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 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.

Evaluating the psychometric structure of the Hamilton Rating Scale for Depression pre- and post-treatment in antidepressant randomised trials: Secondary analysis of 6843 individual participants from 20 trials.

BACKGROUND: The 17-item Hamilton Rating Scale for Depression (HRSD-17) is the most popular depression measure in antidepressant clinical trials. Prior evidence indicates poor replicability and inconsistent factorial structure. This has not been studied in pooled randomised trial data, nor has a psychometrically optimal model been developed. AIMS: To examine the psychometric properties of the HRSD-17 for pre-treatment and post-treatment clinical trial data in a large pooled database of antidepressant randomised controlled trial participants, and to determine an optimal abbreviated version. METHOD: Data for 6843 participants were obtained from the data repository Vivli.org and randomly split into groups for exploratory (n = 3421) and confirmatory (n = 3422) factor analysis. Invariance methods were used to assess potential sex differences. RESULTS: The HRSD-17 was psychometrically sub-optimal and non-invariant for all models. High item variances and low variance explained suggested redundancy in each model. EFA failed at baseline and produced four item models for outcome groups (five for placebo-outcome), which were metric but not scalar invariant. CONCLUSIONS: In antidepressant trial data, the HRSD-17 was psychometrically inadequate and scores were not sex invariant. Neither full nor abbreviated HRSD models are suitable for use in clinical trial settings and the HRSD's status as the gold standard should be reconsidered.

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.