An exploratory graphical analysis of the Montgomery-Asberg Depression Rating Scale pre- and post-treatment using pooled antidepressant trial secondary data

Background: The 10-item Montgomery-Asberg Depression Rating Scale (MADRS) is a commonly used measure of depression in antidepressant clinical trials. Numerous studies have adopted classical test theory perspectives to assess the psychometric properties of this scale, finding generally positive results. However, its network configural structure and stability is unexplored across different time-points and treatment groups. Aims: To assess the network structure and stability of the MADRS in clinical settings pre- and post-treatment, and to determine a configurally invariant and stable model across time-points and treatment groups (placebo and intervention). Method: Individual participant data for 6440 participants from 14 clinical trials of major depressive disorder was obtained from the data repository Vivli.org. Exploratory Graphical Analysis (EGA) was used to identify empirical models pre-treatment (baseline) and post-treatment (8-week outcome). Bootstrapping techniques were applied to obtain optimised configurally invariant models. Results: Empirical models presented with performance issues at baseline and for the placebo group at outcome. An abbreviated 8-item single-community model was found to be stable and configurally invariant across time-points and treatment groups. Symptoms such as low mood and lassitude showed most centrality across all models. Limitations: Metric invariance could not be explored due to research environment limitations. Conclusions: An 8-item one-community variant of the MADRS may provide optimal performance when conducting network analyses of antidepressant clinical trial outcomes. Findings suggest that interventions targeting low mood and lassitude might be most efficacious in treating depression among clinical trial participants. Further considerations of the potential impact on trial design and analysis should be explored.