The DataScience+ desk compares methods:
Missing data is the daily reality of medical and epidemiological work: a patient skips a lab draw, a questionnaire item is left blank. So in this tutorial I use real health-survey data and compare three current imputation packages on it: mice (multiple imputation, still the reference method for statistical inference), missRanger (random-forest imputation, the fast successor of missForest), and VIM (k-nearest-neighbour imputation). Before any imputing I use naniar, the modern, ggplot-native toolkit for missing data, to picture where the gaps are. I also include the method most analyses actually use by default, dropping incomplete rows, because seeing why it fails is half the point.
I start from complete records, delete values myself, and check each package against the truth I hid.
Click through for the method and different ways to impute data to fill in the gaps. The text of the article looks AI-generated, though the recommendations end up being pretty solid (at least in my experience with imputation and working with mice).