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

使用多元网络元分析辅助决策和试验设计

基本信息

批准号：
9473144
负责人：
Stacia DeSantis
金额：
$ 4.62万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2017-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9473144
关键词：
Address Adolescent Adult American Medical Association Antidepressive Agents Case Study Characteristics Clinical Clinical Trials Clinical Trials Design Complex Computer software Data Data Set Decision Making Development Discipline Elderly Environment Future Generations Goals Individual Information Networks Intervention Journals Major Depressive Disorder Markov Chains Markov chain Monte Carlo methodology Mental Health Mental disorders Meta-Analysis Methodology Methods Modeling National Institute of Mental Health Outcome Performance Public Health Randomized Controlled Trials Reporting Research Research Domain Criteria Research Personnel Sample Size Specific qualifier value Statistical Methods Structure Testing United States Food and Drug Administration arm base demographics design effective therapy flexibility improved innovation novel practical application public health relevance simulation software development systematic review tool treatment effect trial design

项目摘要

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.

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。