A dose-effect network meta-analysis model: an application in antidepressants

TL;DR

This paper introduces a novel dose-effect network meta-analysis model using restricted cubic splines, allowing for flexible assessment of how drug doses influence efficacy, demonstrated on antidepressants for depression.

Contribution

It develops a new DE-NMA model incorporating dose-response relationships with RCS and extends it to include covariates and class effects, improving analysis of pharmacological interventions.

Findings

All antidepressants are more effective than placebo at certain doses.

Identified optimal dose levels where efficacy surpasses placebo.

Beyond a certain dose, increasing dosage does not improve efficacy.

Abstract

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.