Modernizing Meta-Analysis to Facilitate Comparative Effectiveness Reviews

现代化荟萃分析以促进比较有效性审查

• 批准号: 7942744
• 负责人: CHRISTOPHER H. SCHMID
• 金额: $ 41.77万
• 依托单位: TUFTS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7942744
• 关键词: Modernizing; Meta; Analysis; Facilitate; Comparative

DESCRIPTION (provided by applicant): The growing number of treatment choices, as well as the rapid escalation in the cost of health care has spawned the need for scientifically rigorous comparisons of the efficacy and safety of drugs, devices and treatments in clinical practice. Most quantitative comparisons carried out by the Evidence Based Practice Centers (EPCs) funded by AHRQ have relied on meta-analyses comparing two treatments for one outcome. But many of the questions posed to assessors involve multiple treatments and multiple outcomes. Standard assessments have approached these questions separately, leading to a plethora of analyses to interpret and no quantitatively rigorous methods for integrating them. Multivariate statistical methods offer a solution. Statistical methods for simultaneously analyzing multiple treatments or for analyzing multiple outcomes have appeared in the past decade, along with applications to important problems. Because they are new, these methods have not penetrated into the standard clinical research arsenal and remain the provenance of experts. To make them more practical, particularly for groups like EPCs, we propose further development of the methods, empirical assessment of their strengths and weaknesses, integration into publicly available, easy to-use software and application to key clinical questions. In particular, we propose a three-year project to carry out the following three Specific Aims focusing on intervention studies: 1) Develop new and enhance existing techniques for the statistical analysis of possibly incomplete multivariate meta-analytic data involving multiple (> 2) treatments, multiple outcomes or categories of outcomes and multiple follow-up times; 2) Implement these methods in an ongoing open-source software project so users will have access to customized and customizable algorithms and support for their use through analytic wizards that will guide users to appropriate statistical methods and presentation quality graphics and reports; and 3) Apply multivariate methods to a set of key clinical questions that we have identified as well as to topics in the EPC program as they arise over the course of this application so as to gain empirical insight into their strengths and weaknesses and to demonstrate their contribution to improving comparative effectiveness reviews. Completion of these three aims will directly benefit the comparative effectiveness research program at AHRQ by providing state-of-the art methods implemented in user-friendly open-source software that will be made freely available to the public. The open-source nature of the software will enable it to incorporate all of the capabilities of the R and OpenBugs statistical programs and will facilitate the incorporation of new methods as they are developed. The user interface will allow the non-statistically expert user access to these features without the need for programming expertise.

描述（由申请人提供）：治疗选择的数量不断增加，以及医疗保健成本的快速上升，催生了对临床实践中药物、器械和治疗的有效性和安全性进行科学严格比较的需求。由AHRQ资助的循证实践中心（EPCs）进行的大多数定量比较都依赖于比较两种治疗方法的荟萃分析。但向评估人员提出的许多问题涉及多种治疗和多种结果。标准评估分别处理这些问题，导致过多的分析来解释，没有定量的严格方法来整合它们。多元统计方法提供了一个解决方案。在过去的十年中，同时分析多种治疗或分析多种结果的统计方法已经出现，沿着重要问题的应用。因为它们是新的，这些方法还没有渗透到标准的临床研究武器库中，仍然是专家的来源。为了使它们更实用，特别是对于EPC这样的团体，我们建议进一步发展 方法，经验评估其优点和缺点，整合到公开可用，易于使用的软件和应用程序的关键临床问题。特别是，我们建议一个为期三年的项目，以开展以下三个具体目标，重点是干预研究： 1)开发新的和增强现有的技术，用于对可能不完整的多变量荟萃分析数据进行统计分析，这些数据涉及多种（> 2）治疗、多种结局或结局类别以及多种随访时间; 2)在一个正在进行的开放源码软件项目中实施这些方法，使用户能够获得定制和可定制的算法，并通过分析向导支持其使用，这些向导将指导用户使用适当的统计方法和高质量的图形和报告; 3)将多变量方法应用于我们已经确定的一组关键临床问题以及EPC计划中的主题，因为它们在此应用过程中出现，以便获得对其优点和缺点的经验性洞察，并证明其对改进比较有效性审查的贡献。 这三个目标的完成将直接有利于比较有效性研究计划， AHRQ通过提供最先进的方法，在用户友好的开源软件中实施，将免费提供给公众。该软件的开放源码性质将使其能够纳入R和OpenBugs统计程序的所有功能，并将促进纳入新方法的开发。用户界面将允许非统计专家用户访问这些功能，而无需编程专业知识。