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A Bayesian network meta-analysis for binary outcome: how to do it.

Teresa Greco1, Giovanni Landoni2, Giuseppe Biondi-Zoccai3

  • 1Anaesthesia and Intensive Care Department, San Raffaele Scientific Institute, Milan, Italy Section of Medical Statistics and Biometry Giulio A. Maccacaro, Department of Occupational and Environmental Health, University of Milan, Milan, Italy greco.teresa@hotmail.it.

Statistical Methods in Medical Research
|August 24, 2013
PubMed
Summary
This summary is machine-generated.

This study provides a practical guide to network meta-analysis (NMA) for randomized controlled trials with binary outcomes. It details steps from data collection to sensitivity analysis, aiding clinicians in understanding NMA.

Keywords:
BayesianWinBUGSanaesthetic agentsbinary outcomeshierarchical modelsmixed treatment comparisonnetwork meta-analysis

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Published on: October 11, 2018

Area of Science:

  • Medical Statistics
  • Clinical Epidemiology

Background:

  • Network meta-analysis (NMA) is increasingly used to synthesize evidence from multiple randomized controlled trials (RCTs).
  • Understanding the practical application of NMA, especially for binary outcomes, is crucial for clinicians and researchers.

Purpose of the Study:

  • To provide a comprehensive overview of the conceptual and practical aspects of conducting NMA for RCTs with binary outcomes.
  • To offer a practical tool for physicians and clinician-investigators to understand the advantages and limitations of NMA.
  • To guide users through the process of data collection, network structuring, model specification, and interpretation.

Main Methods:

  • Detailed explanation of key steps in performing NMA, including literature search, data extraction, and network construction.
  • Application of Markov Chain Monte Carlo (MCMC) methods for analyzing binomial data in NMA.
  • Illustrative case study comparing volatile agents and total intravenous anaesthetics in surgery.
  • Provision of datasets and models for the WinBUGS freeware package.

Main Results:

  • The study outlines a systematic approach to NMA for binary outcomes, emphasizing data quality and appropriate model selection.
  • The case study demonstrates the practical application of NMA, including model diagnostics and computational aspects.
  • The presented methods and tools facilitate a deeper understanding of NMA for clinical research.

Conclusions:

  • Network meta-analysis is a valuable tool for synthesizing evidence from multiple studies, particularly for binary outcomes.
  • This work provides a practical framework and computational resources to support the valid application of NMA in clinical research.
  • The study empowers clinicians and investigators to critically evaluate and conduct NMAs effectively.