A General Framework to Account for Outcome Reporting Bias in Systematic Reviews

项目摘要 比较有效性研究(CER)从根本上依赖于对 不同治疗方案的益处和风险。经验证据表明，有效率的中位数为35% 每组平行试验中有50%的安全性结果没有完全报道，且有统计学意义 与不重要的结果相比，结果被完全报告的可能性更高，两者都是 有效性和安全性。这种偏向称为结果报告偏向(ORB)，即“选择性报告 有些结果，但不是其他结果，这取决于结果的性质和方向(即，错过某些 结果)。“选择性报告可能会使荟萃分析的结果无效。正如《Cochrane》所承认的那样 手册“检测研究内选择性报告(即结果报告偏差)的统计方法为 然而，不是很好地开发“(第8.14.2章，版本5.0.2)，迫切需要开发专门的方法 为ORB记账。 根据PA-16-160，本提案的总体目标是开发、测试和评估新的统计数据 方法和用户友好的软件来解释多变量和网络Meta分析中的ORB。在这 建议，我们将集中在：(1)提出和评估新的ORB证据量化方法，以 对ORB进行调整，并开发了ORB下多元Meta分析的敏感性分析程序。 (2)将目标1中的方法推广到网络Meta分析(其中超过2个处理是 同时进行比较)，并提出了评价证据一致性的方法。(3)发展 公开可用的、用户友好的和有良好文档记录的软件，并将建议的方法应用于研究 数据集。我们将使用精心设计的模拟研究来调查拟议的 方法，将提出的方法应用于多个现有的数据库，并开发更广泛的统计软件 研究社区。 我们建议通过以下方式对这些方法的优点和缺点进行实证评估 精心设计的模拟研究，更重要的是，在临床(网络)荟萃分析中的应用 具有多变量结果的试验。完成本提案中的这三个目标将直接惠及CER 通过提供在用户友好的R包中实现的最先进的方法来编程，该R包将免费制作 对公众开放。这有可能催化许多新方法的发展，放大 我们项目的影响。

Project Summary Comparative effectiveness research (CER) relies fundamentally on accurate and timely assessment of the benefits and risks of different treatment options. Empirical evidence suggests that a median of 35% of efficacy and 50% of safety outcomes per parallel group trials were incompletely reported, and statistically significant outcomes had a higher likelihood of being fully reported compared to non-significant outcomes, both for efficacy and safety. Such a bias is referred to as outcome reporting bias (ORB), i.e., “the selective reporting of some outcomes but not others, depending on the nature and direction of the results (i.e., missing certain outcomes).” Selective reporting can invalidate results from meta-analyses. As acknowledged in the Cochrane handbook “Statistical methods to detect within-study selective reporting (i.e., outcome-reporting bias) are, as yet, not well developed” (chapter 8.14.2, version 5.0.2), there is a critical need to develop methods specifically accounting for ORB. In response to PA-16-160, the overall goal of this proposal is to develop, test and evaluate new statistical methods and user-friendly software to account for ORB in multivariate and network meta-analyses. In this proposal, we will focus on: (1) To propose and evaluate new methods for quantifying the evidence of ORB, to adjusting for ORB, and to develop a procedure of sensitivity analysis under ORB in multivariate meta-analysis. (2) To generalize the methods in Aim 1 to network meta-analyses (where more than 2 treatments are compared simultaneously), and to propose methods to evaluate the evidence consistency. And (3) To develop publicly available, user-friendly and well-documented software and apply the proposed methods to research data sets. We will use carefully designed simulation studies to investigate the performance of the proposed methods, apply the proposed methods to multiple existing databases, and develop statistical software for wider research communities. We propose to perform empirical assessment of the strengths and weaknesses of these methods through carefully designed simulation studies and, more importantly, applications to (network) meta-analyses of clinical trials with multivariate outcomes. Completion of these three aims in this proposal will directly benefit the CER program by providing state-of-the art methods implemented in user-friendly R package that will be made freely available to the public. This has the potential to catalyze the development of many new methods, amplifying the impact of our project.