Abstract:
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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 due to limited access to such data. In this paper, we propose a Bayesian IPD NMA modeling framework for multiple continuous outcomes under both contrast- and arm-based parameterizations. We incorporate individual covariate-by-treatment interactions to facilitate personalized decision-making. Furthermore, we can find sub-populations performing well with a certain drug in terms of predictive outcomes. We also impute missing individual covariates via a 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.
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