Network Meta-Analysis with Competing Risk Outcomes
Article first published online: 3 SEP 2010
© 2010, International Society for Pharmacoeconomics and Outcomes Research (ISPOR)
Value in Health
Volume 13, Issue 8, pages 976–983, December 2010
How to Cite
Ades, A. E., Mavranezouli, I., Dias, S., Welton, N. J., Whittington, C. and Kendall, T. (2010), Network Meta-Analysis with Competing Risk Outcomes. Value in Health, 13: 976–983. doi: 10.1111/j.1524-4733.2010.00784.x
- Issue published online: 7 DEC 2010
- Article first published online: 3 SEP 2010
- antipsychotic medication;
- Markov model;
- mixed treatment comparisons;
Background: Cost-effectiveness analysis often requires information on the effectiveness of interventions on multiple outcomes, and commonly these take the form of competing risks. Nevertheless, methods for synthesis of randomized controlled trials with competing risk outcomes are limited.
Objective: The aim of this study was to develop and illustrate flexible evidence synthesis methods for trials reporting competing risk results, which allow for studies with different follow-up times, and that take account of the statistical dependencies between outcomes, regardless of the number of outcomes and treatments.
Methods: We propose a competing risk meta-analysis based on hazards, rather than probabilities, estimated in a Bayesian Markov chain Monte Carlo (MCMC) framework using WinBUGS software. Our approach builds on existing work on mixed treatment comparison (network) meta-analysis, which can be applied to any number of treatments, and any number of competing outcomes, and to data sets with varying follow-up times. We show how a fixed effect model can be estimated, and two random treatment effect models with alternative structures for between-trial variation. We suggest methods for choosing between these alternative models.
Results: We illustrate the methods by applying them to a data set involving 17 trials comparing nine antipsychotic treatments for schizophrenia including placebo, on three competing outcomes: relapse, discontinuation because of intolerable side effects, and discontinuation for other reasons.
Conclusions: Bayesian MCMC provides a flexible framework for synthesis of competing risk outcomes with multiple treatments, particularly suitable for embedding within probabilistic cost-effectiveness analysis.