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

解释系统评价中结果报告偏差的总体框架

• 批准号: 9765388
• 负责人: Yong Chen
• 金额: $ 33.66万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-08 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9765388
• 关键词: Accounting; Area; Benefits and Risks; Clinical Trials; Communities; Computer software; Data; Data Set; Databases; Decision Making; Development; Effectiveness of Interventions; Ensure; Epidemiologist; Event; Evidence Based Medicine; Evidence based practice; Glaucoma; Goals; Guidelines; Healthcare; Investigation; Joints; Lead; Literature; Medical; Meta-Analysis; Methods; Modeling; Nature; Outcome; Performance; Policies; Procedures; Property; Protocols documentation; Public Domains; Publishing; Randomized Controlled Trials; Recommendation; Reporting; Research; Research Personnel; Safety; Sampling; Solid; Statistical Methods; Testing; Time; Work; base; comparative effectiveness; design; effectiveness research; handbook; hospital readmission; novel; programs; response; simulation; standard care; systematic review; user friendly software; user-friendly

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.

项目摘要 比较有效性研究（CER）从根本上依赖于准确和及时的评估， 不同治疗方案的风险和益处。经验证据表明，35%的有效性中位数 每个平行组试验中50%的安全性结局报告不完整，且具有统计学显著性 结果有更高的可能性被充分报告相比，非重大成果，无论是 有效性和安全性。这种偏倚被称为结果报告偏倚（ORB），即，“选择性报道 某些结果，而不是其他结果，这取决于结果的性质和方向（即，缺少某些 成果）”。选择性报告可能会使荟萃分析的结果无效。正如科克伦中所承认的 手册“检测研究内选择性报告的统计方法（即，结果报告偏倚），如 然而，还没有得到很好的发展”（第8.14.2章，第5.0.2版），迫切需要制定具体的方法， 关于ORB 作为对PA-16-160的回应，本提案的总体目标是开发、测试和评估新的统计 方法和用户友好的软件来解释多变量和网络荟萃分析中的ORB。在这 我们的工作重点是：（1）提出并评估新的ORB证据量化方法， 对ORB进行调整，并建立一个在ORB下进行多元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荟萃分析（其中超过2种治疗方法 同时进行比较），并提出评估证据一致性的方法。（3）发展 公开可用的，用户友好的和有据可查的软件，并将所提出的方法应用于研究 数据集。我们将使用精心设计的模拟研究，以调查拟议的性能 方法，将所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，应用于（网络）临床荟萃分析 多变量结局的试验。完成本提案中的这三个目标将直接使CER受益 程序通过提供最先进的方法，在用户友好的R包中实现，将免费提供 提供给公众。这有可能催化许多新方法的发展，放大 我们项目的影响。