Statistical Methods and Software for Multivariate Meta-analysis

多元Meta分析的统计方法和软件 主要研究员：朱海涛，医学博士，博士。 摘要 比较有效性研究(CER)旨在为医疗保健决策提供有关益处和 不同的预防战略、诊断工具和治疗选择的风险。元分析(MA)是一种 一种统计方法，结合多个独立研究的结果，以提高统计能力和 减少个别研究中的某些偏见。Ma也有能力对比不同研究的结果 并找出这些结果之间存在分歧的模式和根源。虽然移动支付的统计方法有很多， 虽然已经提出并进行了调查，但仍存在重要的研究空白。越来越多的预防措施 针对特定疾病状况的战略、评估工具和治疗选择，以及快速 成本的上升，产生了同时比较临床实践中的多个选项的需求 创新和严谨的多元移动平均方法。 遵循NIH数据科学战略计划和国家医学图书馆优先领域 “集成异类数据类型”，针对PA-18-484，本提案的总体目标是开发 先进的统计方法，以提高MA的重复性、效率和普适性，以及 开发简单易用的软件。具体地说，在这项建议中，我们将：(1)考察偏度的表现 用于量化单变量MA中的发表偏倚的标准化偏差，并开发量化方法 多变量移动平均中的发表偏差；(2)发展了一种贝叶斯分层汇总接收器操作 用于同时比较多个诊断的特征(HSROC)网络元分析框架 (3)建立了一个解释多变量MA中随机化后变量的因果推断框架； (4)开发开源、跨平台、公开易用的软件(包括R包 和SAS宏)来实现所建议的MA方法。 我们将评估这些建议方法与现有MA方法的优缺点 运用了大量的真实案例研究和广泛的模拟研究。拟议的统计方法将广泛地 适用于荟萃分析。完成这四个目标将直接有利于CER的证据基础，因为它提供了 在用户友好的软件中实施的最先进的方法，包括R包和SAS宏，它们 将向公众免费提供。它将通过促进预防、诊断和治疗来改善公共健康 治疗癌症、心血管疾病、传染病和其他疾病。

Statistical Methods and Software for Multivariate Meta-analysis Principal Investigator: Haitao Chu, M.D., Ph.D. Summary Comparative effectiveness research (CER) aims to inform health care decisions concerning the benefits and risks of different prevention strategies, diagnostic instruments and treatment options. A meta-analysis (MA) is a statistical method that combines results of multiple independent studies to improve statistical power and to reduce certain biases within individual studies. MA also has the capacity to contrast results from different studies and identify patterns and sources of disagreement among those results. While many statistical methods for MA have been proposed and investigated, important research gaps remain. The increasing number of prevention strategies, assessment instruments and treatment options for a given disease condition, as well as the rapid escalation in costs, have generated a need to simultaneously compare multiple options in clinical practice using innovative and rigorous multivariate MA methods. Following the NIH strategic plan for data science and the National Library of Medicine priority area on “integration of heterogeneous data types”, in response to PA-18-484, this proposal's overall goal is to develop cutting-edge statistical methods to enhance the reproducibility, efficiency and generalizability of MA, as well as to develop easy-to-use software. Specifically, in this proposal, we will: (1) examine the performance of skewness of the standardized deviates for quantifying publication bias in univariate MA, and develop methods quantifying publication bias in multivariate MA; (2) develop a Bayesian hierarchical summary receiver operating characteristic (HSROC) network meta-analysis framework for simultaneously comparing multiple diagnostic tests; (3) develop a causal inference framework accounting for post-randomization variables in multivariate MA; and (4) develop open-source, cross-platform, publicly available and easy-to-use software (including R packages and SAS macros) to implement the proposed MA methods. We will evaluate the strengths and weaknesses of these proposed methods versus existing MA methods using many real case studies and extensive simulation studies. The proposed statistical methods will be broadly applicable to meta-analysis. Completing these four aims will directly benefit the CER evidence base by providing state-of-the-art methods implemented in user-friendly software including R packages and SAS macros, which will be made freely available to the public. It will improve public health by facilitating prevention, diagnosis, and treatment of cancers and cardiovascular, infectious, and other diseases.