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Related Experiment Video

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An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Network meta-analysis with competing risk outcomes.

A E Ades¹, Ifigeneia Mavranezouli, Sofia Dias

¹Department of Community Based Medicine, University of Bristol, Cotham Hill, Bristol, UK. t.ades@bristol.ac.uk

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research

|September 10, 2010

Summary

This study introduces a flexible Bayesian meta-analysis method for synthesizing competing risks data from multiple trials. The approach handles various follow-up times and treatment comparisons, aiding cost-effectiveness analysis.

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Area of Science:

Biostatistics
Health Economics
Evidence Synthesis

Background:

Cost-effectiveness analysis frequently requires synthesizing data on multiple intervention outcomes, often involving competing risks.
Current methods for synthesizing randomized controlled trials with competing risk outcomes are limited.

Purpose of the Study:

To develop and demonstrate flexible evidence synthesis methods for randomized controlled trials reporting competing risk results.
The methods accommodate varying follow-up times and statistical dependencies between outcomes, irrespective of the number of outcomes and treatments.

Main Methods:

A competing risk meta-analysis framework based on hazards, utilizing a Bayesian Markov chain Monte Carlo (MCMC) approach.
The method extends existing mixed treatment comparison (network) meta-analysis, applicable to multiple treatments and outcomes with varying follow-up durations.
Includes estimation of fixed and two random effect models, with guidance on model selection.

Main Results:

The developed methods were applied to a dataset of 17 trials comparing nine antipsychotic treatments for schizophrenia.
The analysis included three competing outcomes: relapse, discontinuation due to side effects, and discontinuation for other reasons.

Conclusions:

Bayesian MCMC offers a versatile framework for synthesizing competing risk outcomes across multiple treatments.
This approach is particularly well-suited for integration into probabilistic cost-effectiveness analyses.