Year: 2026
Type: article
Abstract: Missing data can often occur in longitudinal data. Multiple imputation has been very popular for longitudinal analysis in recent years. In this study, we aim to determine which multiple imputation method is superior for longitudinal normally distributed data when we use linear mixed models. In the literature, multiple imputation by chained equations (MICE) is one of the most popular methods for longitudinal data when using linear mixed models. In MICE, there are three parametric multiple imputation methods, and we compare the three methods with maximum likelihood multiple imputation. Maximum likelihood multiple imputation estimates the parameters using maximum likelihood. After the literature review, we determined that this study will be the first to compare these two approaches for longitudinal normally distributed data when we use linear mixed models. After the simulation study, maximum likelihood multiple imputation has less biased results than the three methods in MICE in terms of mean square error (MSE). The three imputation methods in MICE give closer results to each other. Therefore, we find that maximum likelihood multiple imputation is superior to MICE for longitudinal normally distributed data when we use linear mixed models. Moreover, we show that maximum likelihood multiple imputation can be used for longitudinal normally distributed data with linear mixed models.
Source: DergiPark (Istanbul University)
Author: Yanarateş, Tuncay
Institution: Türkiye İş Kurumu
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7155666684