  Network meta-analysis (NMA) has been used to answer a range of clinical
questions about the preferable intervention for a given condition. Although the
effectiveness and safety of pharmacological agents depend on the dose
administered, NMA applications typically ignore the role that drugs dosage play
on the results. This leads to more heterogeneity in the network. In this paper
we present a suite of network meta-analysis models that incorporates the
dose-effect relationship (DE-NMA) using restricted cubic splines (RCS). We
extend the model into a dose-effect network meta-regression to account for
study-level covariates and for groups of agents in a class-effect DE-NMA model.
We apply the models to a network of aggregate data about the efficacy of 21
antidepressants and placebo for depression. We found that all antidepressants
are more efficacious than placebo after a certain dose. We also identify the
dose level in which each antidepressant effect exceeds that of placebo and
estimate the dose beyond the effect of the antidepressants no longer increases.
The DE-NMA model with RCS takes a flexible approach to modelling the
dose-effect relationship in multiple interventions, so decision-makers can use
them to inform treatment choice.
