Network Meta-Analysis with Competing Risk Outcomes
Network Meta-Analysis with Competing Risk Outcomes
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DOI：
10.1111/j.1524-4733.2010.00784.x
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发表时间：
2010-12-01
期刊：
影响因子：
4.5
通讯作者：
Kendall, Tim
中科院分区：
文献类型：
--
作者：
Ades, A. E.;Mavranezouli, Ifigeneia;Kendall, Tim
背景：成本效益分析通常要求提供关于多种结果干预措施有效性的信息，通常这些信息采取相互竞争的风险的形式。然而，具有竞争风险结果的随机对照试验的合成方法是有限的。目的：本研究的目的是开发和说明灵活的证据合成方法，用于报告竞争风险结果的试验，允许不同随访时间的研究，并且考虑结果之间的统计相关性，而不管结果和治疗的数量。方法：我们提出了基于风险而不是概率的竞争风险荟萃分析，在贝叶斯马尔可夫链蒙特卡罗(MCMC)框架下使用WinBUGS软件。我们的方法建立在混合治疗比较(网络)荟萃分析的现有工作基础上，该分析可以应用于任何数量的治疗、任何数量的相互竞争的结果，以及具有不同随访时间的数据集。我们展示了如何估计固定效应模型，以及两个具有不同结构的随机治疗效应模型用于试验间差异。我们建议在这些替代模型之间进行选择的方法。结果：我们将这些方法应用于一个包含17个试验的数据集，比较了包括安慰剂在内的9种抗精神病药物治疗精神分裂症的三种相互竞争的结果：复发、因无法忍受的副作用而停止使用以及因其他原因停止使用。结论：贝叶斯MCMC为综合多种治疗方法的竞争风险结果提供了一个灵活的框架，特别适合嵌入到概率成本效果分析中。
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.