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

Hwanhee Hong, Haoda Fu, Karen L. Price, Bradley P. Carlin

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

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 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.

Original languageEnglish (US)
Pages (from-to)2794-2819
Number of pages26
JournalStatistics in Medicine
Volume34
Issue number20
DOIs
StatePublished - Sep 10 2015

Keywords

  • Bayesian hierarchical model
  • Individual-patient data (IPD)
  • Markov chain Monte Carlo (MCMC)
  • Multiple-treatment comparison (MTC)
  • Subgroup analysis

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