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

应该利用对精神健康障碍治疗的系统评价，以获得准确的 关于当前干预措施有效性的信息，并使用现有数据来计划未来的临床试验。多数 系统审查的结果是多变量、多臂数据的图形网络，通常有高达50%的缺失 结果。丢失临床试验结果通常是结果报告偏差(ORB)的结果，在这种情况下 根据观察到的重要程度，未报告结果。这种偏见导致了集合的元分析 要偏置的效果大小。为了获得公正和准确的网络元分析效应大小，网络应该 使用多元网络元分析(MNMA)框架进行联合分析，该框架尚未 建议。在马尔科夫链蒙特卡罗工具支持的贝叶斯范式下， 这一建议将利用结果相关性，并通过制定MNMA来减轻ORB的影响 模型，使得对治疗效果的两两估计(即使对于治疗也是如此)的偏差更小、更精确 那些被弱化或从未被比较过的国家)。基于这些结果，预测分布将被用于 告知新的临床试验的操作特点。 目标：将发展多变量NMA并将其应用于3个案例研究：随机对照的系统评价 第二代抗抑郁药治疗成人、青少年和老年人的对照试验 严重的抑郁障碍，其结果已经被证明受到 报道存在偏见。将与单变量NMA方法进行比较。未来试验设计的方法学 将利用由多变量网络通知的贝叶斯预测推理来开发。这种方法 将改进功率和样本量计算，从而实现最佳功率和更有效的试验 弱测试或从未测试过的治疗方法。软件将完全通用化到所有网络中 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.