CAUSAL INFERENCE FROM OBSERVATIONAL STUDIES WITH CLUSTERED INTERFERENCE, WITH APPLICATION TO A CHOLERA VACCINE STUDY [0.03%]
观察研究中的因果推断：存在聚类干扰情况及霍乱疫苗研究的应用
Brian G Barkley,Michael G Hudgens,John D Clemens et al.
Brian G Barkley et al.
Understanding the population-level effects of vaccines has important public health policy implications. Inferring vaccine effects from an observational study is challenging because participants are not randomized to vaccine (i.e., treatment...
Jane W Liang,Śaunak Sen
Jane W Liang
Recent technological advancements have led to the rapid generation of high-throughput biological data, which can be used to address novel scientific questions in broad areas of research. These data can be thought of as a large matrix with c...
Peng Yu,Yumin Lian,Elliot Xie et al.
Peng Yu et al.
Surrogate selection is an experimental design that without sequencing any DNA can restrict a sample of cells to those carrying certain genomic mutations. In immunological disease studies, this design may provide a relatively easy approach t...
QUANTILE REGRESSION DECOMPOSITION ANALYSIS OF DISPARITY RESEARCH USING COMPLEX SURVEY DATA: APPLICATION TO DISPARITIES IN BMI AND TELOMERE LENGTH BETWEEN U.S. MINORITY AND WHITE POPULATION GROUPS [0.03%]
基于复杂抽样调查数据的分位数回归分解法在差异研究中的应用：美国少数族裔与白种人体质指数和端粒长度差异分析为例
Hyokyoung G Hong,Barry I Graubard,Joseph L Gastwirth et al.
Hyokyoung G Hong et al.
We develop a quantile regression decomposition (QRD) method for analyzing observed disparities (OD) between population groups in socioeconomic and health-related outcomes for complex survey data. The conventional decomposition approaches us...
CONTRASTIVE LATENT VARIABLE MODELING WITH APPLICATION TO CASE-CONTROL SEQUENCING EXPERIMENTS [0.03%]
基于对照组的列联表数据的条件似然方法及其应用
Andrew Jones,F William Townes,Didong Li et al.
Andrew Jones et al.
High-throughput RNA-sequencing (RNA-seq) technologies are powerful tools for understanding cellular state. Often, it is of interest to quantify and to summarize changes in cell state that occur between experimental or biological conditions....
A LATENT VARIABLE MIXTURE MODEL FOR COMPOSITION-ON-COMPOSITION REGRESSION WITH APPLICATION TO CHEMICAL RECYCLING [0.03%]
一种用于化学回收的成分与成分回归的潜在变量混合模型
Nicholas Rios,Lingzhou Xue,Xiang Zhan
Nicholas Rios
It is quite common to encounter compositional data in a regression framework in data analysis. When both responses and predictors are compositional, most existing models rely on a family of log-ratio based transformations to move the analys...
Bayesian multivariate sparse functional principal components analysis with application to longitudinal microbiome multiomics data [0.03%]
具有应用价值的纵向微生物组多组学数据分析的贝叶斯多元稀疏函数主成分分析
Lingjing Jiang,Chris Elrod,Jane J Kim et al.
Lingjing Jiang et al.
Microbiome researchers often need to model the temporal dynamics of multiple complex, nonlinear outcome trajectories simultaneously. This motivates our development of multivariate Sparse Functional Principal Components Analysis (mSFPCA), ex...
BAYESIAN LEARNING OF CLINICALLY MEANINGFUL SEPSIS PHENOTYPES IN NORTHERN TANZANIA [0.03%]
坦桑尼亚北部具有临床意义的脓毒症表型的贝叶斯学习方法
Alexander Dombowsky,David B Dunson,Deng B Madut et al.
Alexander Dombowsky et al.
Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Recently, researchers have hypothesized that sepsis consists of a heterogeneous spectrum of distinct subtypes, motivating several studies to identif...
INFERRING SYNERGISTIC AND ANTAGONISTIC INTERACTIONS IN MIXTURES OF EXPOSURES [0.03%]
推测暴露混合物的协同和拮抗效应
Shounak Chattopadhyay,Stephanie M Engel,David Dunson
Shounak Chattopadhyay
There is abundant interest in assessing the joint effects of multiple exposures on human health. This is often referred to as the mixtures problem in environmental epidemiology and toxicology. Classically, studies have examined the adverse ...
BAYESIAN DATA AUGMENTATION FOR RECURRENT EVENTS UNDER INTERMITTENT ASSESSMENT IN OVERLAPPING INTERVALS WITH APPLICATIONS TO EMR DATA [0.03%]
基于间歇性评估的时间段内复发事件的贝叶斯数据增广及其在电子病历数据中的应用研究
Xin Liu,Patrick M Schnell
Xin Liu
Electronic medical records (EMR) data contain rich information that can facilitate health-related studies but is collected primarily for purposes other than research. For recurrent events, EMR data often do not record event times or counts ...