A Comparative Evaluation of a Conditional Median-Based Bayesian Growth Curve Modeling Approach with Missing Data

Longitudinal data are essential for studying within-subject change and between-subject differences in change. However, missing data, especially with nonnormal distributions, remains a significant challenge in longitudinal analysis. Full information maximum likelihood estimation (FIML) and a two-stage robust estimation (TSRE) are widely used for handling missing data, but their effectiveness may diminish with data skewness, high missingness rate, and nonignorable missingness. Recently, a robust median-based Bayesian (RMB) approach for growth curve modeling (GCM) was proposed to offer a novel way to handle nonnormal longitudinal data, yet its effectiveness with missing data has not been fully investigated. This study fills this gap by using Monte Carlo simulations to systematically evaluate RMB's performance relative to FIML and TSRE. It is shown that, in general, RMB GCM is a reliable option for managing ignorable and nonignorable missing data in various data distributional scenarios. An empirical example further demonstrates the application of these methods.