Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data [0.03%]
整合暴露组、基因组和表型数据的因果中介分析
Haoyu Yang,Zhonghua Liu,Ruoyu Wang et al.
Haoyu Yang et al.
Causal mediation analysis provides an attractive framework for integrating diverse types of exposure, genomic, and phenotype data. Recently, this field has seen a surge of interest, largely driven by the increasing need for causal mediation...
Mengyun Wu,Yingmeng Li,Shuangge Ma
Mengyun Wu
Beyond the main genetic and environmental effects, gene-environment (G-E) interactions have been demonstrated to significantly contribute to the development and progression of complex diseases. Published analyses of G-E interactions have pr...
Hyunseung Kang,Zijian Guo,Zhonghua Liu et al.
Hyunseung Kang et al.
Instrumental variables (IVs) are widely used to study the causal effect of an exposure on an outcome in the presence of unmeasured confounding. IVs require an instrument, a variable that ( a ) is associated with the exposure, ( b ) has no d...
Designs for Vaccine Studies [0.03%]
疫苗研究的设计
M Elizabeth Halloran
M Elizabeth Halloran
Due to dependent happenings, vaccines can have different effects in populations. In addition to direct protective effects in the vaccinated, vaccination in a population can have indirect effects in the unvaccinated individuals. Vaccination ...
Sean L Simpson,Heather M Shappell,Mohsen Bahrami
Sean L Simpson
The recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and a...
Post-Processing of MCMC [0.03%]
关于MCMC的后处理
Leah F South,Marina Riabiz,Onur Teymur et al.
Leah F South et al.
Markov chain Monte Carlo is the engine of modern Bayesian statistics, being used to approximate the posterior and derived quantities of interest. Despite this, the issue of how the output from a Markov chain is post-processed and reported i...
Making Sense of Censored Covariates: Statistical Methods for Studies of Huntington's Disease [0.03%]
截断变量的统计处理方法及其在亨廷顿舞蹈病研究中的应用
Sarah C Lotspeich,Marissa C Ashner,Jesus E Vazquez et al.
Sarah C Lotspeich et al.
The landscape of survival analysis is constantly being revolutionized to answer biomedical challenges, most recently the statistical challenge of censored covariates rather than outcomes. There are many promising strategies to tackle censor...
Analysis of Microbiome Data [0.03%]
微生物组数据分析
Christine B Peterson,Satabdi Saha,Kim-Anh Do
Christine B Peterson
The microbiome represents a hidden world of tiny organisms populating not only our surroundings but also our own bodies. By enabling comprehensive profiling of these invisible creatures, modern genomic sequencing tools have given us an unpr...
Gabriel W Hassler,Andrew Magee,Zhenyu Zhang et al.
Gabriel W Hassler et al.
Researchers studying the evolution of viral pathogens and other organisms increasingly encounter and use large and complex data sets from multiple different sources. Statistical research in Bayesian phylogenetics has risen to this challenge...
Ali Shojaie,Emily B Fox
Ali Shojaie
Introduced more than a half-century ago, Granger causality has become a popular tool for analyzing time series data in many application domains, from economics and finance to genomics and neuroscience. Despite this popularity, the validity ...