A causal inference approach to network meta-analysis

TL;DR

This paper introduces a causal inference framework for network meta-analysis, defining conditions for valid aggregation across heterogeneous populations and adapting causal models for consistent treatment effect estimation.

Contribution

It develops a causal inference approach to identify when network meta-analysis provides valid estimates and adapts causal modeling strategies for treatment effect estimation.

Findings

Provides conditions for effect estimate identifiability

Adapts causal models for consistent estimation

Reanalyzes antibiotic efficacy data

Abstract

While standard meta-analysis pools the results from randomized trials that compare two treatments, network meta-analysis aggregates the results of randomized trials comparing a wider variety of treatment options. However, it is unclear whether the aggregation of effect estimates across heterogeneous populations will be consistent for a meaningful parameter when not all treatments are evaluated on each population. Drawing from counterfactual theory and the causal inference framework, we define the population of interest in a network meta-analysis and define the target parameter under a series of nonparametric structural assumptions. This allows us to determine the requirements for identifiability of this parameter, enabling a description of the conditions under which network meta-analysis is appropriate and when it might mislead decision making. We then adapt several modeling strategies from the causal inference literature to obtain consistent estimation of the intervention-specific mean outcome and model-independent contrasts between treatments. Finally, we perform a reanalysis of a systematic review to compare the efficacy of antibiotics on suspected or confirmed methicillin-resistant \emph{Staphylococcus aureus} in hospitalized patients.