Bayesian unanchored additive models for component network meta-analysis

TL;DR

This paper introduces Bayesian unanchored additive models for component network meta-analysis, clarifies assumptions about additivity, and demonstrates their superior performance through simulations and real data application.

Contribution

It develops two novel Bayesian unanchored CNMA models with a unified notation, addressing previous assumptions and improving model fit and performance.

Findings

Unanchored Bayesian CNMA models outperform anchored models in simulations.

The models provide accurate treatment rankings and credible intervals.

Application to real data demonstrates practical utility.

Abstract

Component network meta-analysis (CNMA) models are an extension of standard network meta-analysis (NMA) models which account for the use of multicomponent treatments in the network. This article contributes innovatively to several statistical aspects of CNMA. First, by introducing a unified notation, we establish that currently available methods differ in the way they assume additivity, an important distinction that has been overlooked so far in the literature. In particular, one model uses a more restrictive form of additivity than the other which we term an anchored and unanchored model, respectively. We show that an anchored model can provide a poor fit to the data if it is misspecified. Second, given that Bayesian models are often preferred by practitioners, we develop two novel unanchored Bayesian CNMA models presented under the unified notation. An extensive simulation study examining bias, coverage probabilities, and treatment rankings confirms the favorable performance of the novel models. This is the first simulation study to compare the statistical properties of CNMA models in the literature. Finally, the use of our novel models is demonstrated on a real dataset, and the results of CNMA models on the dataset are compared.