Modernizing Meta-Analysis to Facilitate Comparative Effectiveness Reviews

描述（由申请人提供）：越来越多的治疗选择，以及医疗保健成本的快速上升，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需求。大多数由AHRQ资助的循证实践中心（EPCs）进行的定量比较依赖于荟萃分析，比较两种治疗方法的一个结果。但评估人员面临的许多问题涉及多种治疗方法和多种结果。标准评估分别处理这些问题，导致需要解释的分析过多，而没有严格的定量方法来整合它们。多元统计方法提供了一个解决方案。在过去十年中出现了同时分析多种治疗或分析多种结果的统计方法，并将其应用于重要问题。由于是新方法，这些方法还没有进入标准的临床研究库，仍然是专家的来源。为了使它们更实用，特别是对于EPCs这样的群体，我们建议进一步开发

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.