Handling missing values of a categorical covariate I have a longitudinal dataset with records from 22902 individuals. The outcome is a continuous variable and has no missing values. However, one of the predictors (from a total of 8) has some missing values. This predictor refers to the patient's smoking status (Yes or No), and I have no record of such status for 9430 individuals.
Since this variable does not change over time, I think the smoking status was accessed upon the first medical appointment and kept that way; in cases where the information is unavailable, the patient must not have disclosed their smoking status when first evaluated. So I don't think it is related to the outcome.
How to handle this?
Suppose I remove the cases where the values are missing. In that case, I lose information on 9430 patients. So I thought about creating a third category: "missing" or "unknown", and carrying on with the analysis. But I'm not sure that would be the correct way of handling this.
I appreciate any help you can provide. Thanks!

In this case, your idea seems correct of treating "not disclosed" as a third value of the categorical variable.
Are any of the available descriptors correlated with smoking? Blood pressure for example? If so, then you should take care about the choice of model - Random Forest would work better than Logistic Regression in face of the partial correlation.
