A review of methods for addressing components of interventions in meta-analysis

Abstract

Many healthcare interventions are complex, consisting of multiple, possibly interacting, components. Several methodological articles addressing complex interventions in the meta-analytical context have been published. We hereby provide an overview of methods used to evaluate the effects of complex interventions with meta-analytical models. We summarized the methodology, highlighted new developments, and described the benefits, drawbacks, and potential challenges of each identified method. We expect meta-analytical methods focusing on components of several multicomponent interventions to become increasingly popular due to recently developed, easy-to-use, software tools that can be used to conduct the relevant analyses. The different meta-analytical methods are illustrated through two examples comparing psychotherapies for panic disorder.

Fig 1. Forest plot for a subgroup of studies that compared combined PT+AD versus AD for the rate of response (i.e. substantial improvement) at 2–4 months in patients with panic disorder with or without agoraphobia.

Subgroup analysis is conducted for the three components of the combined psychotherapy: (1) Behaviour therapy, (2) Cognitive-behaviour therapy, (3) Psychodynamic psychotherapy, and others. The analysis provided with the inverse-variance random-effects model with R package meta [41]. The risk ratio was used as an effect size. Heterogeneity was estimated with the DerSimonian-Laird estimation method.

Fig 2. Network plot for psychological interventions at intervention and component level.

On the left, we show the network plot at the intervention level (Fig 2(A)). Each circle (node) represents an intervention. Solid lines indicate comparisons for which direct information was available. Abbreviations for 11 interventions: Waiting List (WL); Supportive Psychotherapy (SP); Self Help Physiological Therapy (SH-PT); Self Help Cognitive Behavioral Therapy (SH-CBT); Self Help Behavioral Therapy (SH-BT); Physiological Therapy (PT); No Intervention (NT); Cognitive Therapy (CT); Behavioral Therapy (BT); Cognitive Behavioral Therapy (CBT); Third Wave CBT (3W)). On the right, we show the network plot at the component level (Fig 2(B)). Each node corresponds to a particular combination of components. Abbreviations for 12 components: waiting component (wl); placebo effect (pl); psychological support (ps); psychoeducation (pe); breathing retraining (br); progressive/applied muscle relaxation (mr); cognitive restructuring (cr); interoceptive exposure (ine); in vivo exposure (ive); virtual reality exposure (vre); 3w, third-wave components (3w); face-to-face setting (ftf).

Fig 3. Results from fitting the additive and interaction CNMA model in the panic disorder dataset.

The interaction model assumes only one interaction term between components psychoeducation (pe) and interoceptive exposure (ine). Both analyses were conducted in the frequentist framework with the discomb() command in the netmeta package [33, 60]. Estimation of the combination component interventions versus the reference waiting list component (wl) is provided in the OR scale with their 95% confidence intervals (CI). The red color is for the additive CNMA model, blue for the interaction CNMA model.