I am trying to model hospital readmission using both categorical and nominal variables. Laboratory data comprises a big chunk of the nominal variables, the problem is not every patient has laboratory tests done, and for those who do, not the same tests are taken. So, for the 18 laboratory predictors, all contain missing data (ranging from 36 to 58 per cent, with 33 per cent of patients not having tests done) and I am not sure on how to deal with that.
If I understand correctly, MNAR means the missingness is related to what is being measured and with MAR, this is not the case and missing data can be predicted from other collected information.
From my point of view, laboratory data falls a bit in between since decision to take tests is a bit subjective depending on different clinicians, but tests are also not taken because the measured variables are expected to be OK?
What would the right approach be in this case? I have been considering:
- Impute Lab Data
- Categorize the data such that I have it coded as No test taken/ Normal value/ High value / Low value (or something similar)
Edit: The ultimate purpose of the model would be to be used for discharge of patients and it would be expected that some of those would not have data for lab tests - so I think it would be good to have a variable representing that (categorize), but I am quite inexperienced in this area.