Creating treatment and component hierarchies in component network meta-analysis

TL;DR

This paper introduces a step-by-step workflow for establishing treatment hierarchies in component network meta-analysis, addressing unique challenges not present in standard NMA.

Contribution

It extends existing methods to CNMA, explicitly identifying estimable effects and applying the workflow to real clinical networks.

Findings

Workflow successfully identifies treatment hierarchies in two clinical networks.

Addresses complexities in estimating relative effects in CNMA.

Provides guidance for both frequentist and Bayesian approaches.

Abstract

Component network meta-analysis (CNMA) is a statistical methodology that enables estimation of relative effects for multi-component treatments, such as combinations of antidepressants, and individual components, such as single antidepressants, by synthesizing data from multiple studies. A commonly desired output of a systematic review and meta-analysis is a hierarchy of the treatments in terms of a certain performance metric. Methods have been established for standard network meta-analysis (NMA), but have not yet been extended to CNMA. In particular, CNMA presents unique challenges because the set of relative effects that can be uniquely estimated is more complex to determine compared to standard NMA, and a hierarchy involving relative effects that are not uniquely estimable is misleading. We present a step-by-step workflow for answering treatment hierarchy questions in both frequentist and Bayesian CNMA, including explicitly identifying the uniquely estimable relative effects. We illustrate the workflow by posing multiple treatment hierarchy questions in two distinct networks, one concerning primary care of depression and one disconnected network investigating treatment for chronic lymphocytic leukemia.