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：
9108437
负责人：
Haitao Chu
金额：
$ 20.38万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-07-15 至 2018-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9108437
关键词：
Address Archives Assessment tool Benefits and Risks Cardiovascular system Clinical Clinical assessments Complex Computer software Correlation Studies Data Data Analyses Development Diagnosis Diagnostic tests Disease Dose Evaluation Goals Healthcare Imagery Individual Intervention Investigation Joints Journals Language Letters Linear Regressions Literature Malignant Neoplasms Measures Medical Meta-Analysis Methodology Methods Odds Ratio Outcome Pain Patients Peer Review Performance Phase Placebos Prevalence Property Public Health Publication Bias Publications Publishing Randomized Clinical Trials Research Personnel Sample Size Sensitivity and Specificity Software Framework Specificity Statistical Data Interpretation Statistical Methods Testing Writing base clinical practice comparative effectiveness cost design effectiveness research gabapentin improved individual patient innovation interest open source primary outcome programs response simulation software development statistics theories tool user friendly software user-friendly web page web site

项目摘要

DESCRIPTION (provided by applicant): Comparative effectiveness research (CER) is aiming at informing health care decisions concerning the benefits and risks of different diagnosis and treatment options. The growing number of assessment instruments and treatment options for a given condition, as well as the rapid escalation in their costs, has generated the increasing need for scientifically rigorous comparisons of multiple diagnostic tests and multiple treatments in clinical practice via multivariate meta-analysis. While multivariate meta-analysis methods are certainly very useful as they can provide estimates with better statistical properties, there are many additional challenges compared to traditional univariate meta-analysis. In response to PA-13-303, the overall goal of this proposal is to develop cutting-edge and robust multivariate meta-analysis methods to enhance the consistency, applicability, and generalizability, and a completely open-source, cross-platform, publicly available and easy-to-use R software package. In addition, we will integrate the to-be-developed R package into the publicly available software Open Meta-Analyst for advanced meta-analysis. In this proposal, we will: (1) develop multivariate network meta-analysis frameworks and software to simultaneously compare multiple treatments with multivariate outcomes, and multiple diagnostic tests (which intrinsically involve multivariate outcomes due to the joint interests in prevalence, sensitivity ad specificity); and (2) develop a multivariate visualization tool and nonparametric/parametric methods to detect and adjust for publication bias. We will evaluate the strengths and weaknesses of these proposed methods versus existing meta-analysis methods through many real data analyses and carefully designed simulation studies. The proposed statistical methodology will be broadly applicable to multivariate meta-analysis. Completion of these two aims will directly benefit the CER program by providing state-of-the art methods implemented in user-friendly software using the JAGS and R statistical languages that will be made freely available to the public. It will improve public health by facilitating the diagnosis and treatment of various cancers, cardiovascular, infectious and other diseases.

描述（由适用提供）：比较有效性研究（CER）的目的是为医疗保健决定提供有关不同诊断和治疗方案的好处和风险的信息。给定情况的评估工具和治疗方案的数量越来越多，以及成本的迅速升级，这使人们对通过多元荟萃分析进行了多种诊断测试的科学严格比较和临床实践中多种治疗的科学比较。虽然多元荟萃分析方法肯定非常有用，因为它们可以提供更好的统计特性估算值，但与传统的单变量荟萃分析相比，还有许多其他挑战。为了响应PA-13-303，该提案的总体目标是开发尖端和健壮的多元荟萃分析方法，以增强一致性，适用性和可推广性，以及完全开放的，跨平台，公开可用且易于使用的R软件包。此外，我们将将待机的R软件包集成到用于高级荟萃分析的公开软件开放式荟萃分析中。在此提案中，我们将：（1）开发多变量网络荟萃分析框架和软件，以同时比较多种治疗方法与多元诊断测试以及多个诊断测试（由于敏感性AD特异性的患病率，这些诊断涉及多变量的结果，它们本质上涉及多变量的结果）；（2）开发一种多元可视化工具和非参数/参数方法来检测和调整出版偏差。我们将通过许多真实的数据分析以及精心设计的仿真研究来评估这些提出方法与现有荟萃分析方法的优势和劣势。所提出的统计方法将广泛适用于多元荟萃分析。完成这两个目标的完成将直接通过使用JAGS和R统计语言在用户友好的软件中实施的最先进的方法直接使CER计划受益，这些方法将免费提供给公众。它将通过支持各种癌症，心血管，传染和其他疾病的诊断和治疗来改善公共卫生。