Approaches for synthesising complex mental health interventions in meta-analysis

Abstract

Clinical and statistical heterogeneity are commonplace in meta-analysis of mental health interventions. One possible source of this heterogeneity is the complexity of the intervention being evaluated. Complexity may relate to the intervention, or to the way in which it is implemented; however, the most common interpretation of a complex intervention is one which has multiple, potentially interacting components. In this article we outline different analytical strategies suggested for incorporating intervention complexity in a meta-analysis.

Figure 1

Forest plot from a fixed effect meta-analysis comparing “any psychological intervention“ vs control (treatment as usual). Outcome is reduction in depressive symptoms. Analysis conducted in Review Manager.

Figure 2

Forest plot from a fixed effect meta-analysis comparing “any psychological intervention” vs control, interventions categorised as “individual therapy” and “group therapy”. Outcome is reduction in depressive symptoms. Analysis conducted in Review Manage.

Figure 3

Random effects meta-analysis comparing psychological intervention vs control (usual or standard care), interventions analysed as “clinically meaningful units”. Outcome is reduction in depressive symptoms. Analysis conducted in Review Manager. Tau2 is the between-study variance. Its square root is the estimated standard deviation of underlying effects across studies.

Figure 4

Each circle (node) represents an intervention component defined as a ‘clinically meaningful unit’ as extracted from the psychological interventions for coronary heart disease review for the outcome of depression. The solid lines indicate where there was direct information available between comparisons. CBT, cognitive–behavioural therapy; TAU, treatment as usual; BT, behavioural therapy.

Figure 5

Standardised mean difference estimates from NMA of each psychological intervention versus every other. Interventions analysed as “clinically meaningful units”. Outcome is reduction in depressive symptoms. Analysis conducted using OpenBUGS and results plotted using RevMan.

Figure 6

Network plot for a multi-component-based network meta-analysis: components identified from a full interaction model for psychological interventions for coronary heart disease. Each node represents a component, or combination of components, identified from the interventions included in the NMA for psychological interventions for coronary heart disease review for outcome of depression. The diagram depicts all possible combinations of components from the full interaction model. The solid lines indicate where there was direct information available between component comparisons. TAU/T, treatment as usual; EDU/E, educational; BEH/B, behavioural; COG/C, cognitive; RELAX/R, relaxation; SUP/S, support. + indicates a combination of components, for example, ‘E+B’ is educational and behavioural components.

Figure 7

Components based NMA: additive main effect model for psychological interventions relative to usual care. Analysis conducted using OpenBUGS and results plotted using RevMan.