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Stat Med. 2015 Sep 10;34(20):2794-819. doi: 10.1002/sim.6519. Epub 2015 Apr 30.

Incorporation of individual-patient data in network meta-analysis for multiple continuous endpoints, with application to diabetes treatment.

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Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, U.S.A.
Eli Lilly and Company, Indianapolis, IN, U.S.A.
Division of Biostatistics, University of Minnesota, Minneapolis, MN, 55405, U.S.A.


Availability of individual patient-level data (IPD) broadens the scope of network meta-analysis (NMA) and enables us to incorporate patient-level information. Although IPD is a potential gold mine in biomedical areas, methodological development has been slow owing to limited access to such data. In this paper, we propose a Bayesian IPD NMA modeling framework for multiple continuous outcomes under both contrast-based and arm-based parameterizations. We incorporate individual covariate-by-treatment interactions to facilitate personalized decision making. Furthermore, we can find subpopulations performing well with a certain drug in terms of predictive outcomes. We also impute missing individual covariates via an MCMC algorithm. We illustrate this approach using diabetes data that include continuous bivariate efficacy outcomes and three baseline covariates and show its practical implications. Finally, we close with a discussion of our results, a review of computational challenges, and a brief description of areas for future research.


Bayesian hierarchical model; Markov chain Monte Carlo (MCMC); individual-patient data (IPD); multiple-treatment comparison (MTC); subgroup analysis

[Indexed for MEDLINE]

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