Network meta-analysis with dose-response relationships

TL;DR

This paper introduces a new method for network meta-analysis that considers drug dosage effects, improving treatment effect estimation and decision-making.

Contribution

A novel frequentist approach called DR-NMA is proposed to model dose-response relationships in network meta-analysis.

Findings

DR-NMA models linear and nonlinear dose-response relationships across multiple interventions.

Dose-response network meta-analysis yields different results compared to standard NMA methods.

The DR-NMA approach is implemented in the R package netdose for accessibility and reproducibility.

Abstract

Network meta-analysis (NMA) is a widely used method for synthesizing evidence from multiple interventions for a medical condition. However, NMA applications typically ignore the crucial role of drug dosage on intervention effects. Traditional NMAs either consider each intervention dose as an independent node or ignore the intervention dose, which may impact heterogeneity, inconsistency, or sparsity.

This paper introduces a novel frequentist approach, termed dose-response network meta-analysis (DR-NMA), which explicitly models the dose-response relationships across multiple interventions. The DR-NMA approach incorporates both linear and nonlinear dose-response relationships, including exponential, quadratic, fractional polynomials, and restricted cubic splines. DR-NMA allows for dose-dependent estimation and prediction of treatment effects across dose ranges, even in disconnected networks if common agents exist. The proposed methods are implemented in the R package netdose, enhancing accessibility and reproducibility. We illustrate the approach using clinical datasets on postoperative nausea and vomiting, as well as antidepressant treatments.

Our findings indicate that some dose-response NMA models yield substantially different results compared to standard NMA, emphasizing the critical importance of dose-response function selection in model performance.

DR-NMA provides valuable insights into the dose-dependent effects of interventions, enhancing decision-making and offering perspectives beyond traditional methods.

The online version contains supplementary material available at 10.1186/s12874-025-02754-4.

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