Statistical Methods and Software for Multivariate Meta-analysis

多元荟萃分析的统计方法和软件

• 批准号: 10015333
• 负责人: Haitao Chu
• 金额: $ 32.55万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-10 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10015333
• 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; 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

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：Haitao Chu，M.D。，博士 概括 比较有效性研究（CER）旨在为医疗保健决定提供有关收益和 不同预防策略，诊断工具和治疗方案的风险。荟萃分析（MA）是 统计方法结合了多项独立研究的结果以提高统计能力和 减少单个研究中的某些偏见。 MA还具有与不同研究的对比结果的能力 并确定这些结果之间分歧的模式和来源。而MA的许多统计方法 已经提出和研究了，仍然存在重要的研究差距。预防数量增加 给定疾病状况的策略，评估工具和治疗方案以及快速 成本升级，已经产生了只需比较临床实践中的多种选择 创新和严格的多元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 “异构数据类型的整合”，为了响应PA-18-484，该提案的总体目标是开发 提高MA的可重复性，效率和概括性的尖端统计方法以及 开发易于使用的软件。具体来说，在此提案中，我们将：（1）检查偏度的性能 用于量化单变量MA中出版物偏见的标准化偏差，并开发量化的方法 多元MA的出版偏见； （2）开发贝叶斯分层摘要接收器操作 特征性（HSROC）网络荟萃分析框架，用于简单比较多个诊断 测试； （3）开发一个因多变量MA中随机化变量的因果推理框架； （4）开发开源，跨平台，公开可用且易于使用的软件（包括R软件包 和SAS宏）以实施提出的MA方法。 我们将评估这些提议的方法与现有MA方法的优势和劣势 使用许多实际案例研究和广泛的仿真研究。提出的统计方法将广泛 适用于荟萃分析。完成这四个目标将直接通过提供CER证据基础。 在用户友好的软件中实施的最新方法，包括R套件和SAS宏， 将免费提供给公众。它将通过促进预防，诊断和 癌症和心血管，传染和其他疾病的治疗。