An Exploratory Graphical Analysis of the Montgomery-Åsberg Depression Rating Scale Pre- and Post-Treatment using Pooled Antidepressant Trial Secondary Data

Abstract

BackgroundThe 10-item Montgomery-Åsberg 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.AimsTo assess the network structure and stability of the MADRS in clinical settings at baseline (pre-treatment) and outcome (post-treatment), and to determine a configurally invariant and stable model across time-points and treatment groups (placebo and intervention).MethodIndividual participant data for 6,962 participants from 15 clinical trials 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 [4-12 week bracket]). Bootstrapping techniques were applied to obtain revised models in line with minimum item and dimension stability thresholds. Finalised models were determined in relation to the optimally performing revised model to pursue configural invariance.ResultsEmpirical 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.ConclusionsAn 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 symptoms might be most efficacious in treating depression among clinical trial participants.