Statistical Methods for Modern Evidence Syntheses with Multiple Biases

项目摘要/摘要 Meta分析对临床建议和政策的形成至关重要，但它们的可信度可能 受到研究内偏差(例如，观察性研究中的混淆)和跨研究偏差的破坏 (例如，按出版偏向进行过滤)。这些偏差也可能产生过大的元分析估计 很小，或者方向错了。科学家、临床医生和卫生政策制定者越来越担心 鉴于最近的经验证据表明，对同一主题的元分析可能与 彼此之间以及系统复制研究的结果，旨在最大限度地减少发布 通过让独立调查人员重复已发表的研究来实现偏见。这侵蚀了人们对出版商的信心 文学和元分析代表了一个认识的转折点。 该建议开发并经验性地验证了创新的、独立于领域的统计数据 定量综合研究的框架，这些研究受研究内部和研究间的偏差影响。目标1将因此 开发新的、独立于领域的统计敏感度分析将如何量化元分析的结果 估计可能会因研究内部和研究之间的偏差而发生变化，从而允许对研究进行偏见校正的综合 这两种形式的偏差，并预测了新研究结果的可能范围和 将它们添加到现有的荟萃分析中。这些方法将通过用户友好的方式广泛使用 网站和R软件，它们的使用将在关于儿童肥胖的荟萃分析中得到说明。目标2将 说明这些方法的实际影响，并将它们的性能与现有方法进行比较 Cochrane数据库Meta分析可信度的表征方法。目标3将涉及一项 与ManyBabies合作，这是一项创新计划，旨在对里程碑式的结果进行概念性复制 在发展心理学中。这些计划复制的结果将使用新的 目标1的方法以及现有方法；预测将在研究结果进行比较后进行 进行了测试，提供了真正的性能基准。AIMS 2-3还将提供在线“仪表盘”， 对结果的直观探索。 近期目标是开发与现有统计方法不同的方法和软件， 评估给定的荟萃分析对研究内部和跨研究偏差的联合影响的稳健性； 合成荟萃分析的结果并将其与具有较少发表偏倚的研究的结果进行比较(例如， 复制研究)；以及使用可能有偏见的荟萃分析来规划新研究的最佳设计。 长期目标是校准元分析中的信心，以便更快地为科学提供可靠的信息 这些结论将改善实践和卫生政策。

PROJECT SUMMARY/ABSTRACT Meta-analyses critically shape clinical recommendations and policy, but their credibility may be undermined by both within-study biases (e.g., confounding in observational studies) and across-study biases (e.g., filtering by publication bias). These biases can produce meta-analysis estimates that are too large, too small, or in the wrong direction. Scientists, clinicians, and health policymakers are increasingly concerned about these biases given recent empirical evidence that meta-analyses on the same topic can disagree with one another and with the results of systematic replication studies, which are designed to minimize publication bias by having independent investigators repeat published studies. This eroding confidence in the published literature and in meta-analyses represents an epistemic turning point. This proposal develops and empirically validates an innovative, domain-independent statistical framework for quantitatively synthesizing studies subject to within- and across-study biases. Aim 1 will thus develop novel, domain-independent statistical sensitivity analyses will quantify how results of meta-analysis estimates might be shifted by within- and across-study biases, that allow bias-corrected synthesis of studies with these two forms of bias, and that forecast the likely range of results of new studies and the impact of adding them to an existing meta-analysis. The methods will be made broadly accessible via user-friendly websites and R software, and their use will be illustrated in meta-analyses on pediatric obesity. Aim 2 will illustrate the methods' real-world impact and compare their performance to that of existing methods by using the methods to characterize the credibility of Cochrane database meta-analyses. Aim 3 will involve a collaboration with “ManyBabies'', an innovative initiative to conduct conceptual replications of landmark results in developmental psychology. The results of these planned replications will be forecasted using the new methods of Aim 1 as well as existing methods; the forecasts will be compared studies' results after they are conducted, providing real performance benchmarks. Aims 2-3 will also provide online “dashboards” allowing intuitive exploration of the results. The immediate-term goal is to develop methods and software that, unlike existing statistical methods, assess the robustness of a given meta-analysis to the joint effects of within- and across-study biases; that synthesize and compare results of meta-analyses with those of studies subject to less publication bias (e.g., replication studies); and that use potentially biased meta-analyses to plan the optimal design of new studies. The long-term goal is to calibrate confidence in meta-analyses to more swiftly inform scientifically robust conclusions that will improve practice and health policy.