Missing Data Handling via EM and Multiple Imputation in Network Analysis using Glasso and Atan Regularization

The existing literature on missing data handling in psychological network analysis using cross-sectional data is currently limited to likelihood based approaches. In addition, there is a focus on convex regularization, with the missing handling implemented using different calculations in model selection across various packages. Our work aims to contribute to the literature by implementing a missing data handling approach based on multiple imputation, specifically stacking the imputations, and evaluating it against direct and two-step EM methods. Standardized model selection across the multiple imputation and EM methods is ensured, and the comparative evaluation between the missing handling methods is performed separately for convex regularization (glasso) and nonconvex regularization (atan). Simulated conditions vary network size, number of observations, and amount of missingness. Evaluation criteria encompass edge set recovery, partial correlation bias, and correlation of network statistics. Overall, missing data handling approaches exhibit similar performance under many conditions. Using glasso with EBIC model selection, the two-step EM method performs best overall, closely followed by stacked multiple imputation. For atan regularization using BIC model selection, stacked multiple imputation proves most consistent across all conditions and evaluation criteria.