Volume 35, Issue 12 pp. 2092-2108
Tutorial in Biostatistics

A tutorial on Bayesian bivariate meta-analysis of mixed binary-continuous outcomes with missing treatment effects

Olga Gajic-Veljanoski

Olga Gajic-Veljanoski

Osteoporosis Program, University Health Network, Toronto, ON, Canada

Toronto Health Economics and Technology Assessment (THETA) Collaborative, University of Toronto, Toronto, ON, Canada

Search for more papers by this author
Angela M. Cheung

Angela M. Cheung

Osteoporosis Program, University Health Network, Toronto, ON, Canada

Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada

Department of Medicine, University of Toronto, Toronto, ON, Canada

Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

Search for more papers by this author
Ahmed M. Bayoumi

Ahmed M. Bayoumi

Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada

Department of Medicine, University of Toronto, Toronto, ON, Canada

Centre for Research on Inner City Health in the Li Ka Shing Knowledge Institute and Division of General Internal Medicine, St. Michael's Hospital, Toronto, ON, Canada

Search for more papers by this author
George Tomlinson

Corresponding Author

George Tomlinson

Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada

Department of Medicine, University of Toronto, Toronto, ON, Canada

Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

Correspondence to: George Tomlinson, University Health Network, Toronto General Hospital, 200 Elizabeth Street, Eaton North Wing 13 EN-238, Toronto, ON M5G 2C4, Canada.

E-mail: [email protected]

Search for more papers by this author
First published: 09 November 2015
Citations: 3

Abstract

Bivariate random-effects meta-analysis (BVMA) is a method of data synthesis that accounts for treatment effects measured on two outcomes. BVMA gives more precise estimates of the population mean and predicted values than two univariate random-effects meta-analyses (UVMAs). BVMA also addresses bias from incomplete reporting of outcomes.

A few tutorials have covered technical details of BVMA of categorical or continuous outcomes. Limited guidance is available on how to analyze datasets that include trials with mixed continuous-binary outcomes where treatment effects on one outcome or the other are not reported. Given the advantages of Bayesian BVMA for handling missing outcomes, we present a tutorial for Bayesian BVMA of incompletely reported treatment effects on mixed bivariate outcomes. This step-by-step approach can serve as a model for our intended audience, the methodologist familiar with Bayesian meta-analysis, looking for practical advice on fitting bivariate models. To facilitate application of the proposed methods, we include our WinBUGS code.

As an example, we use aggregate-level data from published trials to demonstrate the estimation of the effects of vitamin K and bisphosphonates on two correlated bone outcomes, fracture, and bone mineral density. We present datasets where reporting of the pairs of treatment effects on both outcomes was ‘partially’ complete (i.e., pairs completely reported in some trials), and we outline steps for modeling the incompletely reported data. To assess what is gained from the additional work required by BVMA, we compare the resulting estimates to those from separate UVMAs. We discuss methodological findings and make four recommendations. Copyright © 2015 John Wiley & Sons, Ltd.

The full text of this article hosted at iucr.org is unavailable due to technical difficulties.