Which variables could I impute using the multiple imputation? Suppose I have a dataset, and I want to use it to analyse the association between BMI and stroke. The dataset has some missingness for BMI(independent variable) and some missingness for covariates such as blood pressure. There are many complete covariates and suppose the dataset could be imputed.
My question is whether I could impute the BMI (independent variable) and missed covariates using other complete covariates? A coauthor said that what I could impute is only covariates, not independent variable. Is it right?
If the outcome (dependent variable) is also missed, could I impute that? (impute independent and dependent variables and covariates using other complete variables?)

There are many opinions on the matter. Naysayers seem to get nervous, especially, about imputing outcomes. But the validity of imputation is a theoretical result, and the theory supports imputing anything you want: independent variable, outcome, and covariates. The known contraindication for imputation is missing not at random (MNAR) data, that is missingness depends on the missing value itself. I find it especially odd that, when the discussion of imputation is presented, there is an assumption that imputation of the outcome should be particularly scrutinized for this bias. Wrongly imputing any covariate can lead to arbitrarily bad/wrong inference! Unfortunately, there's no real way to verify the assumption, and the requirement of no MNAR is common to all missing data methods: even complete case analysis. As my professor told me: the best solution for missing data is not to have it. When I refer to "imputation" I specifically mean multiple imputation.
