Presentation (CTI)

OBJECTIVES: Network meta-analysis (NMA) is a widely used method for synthesizing evidence from multiple interventions in a given medical condition. However, NMA applications typically overlook the crucial role that drug dosage plays in shaping intervention effects. Traditional NMAs often treat each intervention dose as independent, using lumping or splitting approaches to define treatment nodes, which may increase heterogeneity, inconsistency, or sparsity. Evaluating interventions through dose-response relationships can address these limitations, enhance decision-making, guide future study designs, and support drug development. We introduce a frequentist dose-response network meta-analysis (DR-NMA) framework, which explicitly models dose-response relationships across multiple interventions. The proposed methods are implemented in the R package netdose, enhancing accessibility and reproducibility.
METHODS: The DR-NMA approach incorporates both linear and non-linear dose-response relationships, including exponential, quadratic, fractional polynomials, and restricted cubic splines. Parameter estimation is conducted using weighted least-squares regression, with model fit evaluated through Q-statistics and heterogeneity metrics. We discuss methodological properties and challenges of the proposed model. A key advantage of DR-NMA is its ability to handle disconnected networks, provided that shared agents exist across subnetworks.
RESULTS: We demonstrate the application of DR-NMA using real-world clinical examples, comparing standard NMA model, linear, and non-linear dose-response models. Our findings indicate that some dose-response NMA models yield substantially different results compared to standard NMA, emphasizing the importance of dose-response function selection in model performance. DR-NMA effectively handles both sparse dose networks (2-3 dose levels per intervention) and richer dose networks (7-8 levels), demonstrating its versatility.
CONCLUSIONS: By explicitly modeling dose-response relationships, DR-NMA improves flexibility and accuracy in capturing complex patterns. It enables prediction of treatment effects across dose ranges, even in disconnected networks. This framework enhances evidence synthesis, supports informed healthcare decision-making, and optimizes treatment strategies by overcoming limitations of traditional NMA.