Personalized Treatment Hierarchies in Bayesian Network Meta-Analysis

TL;DR

This paper introduces a method for creating personalized treatment hierarchies in Bayesian Network Meta-Analysis by incorporating Treatment-Covariate Interactions, demonstrated on depression treatment data.

Contribution

It presents a novel approach to tailor treatment rankings based on covariate profiles within Bayesian NMA models including TCIs.

Findings

Treatment hierarchies vary with covariate profiles.

Method demonstrated on real depression treatment network.

Personalized treatment recommendations can be derived.

Abstract

Network Meta-Analysis (NMA) is an increasingly popular evidence synthesis tool that can provide a ranking of competing treatments, also known as a treatment hierarchy. Treatment-Covariate Interactions (TCIs) can be included in NMA models to allow relative treatment effects to vary with covariate values. We show that in an NMA model that includes TCIs, treatment hierarchies should be created with a particular covariate profile in mind. We outline the typical approach for creating a treatment hierarchy in standard Bayesian NMA and show how a treatment hierarchy for a particular covariate profile can be created from an NMA model that estimates TCIs. We demonstrate our methods using a real network of studies for treatments of major depressive disorder.