RESUMO
We introduce a user-friendly computational framework for implementing robust versions of a wide variety of structured regression methods with the L2 criterion. In addition to introducing an algorithm for performing L2E regression, our framework enables robust regression with the L2 criterion for additional structural constraints, works without requiring complex tuning procedures on the precision parameter, can be used to identify heterogeneous subpopulations, and can incorporate readily available non-robust structured regression solvers. We provide convergence guarantees for the framework and demonstrate its flexibility with some examples. Supplementary materials for this article are available online.
RESUMO
The explosion of biobank data offers unprecedented opportunities for gene-environment interaction (GxE) studies of complex diseases because of the large sample sizes and the rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in G×E assessment, especially for set-based G×E variance component (VC) tests, which are a widely used strategy to boost overall G×E signals and to evaluate the joint G×E effect of multiple variants from a biologically meaningful unit (e.g., gene). In this work, we focus on continuous traits and present SEAGLE, a Scalable Exact AlGorithm for Large-scale set-based G×E tests, to permit G×E VC tests for biobank-scale data. SEAGLE employs modern matrix computations to calculate the test statistic and p-value of the GxE VC test in a computationally efficient fashion, without imposing additional assumptions or relying on approximations. SEAGLE can easily accommodate sample sizes in the order of 105, is implementable on standard laptops, and does not require specialized computing equipment. We demonstrate the performance of SEAGLE using extensive simulations. We illustrate its utility by conducting genome-wide gene-based G×E analysis on the Taiwan Biobank data to explore the interaction of gene and physical activity status on body mass index.
RESUMO
Many studies show that immigrants face significant barriers in accessing health care. These barriers may be particularly pronounced for newer immigrants, who may face additional obstacles in navigating the health care system. Understanding the sources of health care disparities between recent and non-recent immigrants may allow for better design of policies and interventions to address the vulnerabilities unique to different subgroups of immigrants defined by their length of residency. This study employs descriptive analyses and multivariate logistic regression to estimate the likelihood of accessing and utilizing health care services based on immigration-related factors after controlling for predisposing, enabling, and health care need factors. We also employ a regression-based decomposition method to determine whether health care differences between recent and non-recent immigrants are statistically significant and to identify the primary drivers of healthcare differences between recent and non-recent immigrants. The findings support the hypothesis that significant disparities in health care access and utilization exist between recent and non-recent immigrants. We found that health care access and utilization differences between recent and non-recent immigrants were driven primarily by enabling resources, including limited English proficiency (LEP), insurance status, public assistance usage, and poverty level. These results indicate that not only are newer immigrants more likely to underutilize health care, but also that their underutilization is driven primarily by their lack of insurance, lack of adequate financial resources, and inability to navigate the health care system due to LEP. The results further indicate that immigrants with prolonged LEP may be less likely to have a usual source of care and more likely to report delays in obtaining medical treatments, than even recent immigrants with LEP.
