even matter?
I work in medicine and have no formal education in statistics, so I might be missing some fundamental understanding - always happy to learn!

First, it’s best to determine whether data for the variables in question are missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR).  Many people leap to imputation without considering this important question. If the data are MNAR, imputation will not help your cause and in fact will amplify the bias problem that exists by producing one or more new, biased data sets that have a larger sample size than your original, complete-cases set.
