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Statistical Methods and Software for Multivariate Meta-analysis

多元荟萃分析的统计方法和软件

基本信息

批准号：
10405472
负责人：
Lifeng Lin
金额：
$ 32.5万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-10 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10405472
关键词：
Accounting Address Area Assessment tool Attention Benefits and Risks Cardiovascular system Case Study Comparative Effectiveness Research Complex Computer software Data Data Science Development Diagnosis Diagnostic Diagnostic tests Disease Dissemination and Implementation Doctor of Medicine Doctor of Philosophy Evaluation Evidence Based Medicine Goals Gold Healthcare Heterogeneity Individual Measures Medical Meta-Analysis Methodology Methods Modeling Outcome Pattern Performance Phase Prevention Prevention strategy Principal Investigator Property Public Health Publication Bias Publishing Randomized Randomized Clinical Trials Receiver Operating Characteristics Reproducibility Research Research Personnel Science Scientist Source Standardization Statistical Data Interpretation Statistical Methods Strategic Planning Stratification Testing United States National Institutes of Health United States National Library of Medicine Weight cancer therapy clinical practice cost evidence base heterogenous data improved innovation instrument interest non-compliance open source rapid growth response simulation systematic review user friendly software

项目摘要

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.

多元荟萃分析的统计方法和软件首席研究员：褚海涛，医学博士、博士概括比较有效性研究 (CER) 旨在为有关益处和益处的医疗保健决策提供信息不同预防策略、诊断仪器和治疗方案的风险。荟萃分析（MA）是结合多项独立研究结果的统计方法，以提高统计功效并减少个别研究中的某些偏见。 MA 还能够对比不同研究的结果并确定这些结果之间不一致的模式和来源。虽然 MA 的统计方法有很多虽然已经提出并进行了研究，但仍然存在重要的研究空白。预防数量不断增加针对特定疾病状况的策略、评估工具和治疗方案，以及快速成本的上升，需要在临床实践中同时比较多种选择创新且严格的多元 MA 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 “异构数据类型的集成”，响应 PA-18-484，该提案的总体目标是开发尖端统计方法，以提高 MA 的可重复性、效率和普遍性，以及开发易于使用的软件。具体来说，在这个提案中，我们将：（1）检查偏度的表现用于量化单变量 MA 中发表偏倚的标准化偏差，并开发量化方法多元 MA 中的发表偏倚； (2) 开发贝叶斯分层汇总接收器操作特征（HSROC）网络荟萃分析框架，用于同时比较多个诊断测试； (3) 开发一个因果推理框架来解释多元 MA 中的随机化后变量； (4) 开发开源、跨平台、公开且易于使用的软件（包括R包）和 SAS 宏）来实施所提出的 MA 方法。我们将评估这些提出的方法与现有 MA 方法的优缺点使用许多真实案例研究和广泛的模拟研究。拟议的统计方法将广泛适用于荟萃分析。完成这四个目标将直接有利于 CER 证据库，因为提供在用户友好的软件中实现的最先进的方法，包括 R 包和 SAS 宏，将免费向公众开放。它将通过促进预防、诊断和治疗来改善公共卫生治疗癌症以及心血管、传染病和其他疾病。