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Activity Number: 63
Type: Topic Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Biopharmaceutical Section
Abstract #320749
Title: Incorporation of Individual Patient Data in Network Meta-Analysis for Multiple Continuous Endpoints, with Application to Diabetes Treatment
Author(s): Hwanhee Hong* and Haoda Fu and Karen Price and Bradley Carlin
Companies: Johns Hopkins Bloomberg School of Public Health and and Eli Lilly and Company and University of Minnesota
Keywords: Bayesian hierarchical model ; Multiple-treatment comparison ; Individual-patient data ; Subgroup analysis
Abstract:

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.


Authors who are presenting talks have a * after their name.

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