Bayesian network meta-analysis for multiple endpoints

Published

Book Section

© 2017 by Taylor and Francis Group, LLC. Network meta-analysis (NMA) is a statistical technique to assess various treatment effects and compare their benefits or harms simultaneously in a systematic review. In comparative effectiveness research, NMA offers useful information to help patients and their caregivers make better decisions on their health care. Recently, many systematic reviews collecting various endpoints and methods for integrating such multivariate evidence jointly in NMA have been developed. In this chapter, we introduce BayesianNMAmethods under a missing data framework incorporating multiple outcomes by accounting for their inherent correlations. We utilize two different parameterizations which can be applied separately based on the scientific question of interest. In addition, we provide two decision-making tools, best and acceptability probabilities. We illustrate our methods using a real diabetes data example including two outcomes, and a simulation study validates the performance of our methods in terms of model selection and coverage probability. We close this chapter with a brief summary and discussion of potential future work.

Full Text

Duke Authors

Cited Authors

  • Hong, H; Price, KL; Fu, H; Carlin, BP

Published Date

  • January 1, 2017

Book Title

  • Methods in Comparative Effectiveness Research

Start / End Page

  • 385 - 407

International Standard Book Number 13 (ISBN-13)

  • 9781466511972

Digital Object Identifier (DOI)

  • 10.1201/9781315159409

Citation Source

  • Scopus