3期

最新文章

• EXPOSURE EFFECTS ON COUNT OUTCOMES WITH OBSERVATIONAL DATA, WITH APPLICATION TO INCARCERATED WOMEN AI翻译
  作者：Shook-Sa, Bonnie E.; Hudgens, Michael G.; Knittel, Andrea K.; Edmonds, Andrew; Ramirez, Catalina; Cole, Stephen R.; Cohen, Mardge; Adedimeji, Adebola; Taylor, Tonya; Michel, Katherine G.; Kovacs, Andrea; Cohen, Jennifer; Donohue, Jessica; Foster, Antonina; Fischl, Margaret A.; Long, Dustin; Adimora, Adaora A.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; John H Stroger Junior Hospital Cook County; Montefiore Medical Center; Albert Einstein College of Medicine; Yeshiva University; State University of New York (SUNY) System; SUNY Downstate Health Sciences University; Georgetown University; University of Southern California; University of California System; University of California San Francisco; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Emory University; University of Miami; University of Alabama System; University of Alabama Birmingham
  摘要：Causal inference methods can be applied to estimate the effect of a point exposure or treatment on an outcome of interest using data from observational studies. For example, in the Women's Interagency HIV Study, it is of interest to understand the effects of incarceration on the number of sexual partners and the number of cigarettes smoked after incarceration. In settings like this where the outcome is a count, the estimand is often the causal mean ratio, that is, the ratio of the counterfactu...
• A FORENSIC STATISTICAL ANALYSIS OF FRAUD IN THE FEDERAL FOOD STAMP PROGRAM AI翻译
  作者：Woody, Jonathan; Zhao, Zhicong; Lund, Robert; Wu, Tung-Lung
  作者单位：Mississippi State University; University of California System; University of California Santa Cruz
  摘要：This study develops methods to detect anomalous transactions linked with fraud in food stamp purchases through order statistics methods. The methods detect clusters in the order statistics of the transaction amounts that merit further scrutiny. Our techniques use scan statistics to determine when an excessive number of transactions occur (cluster), which is historically linked to fraud. A scoring paradigm is constructed that ranks the degree in which detected clusters and individual transactio...
• NONCONVEX SVM FOR CANCER DIAGNOSIS BASED ON MORPHOLOGIC FEATURES OF TUMOR MICROENVIRONMENT AI翻译
  作者：Kent, Sean; Yu, Menggang
  作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Michigan System; University of Michigan
  摘要：The surroundings of a cancerous tumor impact how it grows and develops in humans. New data from early breast cancer patients contains information on the collagen fibers surrounding the tumorous tissue-offering hope of finding additional biomarkers for diagnosis and prognosis-but poses two challenges for typical analysis. Each image section contains information on hundreds of fibers, and each tissue has multiple image sections contributing to a single prediction of tumor vs. nontumor. This nest...
• ARE MADE AND MISSED DIFFERENT? AN ANALYSIS OF FIELD GOAL ATTEMPTS OF PROFESSIONAL BASKETBALL PLAYERS VIA DEPTH BASED TESTING PROCEDURE AI翻译
  作者：Qi, Kai; Hu, Guanyu; Wu, Wei
  作者单位：Microsoft; University of Texas System; University of Texas Health Science Center Houston; State University System of Florida; Florida State University
  摘要：In this paper we develop a novel depth-based testing procedure on spatial point processes to examine the difference in made and missed field goal attempts for NBA players. Specifically, our testing procedure can statistically detect the differences between made and missed field goal attempts for NBA players. We first obtain the depths of two processes under the polar coordinate system. A two-dimensional Kolmogorov-Smirnov test is then performed to test the difference between the depths of the ...
• SEMIPARAMETRIC MODELING OF SARS-COV-2 TRANSMISSION USING TESTS, CASES, DEATHS, AND SEROPREVALENCE DATA AI翻译
  作者：Bayer, Damon; Goldstein, Isaac H.; Fintzi, Jonathan; Lumbard, Keith; Ricotta, Emily; Warner, Sarah; Strich, Jeffrey R.; Chertow, Daniel S.; Busch, Lindsay M.; Parker, Daniel M.; Boden-Albala, Bernadette; Chhuon, Richard; Zahn, Matthew; Quick, Nichole; Dratch, Alissa; Minin, Volodymyr M.
  作者单位：University of California System; University of California Irvine; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); Frederick National Laboratory for Cancer Research; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); National Institutes of Health (NIH) - USA; NIH Clinical Center (CC); Emory University; University of California System; University of California Irvine; Los Angeles County Department of Public Health; Edwards Lifesciences
  摘要：Mechanistic models fit to streaming surveillance data are critical for understanding the transmission dynamics of an outbreak as it unfolds in realtime. However, transmission model parameter estimation can be imprecise, sometimes even impossible, because surveillance data are noisy and not informative about all aspects of the mechanistic model. To partially overcome this obstacle, Bayesian models have been proposed to integrate multiple surveillance data streams. We devised a modeling framewor...
• BAYESIAN JOINT MODELING OF HIGH-DIMENSIONAL DISCRETE MULTIVARIATE LONGITUDINAL DATA USING GENERALIZED LINEAR MIXED MODELS AI翻译
  作者：Hauser, Paloma; Tan, Xianming; Chen, Fang; Chen, Ronald c.; Ibrahim, Joseph g.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; SAS Institute Inc; University of Kansas
  摘要：In routine cancer care, various patient- and clinician-reported symptoms are collected throughout treatment. This informs a crucial part of clinical research, particularly in studying the factors associated with symptom underascertainment. To jointly analyze such discrete, multivariate, and potentially high-dimensional repeated measures, we propose a Bayesian longitudinal generalized linear mixed model (BLGLMM). This model integrates three key methodologies: a low-rank matrix decomposition to ...
• A BOOTSTRAP MODEL COMPARISON TEST FOR IDENTIFYING GENES WITH CONTEXT-SPECIFIC PATTERNS OF GENETIC REGULATION AI翻译
  作者：Malakhov, Mykhaylo M.; Dai, Ben; Shen, Xiaotong T.; Pan, Wei
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Chinese University of Hong Kong; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Understanding how genetic variation affects gene expression is essential for a complete picture of the functional pathways that give rise to complex traits. Although numerous studies have established that many genes are differentially expressed in distinct human tissues and cell types, no tools exist for identifying the genes whose expression is differentially regulated. Here we introduce DRAB (differential regulation analysis by bootstrapping), a gene-based method for testing whether patterns...
• SUPPORT VECTOR MACHINE FOR DYNAMIC SURVIVAL PREDICTION WITH TIME-DEPENDENT COVARIATES AI翻译
  作者：Xie, Wenyi; Zeng, Donglin; Wang, Yuanjia
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of Michigan System; University of Michigan; Columbia University
  摘要：Predicting time-to-event outcomes using time-dependent covariates is a challenging problem. Many machine learning approaches, such as tree-based methods and support vector regression, predominantly utilize only baseline covariates. Only a few methods can incorporate time-dependent covariates, but they often lack theoretical justification. In this paper we present a new framework for event time prediction, leveraging the support vector machines to forecast the associated counting processes. Uti...
• PATIENT RECRUITMENT USING ELECTRONIC HEALTH RECORDS UNDER SELECTION BIAS: A TWO-PHASE SAMPLING FRAMEWORK AI翻译
  作者：Zhang, Guanghao; Beesley, Lauren j.; Mukherjee, Bhramar; Shi, Xu
  作者单位：University of Michigan System; University of Michigan; United States Department of Energy (DOE); Los Alamos National Laboratory
  摘要：Electronic health records (EHRs) are increasingly recognized as a costeffective resource for patient recruitment in clinical research. However, how to optimally select a cohort from millions of individuals to answer a scientific question of interest remains unclear. Consider a study to estimate the mean or mean difference of an expensive outcome. Inexpensive auxiliary covariates predictive of the outcome may often be available in patients' health records, presenting an opportunity to recruit p...
• SCALABLE MULTIPLE NETWORK INFERENCE WITH THE JOINT GRAPHICAL HORSESHOE AI翻译
  作者：Lingjaerde, Camilla; Fairfax, Benjamin P.; Richardson, Sylvia; Ruffieux, Helene
  作者单位：MRC Biostatistics Unit; University of Cambridge; University of Oxford
  摘要：Network models are useful tools for modelling complex associations. In statistical omics such models are increasingly popular for identifying and assessing functional relationships and pathways. If a Gaussian graphical model is assumed, conditional independence is determined by the nonzero entries of the inverse covariance (precision) matrix of the data. The Bayesian graphical horseshoe estimator provides a robust and flexible framework for precision matrix inference, as it introduces local, e...