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Incorporation of individual‐patient data in network meta‐analysis for multiple continuous endpoints, with application to diabetes treatment
Author(s) -
Hong Hwanhee,
Fu Haoda,
Price Karen L.,
Carlin Bradley P.
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6519
Subject(s) - covariate , bivariate analysis , computer science , missing data , scope (computer science) , bayesian probability , bayesian network , data mining , baseline (sea) , meta analysis , contrast (vision) , outcome (game theory) , machine learning , artificial intelligence , medicine , mathematics , oceanography , mathematical economics , programming language , geology
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. Copyright © 2015 John Wiley & Sons, Ltd.