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Journal of Causal Inference

Ed. by Imai, Kosuke / Pearl, Judea / Petersen, Maya Liv / Sekhon, Jasjeet / van der Laan, Mark J.

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2193-3685
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A Causal Inference Approach to Network Meta-Analysis

Mireille E Schnitzer / Russell J Steele / Michèle Bally
  • Department of Pharmacy, Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Ian Shrier
  • Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, 3755 Cote Sainte Catherine Road, Montreal, Quebec H3T 1E2, Canada
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-11-15 | DOI: https://doi.org/10.1515/jci-2016-0014

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 Staphylococcus aureus in hospitalized patients.

Keywords: g-formula; identifiability; network meta-analysis; nonparametric structural equation; propensity score; systematic review; TMLE

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About the article

Published Online: 2016-11-15

Published in Print: 2016-09-01


Citation Information: Journal of Causal Inference, Volume 4, Issue 2, 20160014, ISSN (Online) 2193-3685, ISSN (Print) 2193-3677, DOI: https://doi.org/10.1515/jci-2016-0014.

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