Network meta-analysis of multicomponent interventions

Abstract

In network meta-analysis (NMA), treatments can be complex interventions, for example, some treatments may be combinations of others or of common components. In standard NMA, all existing (single or combined) treatments are different nodes in the network. However, sometimes an alternative model is of interest that utilizes the information that some treatments are combinations of common components, called component network meta-analysis (CNMA) model. The additive CNMA model assumes that the effect of a treatment combined of two components A and B is the sum of the effects of A and B, which is easily extended to treatments composed of more than two components. This implies that in comparisons equal components cancel out. Interaction CNMA models also allow interactions between the components. Bayesian analyses have been suggested. We report an implementation of CNMA models in the frequentist R package netmeta. All parameters are estimated using weighted least squares regression. We illustrate the application of CNMA models using an NMA of treatments for depression in primary care. Moreover, we show that these models can even be applied to disconnected networks, if the composite treatments in the subnetworks contain common components.

Keywords: combination therapies; complex interventions; disconnected networks; multiple interventions; network meta-analysis.

Figure 1

Left panel: A disconnected network of three two‐arm studies with six treatments. Right panel: The CNMA model adds new joins between the treatments having common components, thus reconnecting the network

Figure 2

Comparing results of the additive model (red), an interaction model (blue), and the standard NMA model (black) for the depression data by a forest plot