  Missing data are inevitable in longitudinal studies. Traditional methods,
such as the full information maximum likelihood (FIML), are commonly used to
handle ignorable missing data. However, they may lead to biased model
estimation due to missing not at random data that often appear in longitudinal
studies. Recently, machine learning methods, such as random forests (RF) and
K-nearest neighbors (KNN) imputation methods, have been proposed to cope with
missing values. Although machine learning imputation methods have been gaining
popularity, few studies have investigated the tenability and utility of these
methods in longitudinal research. Through Monte Carlo simulations, this study
evaluates and compares the performance of traditional and machine learning
approaches (FIML, RF, and KNN) in growth curve modeling. The effects of sample
size, the rate of missingness, and the missing data mechanism on model
estimation are investigated. Results indicate that FIML is a better choice than
the two machine learning imputation methods in terms of model estimation
accuracy and efficiency.
