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Statistical Methods for Modern Evidence Syntheses with Multiple Biases

具有多重偏差的现代证据综合统计方法

基本信息

批准号：
10672230
负责人：
Maya Mathur
金额：
$ 33.45万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2025-08-31
项目状态：
未结题

项目摘要

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.

项目总结/摘要荟萃分析对临床建议和政策产生了重要影响，但其可信度可能被研究内的偏倚（例如，观察性研究中的混杂）和跨研究偏倚 (e.g.,通过出版物偏差进行过滤）。这些偏差也会产生过大的荟萃分析估计值小，或者在错误的方向。科学家、临床医生和卫生政策制定者越来越关注关于这些偏见，最近的经验证据表明，对同一主题的荟萃分析可能不同意并与旨在尽量减少出版物的系统性复制研究的结果相结合通过让独立研究者重复已发表的研究而产生的偏倚。这削弱了人们对出版物的信心，文献和荟萃分析代表了认识上的转折点。该建议开发并实证验证了一种创新的，独立于领域的统计方法，定量综合研究的框架内和跨研究的偏见。目标1将开发新的、独立于领域的统计敏感性分析，将量化荟萃分析的结果估计值可能会因研究内和研究间偏倚而发生变化，从而允许对研究进行偏倚校正综合这两种形式的偏见，并预测可能的范围内的新的研究结果和影响，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，他们的使用将在儿童肥胖的荟萃分析中说明。目标2将说明方法的现实世界的影响，并比较其性能与现有的方法，通过使用描述科克伦数据库荟萃分析可信度的方法。目标3将涉及与“ManyBabies”合作，这是一项创新举措，旨在从概念上复制具有里程碑意义的成果在发展心理学中。这些计划复制的结果将使用新的目标1的方法以及现有的方法;预测将比较研究的结果后，他们是提供了真实的业绩基准。目标2-3还将提供在线“仪表板”，对结果的直观探索。近期目标是开发方法和软件，与现有的统计方法不同，评估给定荟萃分析对研究内和研究间偏倚联合效应的稳健性; 综合并比较荟萃分析的结果与发表偏倚较小的研究的结果（例如，重复研究）;以及使用潜在偏倚的荟萃分析来规划新研究的最佳设计。长期目标是校准荟萃分析的信心，以更快地提供科学可靠的信息。这些结论将改善实践和卫生政策。