Comparison of Two Newly Developed Multiple Imputation Methods for MNAR Cross-Sectional Data

The problem of missing not at random (MNAR) data is a highly complex problem to the difficulty of joint modeling the outcome values and missing pattern while taking the variability of the missing data into consideration. In recent years, two methods by Galimard et. al (2016) and Ogundimu & Collins (2017) each developed their own multiple imputation (MI) methods for handling MNAR data. However, the…