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.

翻译：纵向研究中缺失数据不可避免。传统方法如全信息最大似然（FIML）常用于处理可忽略的缺失数据，但纵向研究中常出现的非随机缺失数据可能导致模型估计产生偏倚。近年来，随机森林（RF）和K近邻（KNN）插补法等机器学习方法被提出用于处理缺失值。尽管机器学习插补法日益普及，但鲜有研究探讨这些方法在纵向研究中的可行性与实用性。本研究通过蒙特卡洛模拟，评估并比较了传统方法与机器学习方法（FIML、RF和KNN）在增长曲线建模中的表现，考察了样本量、缺失率及缺失数据机制对模型估计的影响。结果表明，在模型估计精度与效率方面，FIML优于两种机器学习插补法。