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ABSTRACT We introduce a novel meta-analysis framework to combine dependent tests under a general setting, and utilize it to synthesize various microbiome association tests that are calculated from the same dataset. Our development builds upon the classical meta-analysis methods of aggregating P-values and also a more recent general method of combining confidence distributions, but makes generalizations to handle dependent tests. The proposed framework ensures rigorous statistical guarantees, and we provide a comprehensive study and compare it with various existing dependent combination methods. Notably, we demonstrate that the widely used Cauchy combination method for dependent tests, referred to as the vanilla Cauchy combination in this article, can be viewed as a special case within our framework. Moreover, the proposed framework provides a way to address the problem when the distributional assumptions underlying the vanilla Cauchy combination are violated. Our numerical results demonstrate that ignoring the dependence among the to-be-combined components may lead to a severe size distortion phenomenon. Compared to the existing P-value combination methods, including the vanilla Cauchy combination method and other methods, the proposed combination framework is flexible and can be adapted to handle the dependence accurately and utilizes the information efficiently to construct tests with accurate size and enhanced power. The development is applied to the microbiome association studies, where we aggregate information from multiple existing tests using the same dataset. The combined tests harness the strengths of each individual test across a wide range of alternative spaces, enabling more efficient and meaningful discoveries of vital microbiome associations. 

Until recently, national-level data on criminal victimization in the United States did not include information on immigrant or citizenship status of respondents. This data-infrastructure limitation has hindered scientific understanding of whether immigrants are more or less likely than native-born Americans to be criminally victimized and how victimization may vary among immigrants of different statuses. We address these issues in the present study by using new data from the 2017–2018 National Crime Victimization Survey (NCVS) to explore the association between citizenship status and victimization risk in a nationally representative sample of households and persons aged 12 years and older. The research is guided by a theoretical framing that integrates insights from studies of citizenship with the literature on immigration and crime, as well as with theories of victimization. We find that a person’s foreign-born status (but not their acquired U.S. citizenship) confers protection against victimization. We also find that the protective benefit associated with being foreign born does not extend to those with ambiguous citizenship status, who in our data exhibit attributes similar to the known characteristics of undocumented immigrants. We conclude by discussing the implications of our findings and the potential ways to extend the research.