School of Social and Community Medicine, University of Bristol, Bristol, UK (SD, NJW, AEA)
Department of Health Sciences, University of Leicester, Leicester, UK (AJS)
Med Decis Making. 2013 Jul;33(5):671-8. doi: 10.1177/0272989X13487257.
When multiple parameters are estimated from the same synthesis model, it is likely that correlations will be induced between them. Network meta-analysis (mixed treatment comparisons) is one example where such correlations occur, along with meta-regression and syntheses involving multiple related outcomes. These correlations may affect the uncertainty in incremental net benefit when treatment options are compared in a probabilistic decision model, and it is therefore essential that methods are adopted that propagate the joint parameter uncertainty, including correlation structure, through the cost-effectiveness model. This tutorial paper sets out 4 generic approaches to evidence synthesis that are compatible with probabilistic cost-effectiveness analysis. The first is evidence synthesis by Bayesian posterior estimation and posterior sampling where other parameters of the cost-effectiveness model can be incorporated into the same software platform. Bayesian Markov chain Monte Carlo simulation methods with WinBUGS software are the most popular choice for this option. A second possibility is to conduct evidence synthesis by Bayesian posterior estimation and then export the posterior samples to another package where other parameters are generated and the cost-effectiveness model is evaluated. Frequentist methods of parameter estimation followed by forward Monte Carlo simulation from the maximum likelihood estimates and their variance-covariance matrix represent'a third approach. A fourth option is bootstrap resampling--a frequentist simulation approach to parameter uncertainty. This tutorial paper also provides guidance on how to identify situations in which no correlations exist and therefore simpler approaches can be adopted. Software suitable for transferring data between different packages, and software that provides a user-friendly interface for integrated software platforms, offering investigators a flexible way of examining alternative scenarios, are reviewed.
当从同一个综合模型中估计多个参数时，这些参数之间很可能存在相关性。网络荟萃分析（混合治疗比较）就是这样一个例子，还有元回归和涉及多个相关结局的综合分析。这些相关性可能会影响在概率决策模型中比较治疗方案时增量净收益的不确定性，因此，采用通过成本效益模型传播联合参数不确定性（包括相关结构）的方法至关重要。本教程论文提出了 4 种与概率成本效益分析兼容的通用证据综合方法。第一种是通过贝叶斯后验估计和后验抽样进行证据综合，在这种方法中，可以将成本效益模型的其他参数纳入同一软件平台。使用 WinBUGS 软件的贝叶斯马尔可夫链蒙特卡罗模拟方法是最受欢迎的选择。第二种可能性是通过贝叶斯后验估计进行证据综合，然后将后验样本导出到另一个包中，在该包中生成其他参数并评估成本效益模型。基于最大似然估计及其方差-协方差矩阵的参数估计和正向蒙特卡罗模拟是第三种方法。第四种选择是自举重采样——一种参数不确定性的频率方法。本教程论文还提供了如何识别不存在相关性的情况的指南，因此可以采用更简单的方法。我们还对适合在不同软件包之间传输数据的软件以及为用户提供集成软件平台的用户友好界面的软件进行了审查，为调查人员提供了一种灵活的方法来检查替代方案。
