Using network meta-analysis to identify effective components of complex mental health interventions

Network meta-analysis (NMA) allows pooling evidence on multiple interventions from a set of randomised controlled trials (RCTs), each of which compare two or more of the interventions of interest. This feature enables to address relevant questions for practitioners and policy makers across many health areas including mental health. Interventions designed to prevent or treat mental health problems tend to be complex, in the sense that they may include several active ingredients or “components”. If each combination of components is considered a separate intervention, then NMA could be used to simultaneously compare the different interventions. However, NMA requires that the comparisons made by the RCTs form a connected network, in other words that there is a path of comparisons between any two included interventions. This is unlikely to be the case with complex interventions, due to the large number of possible component combinations, and even if such a network is connected, the resulting analysis may lead to imprecise estimates. Recently, component-level NMA regression methods have been developed within a Bayesian framework to allow estimation of the additive contribution of components and/or combinations of components of complex interventions while fully respecting the randomised structure of the evidence. This approach allows meaningful conclusions on effectiveness of components of complex interventions, whilst overcoming issues with connected networks and low precision with standard NMA. In this presentation, we will illustrate the use of standard and component-level NMA with two examples in the context of mental health interventions. In the first example, we compared the effectiveness of different types of therapy, different components and combinations of components and aspects of delivery used in cognitive-behavioural therapy (CBT) interventions for adult depression. We included 91 RCTs and found strong evidence that CBT interventions yielded a larger short-term decrease in depression scores compared to treatment-as-usual, with a standardised difference in mean change of -1.11 (95% credible interval -1.62 to -0.60) for face-to-face CBT, -1.06 (-2.05 to -0.08) for hybrid CBT, and -0.59 (-1.20 to 0.02) for multimedia CBT, whereas wait list control showed a detrimental effect of 0.72 (0.09 to 1.35). We found no evidence of specific effects of any content components or combinations of components, and importantly, we found that multimedia and hybrid CBT might be as effective as face-to-face CBT, although results need to be interpreted cautiously. The second application that we will discuss is an ongoing systematic review where the overall aim is to identify the most effective intervention component(s), or combination of components, for universal, selective, and indicated prevention of anxiety and depression problems in children and young people. We will present results based on NMA models both at the therapy and at the component levels. Last, we will conclude the presentation with a summary of the advantages of component-level NMA methods to explore the impact of different components of complex interventions on mental health outcomes, alongside the challenges that researchers might find when implementing this approach.