AVERAGED PREDICTION MODELS (APM): IDENTIFYING CAUSAL EFFECTS IN CONTROLLED PRE-POST SETTINGS WITH APPLICATION TO GUN POLICY [0.03%]
平均预测模型(APM)：识别控制前后的因果效应及其在枪支政策中的应用
Thomas Leavitt,Laura A Hatfield
Thomas Leavitt
To investigate causal impacts, many researchers use controlled pre-post designs that compare over-time differences between a population exposed to a policy change and an unexposed comparison group. However, researchers using these designs o...
ACCURATE ESTIMATION OF RARE CELL-TYPE FRACTIONS FROM TISSUE OMICS DATA VIA HIERARCHICAL DECONVOLUTION [0.03%]
通过分层脱卷积累稀有细胞类型组学数据的准确估算细胞比例
Penghui Huang,Manqi Cai,Xinghua Lu et al.
Penghui Huang et al.
Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential...
NETWORK-BASED MODELING OF EMOTIONAL EXPRESSIONS FOR MULTIPLE CANCERS VIA A LINGUISTIC ANALYSIS OF AN ONLINE HEALTH COMMUNITY [0.03%]
基于网络的情感表达建模：通过在线健康社区的语言分析针对多种癌症进行研究
Xinyan Fan,Mengque Liu,Shuangge Ma
Xinyan Fan
The diagnosis and treatment of cancer can evoke a variety of adverse emotions. Online health communities (OHCs) provide a safe platform for cancer patients and those closely related to express emotions without fear of judgement or stigma. I...
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...