Modernizing Meta-Analysis to Facilitate Comparative Effectiveness Reviews

描述(申请人提供)：越来越多的治疗选择，以及医疗保健成本的迅速上升，催生了在临床实践中对药物、设备和治疗的有效性和安全性进行科学严格比较的必要性。由AHRQ资助的循证实践中心(EPC)进行的大多数定量比较都依赖于比较两种治疗方法对一种结果的荟萃分析。但向评估者提出的许多问题涉及多种治疗和多种结果。标准评估分别处理了这些问题，导致需要解释的分析过多，没有严格的量化方法来整合这些分析。多元统计方法提供了一个解决方案。在过去的十年中，出现了同时分析多种治疗方法或分析多种结果的统计方法，以及对重要问题的应用。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，仍然是专家的出处。为了使它们更实用，特别是对于像EPC这样的群体，我们建议进一步开发 这些方法，经验评估的优势和劣势，集成到公开可用的，易于使用的软件和关键临床问题的应用。特别是，我们提出了一个为期三年的项目，以实现以下三个具体目标，重点是干预研究： 1)开发新的和增强的现有技术，用于统计分析可能不完整的多变量元分析数据，涉及多个(&gt；2)治疗、多个结果或多个结果类别和多个随访时间； 2)在正在进行的开源软件项目中实施这些方法，以便用户能够通过分析向导获得定制的和可定制的算法并支持其使用，这些向导将指导用户使用适当的统计方法和呈现质量的图形和报告；以及 3)将多元方法应用于我们已确定的一组关键临床问题，以及EPC计划中在应用过程中出现的主题，以获得对其优势和劣势的经验性洞察，并展示它们对改进比较有效性审查的贡献。 完成这三个目标将直接使比较有效性研究方案受益于 AHRQ通过提供以用户友好的开放源码软件实现的最先进的方法，将免费向公众提供。该软件的开源性质将使其能够纳入R和OpenBugs统计程序的所有能力，并将促进在开发新方法时纳入这些方法。用户界面将允许非统计专家用户访问这些功能，而不需要编程专业知识。

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.