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作者：Rosenblum, Michael; Liu, Han; Yen, En-Hsu
作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Princeton University; University of Texas System; University of Texas Austin
摘要：We propose new, optimal methods for analyzing randomized trials, when it is suspected that treatment effects may differ in two predefined subpopulations. Such subpopulations could be defined by a biomarker or risk factor measured at baseline. The goal is to simultaneously learn which subpopulations benefit from an experimental treatment, while providing strong control of the familywise Type I error rate. We formalize this as a multiple testing problem and show it is computationally infeasible ...
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作者：Rashid, Naim; Sun, Wei; Ibrahim, Joseph G.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：In DAB (DNA after enrichment)-seq experiments, genomic regions related with certain biological processes are enriched/isolated by an assay and are then sequenced on a high-throughput sequencing platform to determine their genomic positions. Statistical analysis of DAE-seq data aims to detect genomic regions with significant aggregations of isolated DNA fragments (enriched regions) versus all the other regions (background). However, many Confounding factors may influence DAE-seq signals. In add...
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作者：Wheeler, Matthew W.; Dunson, David B.; Pandalai, Sudha P.; Baker, Brent A.; Herring, Amy H.
作者单位：Centers for Disease Control & Prevention - USA; National Institute for Occupational Safety & Health (NIOSH); Duke University; Centers for Disease Control & Prevention - USA; National Institute for Occupational Safety & Health (NIOSH); University of North Carolina; University of North Carolina Chapel Hill
摘要：The statistics literature on functional data analysis focuses primarily on flexible black-box approaches, which are designed to allow individual curves to have essentially any shape while characterizing variability. Such methods typically cannot incorporate mechanistic information, which is commonly expressed in terms of differential equations. Motivated by studies of muscle activation, we propose a nonparametric Bayesian approach that takes into account mechanistic understanding of muscle phy...
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作者：Ayra, Eduardo S.; Rios Insua, David; Cano, Javier
作者单位：Universidad Rey Juan Carlos
摘要：According to the International Air Transport Association, the industry fuel bill accounts for more than 25% of the annual airline operating costs. In times of severe economic constraints and increasing fuel costs, air carriers are looking for ways to reduce costs and improve fuel efficiency without putting flight safety into jeopardy. In particular, this is inducing discussions on how much additional fuel to put in a planned route to avoid diverting to an alternate airport due to Air Traffic F...
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作者：Claggett, Brian; Xie, Minge; Tian, Lu
作者单位：Harvard University; Harvard Medical School; Rutgers University System; Rutgers University New Brunswick; Stanford University
摘要：Meta-analysis is a valuable tool for combining information from independent studies. However, most common meta-analysis techniques rely on distributional assumptions that are difficult, if not impossible, to verify. For instance, in the commonly used fixed-effects and random-effects models, we take for granted that the underlying study-level parameters are either exactly the same across individual studies or that they are realizations of a random sample from a population, often under a paramet...
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作者：Gupta, Shuva; Lahiri, S. N.
作者单位：North Carolina State University
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作者：Liu, Xu; Jiang, Hongmei; Zhou, Yong
作者单位：Northwestern University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Shanghai University of Finance & Economics
摘要：In this article, we develop a varying-coefficient density-ratio model for case-control studies. The case and control samples come from two different distributions. Under the model assumption, the ratio of the two densities is related to the linear combination of covariates with varying coefficients through a known function. A special case is the exponential tilt model where the log ratio of the two densities is a linear function of covariates. We propose a local empirical likelihood (EL) appro...
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作者：Miao, Hongyu; Wu, Hulin; Xue, Hongqi
作者单位：University of Rochester
摘要：Existing estimation methods for ordinary differential equation (ODE) models are not applicable to discrete data. The generalized ODE (GODE) model is therefore proposed and investigated for the first time. We develop the likelihood-based parameter estimation and inference methods for GODE models. We propose robust computing algorithms and rigorously investigate the asymptotic properties of the proposed estimator by considering both measurement errors and numerical errors in solving ODEs. The si...
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作者：Zhu, Hongtu; Khondker, Zakaria; Lu, Zhaohua; Ibrahim, Joseph G.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：We propose a Bayesian generalized low-rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local hypothesis testing i...
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作者：Liu, Dandan; Zheng, Yingye; Prentice, Ross L.; Hsu, Li
作者单位：Vanderbilt University; Fred Hutchinson Cancer Center
摘要：Accurate and individualized risk prediction is critical for population control of chronic diseases such as cancer and cardiovascular disease. Large cohort studies provide valuable resources for building risk prediction models, as the risk factors are collected at the baseline and subjects are followed over time until disease occurrence or termination of the study. However, for rare diseases the baseline risk may not be estimated reliably based on cohort data only, due to sparse events. In this...
