A causal inference approach to network meta-analysis
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.
Cite
@article{arxiv.1506.01583,
title = {A causal inference approach to network meta-analysis},
author = {Mireille E. Schnitzer and Russell J. Steele and Michèle Bally and Ian Shrier},
journal= {arXiv preprint arXiv:1506.01583},
year = {2016}
}