I have two variables that I intend to use for creating prediction models for which I'm unsure how to handle missing values. The reason is that both are separated into multiple columns.
All participants in my data have the same genetic disease, which can be caused by one of two distinct genetic mutations. So I have a binary variable asking if the participant was genotyped, a second column telling if the first genetic mutation is present/absent, and a third telling if the second mutation is present/absent. Since there is no possible overlap between both mutations, if missing values were not a concern, I would simply create a categorical variable that could take two values, either mutation 1 or mutation 2.
The second variable is IQ. I have a binary variable telling if an IQ test was passed and a second one dividing participants who passed an IQ test into three categories (Normal IQ, moderate disability, severe disability). In an ideal world, all my participants would have passed an IQ test, and I would simply use the results from the three categories.
Now, for my possible solutions:
Drop those two variables from my models --> Not ideal/possible since they are important predictors
Create my categorical variable for the mutation with what I have and use only complete cases for modelling --> Also not ideal as I am already dealing with a dataset that is not as big as I would have hoped.
Create an additional category for both, something like "Unknown/unmeasured" --> I'm not sure what the repercussions would be, but I'm guessing it would have a non-negligible impact on the coefficients for those variables on my models.
Perform multiple imputations on columns as they are right now --> It does not seem to make sense, some people would at the same time not be tested for IQ and have a value for IQ, same for mutation + some people would end up having both mutations, which is not possible.
Create the categories I need, then impute (One column for mutation, which can take values of either mutation 1 or 2, one column for IQ with the three categories) --> It doesn't seem right to modify data like this before imputation, especially to attribute an IQ result for someone when the preceding column said the participant didn't pass an IQ test.
Is there an adequate way to do multiple imputations in this case? If not, is one of the solutions preferable?
As far as I can see, both dichotomous variables (If IQ test was passed and if genotyping was done) are missing at random, while the following variables are directly dependent on the result of the first.