Meta-analysis is routinely performed in many scientific disciplines. This analysis is attractive since discoveries are possible even when all the individual studies are underpowered. However, the meta-analytic discoveries may be entirely driven by signal in a single study, and thus non-replicable. Although the great majority of meta-analyses carried out to date do not infer on the replicability of their findings, it is possible to do so. We provide a selective overview of analyses that can be carried out towards establishing replicability of the scientific findings. We describe methods for the setting where a single outcome is examined in multiple studies (as is common in systematic reviews of medical interventions), as well as for the setting where multiple studies each examine multiple features (as in genomics applications). We also discuss some of the current shortcomings and future directions.
Multiple Testing of Partial Conjunction Hypotheses for Assessing Replicability Across Dependent Studies
University of Bremen（不来梅大学）；University of Kansas（堪萨斯大学）；Indian Institute of Management, Calcutta（印度管理学院加尔各答分校）；Indian Institute of Technology, Hyderabad（印度理工学院海德拉巴分校）
Detecting Multiple Replicating Signals using Adaptive Filtering Procedures
可复制性是科学发现的一个基本特征：我们感兴趣的是那些在不同实验室、研究人群、不同时间等中可检测到的信号。与元分析不同，元分析解释了实验变异性，但不保证可复制性，测试部分连接（PC）null的目的是专门识别在多项研究中发现的信号。在许多当代应用中，例如，为了比较多个高通量遗传实验，需要同时测试大量的$M$ PC空值，这需要多重比较校正。但是，对$M$ PC $p$-值进行标准多次测试调整可能非常保守，尤其是当$M$较大且信号稀疏时。我们介绍AdaFilter，一个新的多重检验程序，通过自适应地过滤掉不太可能的候选PC空值来增加功率。我们证明了AdaFilter可以控制FWER和FDR，只要研究之间的数据是独立的，并具有更高的权力比其他现有的方法。我们用三个例子说明AdaFilter的应用：杜氏肌营养不良症的微阵列研究，肺癌肿瘤中T细胞的单细胞RNA测序和GWAS的代谢组学。
