A Causal Inference Approach to Network Meta-Analysis

Schnitzer Mireille E (), Steele Russell J, Bally Michèle and Shrier Ian
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Schnitzer Mireille E: Université de Montreal, Faculté de pharmacie, Montreal, Quebec, Canada
Steele Russell J: Department of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada
Bally Michèle: Department of Pharmacy, Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
Shrier Ian: 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

Journal of Causal Inference, 2016, vol. 4, issue 2, 19

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 (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:4:y:2016:i:2:p:19:n:1

DOI: 10.1515/jci-2016-0014

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