Aiding Decision-Making and Trial Design using Multivariate Network Meta-Analysis

应利用对精神健康障碍治疗的系统评价， 关于当前干预措施的有效性的信息，并使用现有数据来规划未来的临床试验。最 系统性综述产生了多变量、多组数据的图形网络，通常缺失率高达50 结果。临床试验结局缺失通常是结局报告偏倚（ORB）的结果，其中 根据观察到的显著性水平，未报告结局。这种偏倚导致汇总荟萃分析 效应大小有偏差。为了获得无偏和精确的网络荟萃分析效应量，网络应该 使用多变量网络元分析（MNMA）框架进行联合分析，该框架尚未被 提出了在由马尔可夫链蒙特卡罗工具提供动力的贝叶斯范例下， 该建议将利用结果相关性，并通过发展多国军事行动来减轻ORB的影响 模型，导致治疗效应的偏倚更小且更精确的成对估计（即使对于治疗 （不太清楚或不太清楚）。基于这些结果，预测分布将用于 告知新临床试验的操作特征。 目标：将开发多变量NMA，并将其应用于3项病例研究：随机化的系统性综述 第二代抗抑郁药治疗成人、青少年和老年人的对照试验 严重抑郁症，分别，其结果已被证明是受 报告偏倚。将与单变量NMA方法进行比较。未来试验设计的方法 将开发利用贝叶斯预测推理告知多元网络。这种方法 将完善功效和样本量计算，从而获得最佳功效和更有效的试验， 弱-或从未测试过的治疗。软件将完全推广到网络所产生的所有 临床学科，并将免费传播。

Systematic reviews of treatments for mental health disorders should be exploited in order to obtain accurate information about efficacy of current interventions, and to use existing data to plan future clinical trials. Most systematic reviews result a graphical networks of multivariate, multi-arm data, often with up to 50% missing outcomes. Missing clinical trial outcomes are frequently a result of outcome reporting bias (ORB), in which outcomes are unreported based on observed level of significance. Such bias causes pooled meta-analytic effect sizes to be biased. To obtain unbiased and precise network meta-analytic effect sizes, networks should be jointly analyzed using a multivariate network meta-analytic (MNMA) framework, which has not yet been proposed. Under a Bayesian paradigm powered by Markov chain Monte Carlo tools, the methods described in this proposal will exploit outcome correlation and mitigate effects of ORB via the development of the MNMA model, resulting in less biased and more precise pairwise estimates of treatment effects (even for treatments that have been weakly or never-compared). Based on these results, predictive distributions will be used to inform operating characteristics of new clinical trials. Goals: Multivariate NMA will be developed and apply it to 3 case studies: systematic reviews of randomized controlled trials of second-generation anti-depressants for the treatment of adult, adolescent, and older adult major depressive disorder, respectively, for which outcomes have been already shown to be subject to reporting bias. Comparisons with univariate NMA methods will be made. A methodology for future trial design will be developed utilizing Bayesian predictive inference informed by the multivariate network. This approach would refine power and sample size calculations resulting in optimally-powered and more efficient trials for weakly- or never-tested treatments. Software will be completely generalizable to networks arising from all clinical disciplines and will be disseminated freely.