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

Background The 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. Aims To 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). Method Individual 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. 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. 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 symptoms might be most efficacious in treating depression among clinical trial participants

Applying advanced psychometric approaches yields differential randomised trial effect sizes: secondary analysis of individual participant data from antidepressant studies using the Hamilton Rating Scale for Depression

Objective: As multiple sophisticated techniques are used to evaluate psychometric scales, in theory reducing error and enhancing measurement of patient reported outcomes, we aimed to determine whether applying different psychometric analyses would demonstrate important differences in treatment effects. Study Design and Setting: We conducted secondary analysis of individual participant data from 20 antidepressant treatment trials obtained from Vivli.org (n=6,843). Pooled item-level data from the HRSD-17 were analysed using confirmatory factory analysis (CFA), item response theory (IRT) and network analysis (NA). Multilevel models were used to analyse differences in trial effects at approximately 8 weeks (range 4-12 weeks) post-treatment commencement, with standardised mean differences calculated as Cohen’s d. Effect size outcomes for the original total depression scores were compared with psychometrically-informed outcomes based on abbreviated and weighted depression scores. Results: Several items performing poorly during psychometric analyses and were eliminated, resulting in different models being obtained for each approach. Treatment effects were modified as follows per psychometric approach: 10.4%-14.9% increase for CFA, 0%-2.9% increase for IRT, 14.9%-16.4% reduction for NA.  Conclusion: Psychometric analyses differentially moderate effect size outcomes depending on the method used. In a 20-trial sample, factor analytic approaches increased treatment effect sizes relative to the original outcomes, NA decreased them, and IRTresults reflected original trial outcomes.</div

The effects of advanced factor analysis approaches on outcomes in randomised trials for depression: protocol for secondary analysis of individual participant data

Background: Modern psychometric methods make it possible to eliminate nonperforming items and reduce measurement error. Application of these methods to existing outcome measures can reduce variability in scores, and may increase treatment effect sizes in depression treatment trials. Aims: We aim to determine whether using confirmatory factor analysis techniques can provide better estimates of the true effects of treatments, by conducting secondary analyses of individual patient data from randomised trials of antidepressant therapies. Method: We will access individual patient data from antidepressant treatment trials through Clinicalstudydatarequest.com and Vivli.org, specifically targeting studies that used the Hamilton Rating Scale for Depression (HRSD) as the outcome measure. Exploratory and confirmatory factor analytic approaches will be used to determine pre-treatment (baseline) and post-treatment models of depression, in terms of the number of factors and weighted scores of each item. Differences in the derived factor scores between baseline and outcome measurements will yield an effect size for factor-informed depression change. The difference between the factor-informed effect size and each original trial effect size, calculated with total HRSD-17 scores, will be determined, and the differences modelled with meta-analytic approaches. Risk differences for proportions of patients who achieved remission will also be evaluated. Furthermore, measurement invariance methods will be used to assess potential gender differences. Conclusions: Our approach will determine whether adopting advanced psychometric analyses can improve precision and better estimate effect sizes in antidepressant treatment trials. The proposed methods could have implications for future trials and other types of studies that use patient-reported outcome measures.</p

Exploring the effects of network analysis on depression trial outcomes: protocol for secondary analysis of individual participant data

Background: Network analysis (NA) is a modern statistical method for exploring relationships and patterns in complex data. NA techniques can also be used to eliminate unstable or nonperforming items, potentially reducing measurement error. However, the use of NA to improve measures used in randomised controlled trials has been limited, and it is unknown whether applying such techniques could impact trial effect size outcomes. Aim: We aim to determine whether network analysis can impact clinical trial effects by reducing measurement error in depression models and subsequently modifying trial effect size outcomes.  Method: We will analyse individual participant data (IPD) from multiple depression trials that used the Montgomery Åsberg Depression Rating Scale (MADRS) as a depression measure. Data will be accessed from Vivli.org. A sequence of network modelling, followed by bootstrapping and stability analysis, will be performed to revise models of the MADRS at baseline and outcome. Net scores will be derived from abbreviated models and the differences in original trial outcomes versus the abbreviated outcomes utilising net scores, will be the effect of interest. Effect size outcomes will be modelled using multilevel linear regression.  Discussion: This study will determine whether network modelling can improve precision and inform better estimates of effect sizes in antidepressant treatment trials. The outcome of the proposed study could inform a shift in the way in which clinical trial data, and indeed data from other types of studies, may be analysed. </p

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

Background: The 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. Aims: To 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). Method: Individual 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. 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. 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 symptoms might be most efficacious in treating depression among clinical trial participants.</p