title: A Bayesian network meta-analysis for binary outcome: how to do it.
abstract: This study presents an overview of conceptual and practical issues of a network meta-analysis (NMA), particularly focusing on its application to randomised controlled trials with a binary outcome of interest. We start from general considerations on NMA to specifically appraise how to collect study data, structure the analytical network and specify the requirements for different models and parameter interpretations, with the ultimate goal of providing physicians and clinician-investigators a practical tool to understand pros and cons of NMA. Specifically, we outline the key steps, from the literature search to sensitivity analysis, necessary to perform a valid NMA of binomial data, exploiting Markov Chain Monte Carlo approaches. We also apply this analytical approach to a case study on the beneficial effects of volatile agents compared to total intravenous anaesthetics for surgery to further clarify the statistical details of the models, diagnostics and computations. Finally, datasets and models for the freeware WinBUGS package are presented for the anaesthetic agent example.
authors: Anaesthesia 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.
authors: Teresa
authors: T
authors: Greco
authors: Anaesthesia and Intensive Care Department, San Raffaele Scientific Institute, Milan, Italy.
authors: Giovanni
authors: G
authors: Landoni
authors: Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Italy Meta-analysis and Evidence based medicine Training in Cardiology (METCARDIO), Italy.
authors: Giuseppe
authors: G
authors: Biondi-Zoccai
authors: Meta-analysis and Evidence based medicine Training in Cardiology (METCARDIO), Italy Division of Cardiology, Department of Internal Medicine, Città Della Salute e Della Scienza, Turin, Italy.
authors: Fabrizio
authors: F
authors: D'Ascenzo
authors: Anaesthesia and Intensive Care Department, San Raffaele Scientific Institute, Milan, Italy.
authors: Alberto
authors: A
authors: Zangrillo
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method; D000071076:Network Meta-Analysis as Topic
keywords: Bayesian; WinBUGS; anaesthetic agents; binary outcomes; hierarchical models; mixed treatment comparison; network meta-analysis