A review of ten imputation methods for handling missing values in logistic regression (Journal of Pure and Applied Sciences (Science Forum), 2026)
This paper presents a brief review of ten imputation methods for missing data in the binary logistic regression model. The performance of these methods under different missingness scenarios has been examined based on a medical dataset. The results indicated that, in general, expectation–maximization and k-nearest neighbor imputation methods are very appropriate for estimating the missing values in this model, whether data are missing in dependent variable only, independent variables only, or in both.