  Missing Not at Random (MNAR) and nonnormal data are challenging to handle.
Traditional missing data analytical techniques such as full information maximum
likelihood estimation (FIML) may fail with nonnormal data as they are built on
normal distribution assumptions. Two-Stage Robust Estimation (TSRE) does manage
nonnormal data, but both FIML and TSRE are less explored in longitudinal
studies under MNAR conditions with nonnormal distributions. Unlike traditional
statistical approaches, machine learning approaches do not require
distributional assumptions about the data. More importantly, they have shown
promise for MNAR data; however, their application in longitudinal studies,
addressing both Missing at Random (MAR) and MNAR scenarios, is also
underexplored. This study utilizes Monte Carlo simulations to assess and
compare the effectiveness of six analytical techniques for missing data within
the growth curve modeling framework. These techniques include traditional
approaches like FIML and TSRE, machine learning approaches by single imputation
(K-Nearest Neighbors and missForest), and machine learning approaches by
multiple imputation (micecart and miceForest). We investigate the influence of
sample size, missing data rate, missing data mechanism, and data distribution
on the accuracy and efficiency of model estimation. Our findings indicate that
FIML is most effective for MNAR data among the tested approaches. TSRE excels
in handling MAR data, while missForest is only advantageous in limited
conditions with a combination of very skewed distributions, very large sample
sizes (e.g., n larger than 1000), and low missing data rates.
