A Bayesian Two-Step Multiple Imputation Approach Based on Mixed Models for Missing EMA Data.

ABSTRACT: Ecological Momentary Assessments (EMA) capture real-time thoughts and behaviors in natural settings, producing rich longitudinal data for statistical analyses. However, the robustness of these analyses can be compromised by the large amount of missing data in EMA studies. To address this, multiple imputation, a method that replaces missing values with several plausible alternatives, has become increasingly popular. In this article, we introduce a two-step Bayesian multiple imputation framework which leverages the configuration of mixed models. We adopt and compare: (1) the Random Intercept Linear Mixed model; (2) the Mixed-effect Location Scale (MELS) model which accounts for subject variance influenced by covariates and random effects; and (3) the Shared Parameter MELS model which additio

SUBMITTER: Wei Y 

PROVIDER: S-EPMC12628364 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature