听力与言语-语言病理学

行为科学

医学伦理学

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  • A spatial Bayesian latent factor model for image-on-image regression.

    abstract::Image-on-image regression analysis, using images to predict images, is a challenging task, due to (1) the high dimensionality and (2) the complex spatial dependence structures in image predictors and image outcomes. In this work, we propose a novel image-on-image regression model, by extending a spatial Bayesian laten...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13420

    authors: Guo C,Kang J,Johnson TD

    更新日期:2020-12-27 00:00:00

  • Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies.

    abstract::Quantitative traits analyzed in Genome-Wide Association Studies (GWAS) are often nonnormally distributed. For such traits, association tests based on standard linear regression are subject to reduced power and inflated type I error in finite samples. Applying the rank-based inverse normal transformation (INT) to nonno...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13214

    authors: McCaw ZR,Lane JM,Saxena R,Redline S,Lin X

    更新日期:2020-12-01 00:00:00

  • Semiparametric modelling and estimation of covariate-adjusted dependence between bivariate recurrent events.

    abstract::A time-dependent measure, termed the rate ratio, was proposed to assess the local dependence between two types of recurrent event processes in one-sample settings. However, the one-sample work does not consider modeling the dependence by covariates such as subject characteristics and treatments received. The focus of ...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13229

    authors: Ning J,Cai C,Chen Y,Huang X,Wang MC

    更新日期:2020-12-01 00:00:00

  • On symmetric semiparametric two-sample problem.

    abstract::We consider a two-sample problem where data come from symmetric distributions. Usual two-sample data with only magnitudes recorded, arising from case-control studies or logistic discriminant analyses, may constitute a symmetric two-sample problem. We propose a semiparametric model such that, in addition to symmetry, t...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13233

    authors: Li M,Diao G,Qin J

    更新日期:2020-12-01 00:00:00

  • A pairwise pseudo-likelihood approach for left-truncated and interval-censored data under the Cox model.

    abstract::Left truncation commonly occurs in many areas, and many methods have been proposed in the literature for the analysis of various types of left-truncated failure time data. For the situation, a common approach is to conduct the analysis conditional on truncation times, and the method is relatively simple but may not be...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13394

    authors: Wang P,Li D,Sun J

    更新日期:2020-10-15 00:00:00

  • A multilevel mixed effects varying coefficient model with multilevel predictors and random effects for modeling hospitalization risk in patients on dialysis.

    abstract::For patients on dialysis, hospitalizations remain a major risk factor for mortality and morbidity. We use data from a large national database, United States Renal Data System, to model time-varying effects of hospitalization risk factors as functions of time since initiation of dialysis. To account for the three-level...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13205

    authors: Li Y,Nguyen DV,Kürüm E,Rhee CM,Chen Y,Kalantar-Zadeh K,Şentürk D

    更新日期:2020-09-01 00:00:00

  • Fair regression for health care spending.

    abstract::The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk ...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13206

    authors: Zink A,Rose S

    更新日期:2020-09-01 00:00:00

  • Comment on "Wang et al. (2005), Robust estimating functions and bias correction for longitudinal data analysis".

    abstract::This note provides a discussion on the manuscript by Wang et al. (2005) who aim to robustify inference for longitudinal data analysis by replacing the ordinary generalized estimating function with an influence-bounded, possibly biased, version. To adjust for the bias of the ensuing robust estimator, the authors provid...

    journal_title:Biometrics

    pub_type: 信件

    doi:10.1111/biom.13263

    authors: Lunardon N,Menardi G

    更新日期:2020-09-01 00:00:00

  • A test of homogeneity of distributions when observations are subject to measurement errors.

    abstract::When the observed data are contaminated with errors, the standard two-sample testing approaches that ignore measurement errors may produce misleading results, including a higher type-I error rate than the nominal level. To tackle this inconsistency, a nonparametric test is proposed for testing equality of two distribu...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13207

    authors: Lee D,Lahiri SN,Sinha S

    更新日期:2020-09-01 00:00:00

  • Estimating overdispersion in sparse multinomial data.

    abstract::Multinomial data arise in many areas of the life sciences, such as mark-recapture studies and phylogenetics, and will often by overdispersed, with the variance being higher than predicted by a multinomial model. The quasi-likelihood approach to modeling this overdispersion involves the assumption that the variance is ...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13194

    authors: Afroz F,Parry M,Fletcher D

    更新日期:2020-09-01 00:00:00

  • Receiver operating characteristic curves and confidence bands for support vector machines.

    abstract::Many problems that appear in biomedical decision-making, such as diagnosing disease and predicting response to treatment, can be expressed as binary classification problems. The support vector machine (SVM) is a popular classification technique that is robust to model misspecification and effectively handles high-dime...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13365

    authors: Luckett DJ,Laber EB,El-Kamary SS,Fan C,Jhaveri R,Perou CM,Shebl FM,Kosorok MR

    更新日期:2020-08-31 00:00:00

  • Multimodal neuroimaging data integration and pathway analysis.

    abstract::With advancements in technology, the collection of multiple types of measurements on a common set of subjects is becoming routine in science. Some notable examples include multimodal neuroimaging studies for the simultaneous investigation of brain structure and function and multi-omics studies for combining genetic an...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13351

    authors: Zhao Y,Li L,Caffo BS

    更新日期:2020-08-13 00:00:00

  • Ultra high-dimensional semiparametric longitudinal data analysis.

    abstract::As ultra high-dimensional longitudinal data are becoming ever more apparent in fields such as public health and bioinformatics, developing flexible methods with a sparse model is of high interest. In this setting, the dimension of the covariates can potentially grow exponentially as ...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13348

    authors: Green B,Lian H,Yu Y,Zu T

    更新日期:2020-08-04 00:00:00

  • Spatial regression and spillover effects in cluster randomized trials with count outcomes.

    abstract::This paper describes methodology for analyzing data from cluster randomized trials with count outcomes, taking indirect effects as well spatial effects into account. Indirect effects are modeled using a novel application of a measure of depth within the intervention arm. Both direct and indirect effects can be estimat...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13316

    authors: Anaya-Izquierdo K,Alexander N

    更新日期:2020-06-18 00:00:00

  • Two-group Poisson-Dirichlet mixtures for multiple testing.

    abstract::The simultaneous testing of multiple hypotheses is common to the analysis of high-dimensional data sets. The two-group model, first proposed by Efron, identifies significant comparisons by allocating observations to a mixture of an empirical null and an alternative distribution. In the Bayesian nonparametrics literatu...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13314

    authors: Denti F,Guindani M,Leisen F,Lijoi A,Wadsworth WD,Vannucci M

    更新日期:2020-06-14 00:00:00

  • An adaptive independence test for microbiome community data.

    abstract::Advances in sequencing technologies and bioinformatics tools have vastly improved our ability to collect and analyze data from complex microbial communities. A major goal of microbiome studies is to correlate the overall microbiome composition with clinical or environmental variables. La Rosa et al. recently proposed ...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13154

    authors: Song Y,Zhao H,Wang T

    更新日期:2020-06-01 00:00:00

  • A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.

    abstract::Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13307

    authors: Zhao K,Oualkacha K,Lakhal-Chaieb L,Labbe A,Klein K,Ciampi A,Hudson M,Colmegna I,Pastinen T,Zhang T,Daley D,Greenwood CMT

    更新日期:2020-05-21 00:00:00

  • Latent Ornstein-Uhlenbeck models for Bayesian analysis of multivariate longitudinal categorical responses.

    abstract::We propose a Bayesian latent Ornstein-Uhlenbeck (OU) model to analyze unbalanced longitudinal data of binary and ordinal variables, which are manifestations of fewer continuous latent variables. We focus on the evolution of such latent variables when they continuously change over time. Existing approaches are limited ...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13292

    authors: Tran TD,Lesaffre E,Verbeke G,Duyck J

    更新日期:2020-05-11 00:00:00

  • Bayesian latent multi-state modeling for nonequidistant longitudinal electronic health records.

    abstract::Large amounts of longitudinal health records are now available for dynamic monitoring of the underlying processes governing the observations. However, the health status progression across time is not typically observed directly: records are observed only when a subject interacts with the system, yielding irregular and...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13261

    authors: Luo Y,Stephens DA,Verma A,Buckeridge DL

    更新日期:2020-03-11 00:00:00

  • An adaptive trial design to optimize dose-schedule regimes with delayed outcomes.

    abstract::This paper proposes a two-stage phase I-II clinical trial design to optimize dose-schedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will imp...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13116

    authors: Lin R,Thall PF,Yuan Y

    更新日期:2020-03-01 00:00:00

  • Robust inference for the stepped wedge design.

    abstract::Stepped wedge designed trials are a type of cluster-randomized study in which the intervention is introduced to each cluster in a random order over time. This design is often used to assess the effect of a new intervention as it is rolled out across a series of clinics or communities. Based on a permutation argument, ...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13106

    authors: Hughes JP,Heagerty PJ,Xia F,Ren Y

    更新日期:2020-03-01 00:00:00

  • Novel two-phase sampling designs for studying binary outcomes.

    abstract::In biomedical cohort studies for assessing the association between an outcome variable and a set of covariates, usually, some covariates can only be measured on a subgroup of study subjects. An important design question is-which subjects to select into the subgroup to increase statistical efficiency. When the outcome ...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13140

    authors: Wang L,Williams ML,Chen Y,Chen J

    更新日期:2020-03-01 00:00:00

  • Randomization inference with general interference and censoring.

    abstract::Interference occurs between individuals when the treatment (or exposure) of one individual affects the outcome of another individual. Previous work on causal inference methods in the presence of interference has focused on the setting where it is a priori assumed that there is "partial interference," in the sense that...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13125

    authors: Loh WW,Hudgens MG,Clemens JD,Ali M,Emch ME

    更新日期:2020-03-01 00:00:00

  • Large scale maximum average power multiple inference on time-course count data with application to RNA-seq analysis.

    abstract::Experiments that longitudinally collect RNA sequencing (RNA-seq) data can provide transformative insights in biology research by revealing the dynamic patterns of genes. Such experiments create a great demand for new analytic approaches to identify differentially expressed (DE) genes based on large-scale time-course c...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13144

    authors: Cao M,Zhou W,Breidt FJ,Peers G

    更新日期:2020-03-01 00:00:00

  • Fast Bayesian inference in large Gaussian graphical models.

    abstract::Despite major methodological developments, Bayesian inference in Gaussian graphical models remains challenging in high dimension due to the tremendous size of the model space. This article proposes a method to infer the marginal and conditional independence structures between variables by multiple testing, which bypas...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13064

    authors: Leday GGR,Richardson S

    更新日期:2019-12-01 00:00:00

  • Multiclass linear discriminant analysis with ultrahigh-dimensional features.

    abstract::Within the framework of Fisher's discriminant analysis, we propose a multiclass classification method which embeds variable screening for ultrahigh-dimensional predictors. Leveraging interfeature correlations, we show that the proposed linear classifier recovers informative features with probability tending to one and...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13065

    authors: Li Y,Hong HG,Li Y

    更新日期:2019-12-01 00:00:00

  • Pleiotropy informed adaptive association test of multiple traits using genome-wide association study summary data.

    abstract::Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large-scale genome-wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They hav...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13076

    authors: Masotti M,Guo B,Wu B

    更新日期:2019-12-01 00:00:00

  • Semiparametric frailty models for zero-inflated event count data in the presence of informative dropout.

    abstract::Recurrent events data are commonly encountered in medical studies. In many applications, only the number of events during the follow-up period rather than the recurrent event times is available. Two important challenges arise in such studies: (a) a substantial portion of subjects may not experience the event, and (b) ...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13085

    authors: Diao G,Zeng D,Hu K,Ibrahim JG

    更新日期:2019-12-01 00:00:00

  • Model selection for G-estimation of dynamic treatment regimes.

    abstract::Dynamic treatment regimes (DTRs) aim to formalize personalized medicine by tailoring treatment decisions to individual patient characteristics. G-estimation for DTR identification targets the parameters of a structural nested mean model, known as the blip function, from which the optimal DTR is derived. Despite its po...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13104

    authors: Wallace MP,Moodie EEM,Stephens DA

    更新日期:2019-12-01 00:00:00

  • A two-stage experimental design for dilution assays.

    abstract::Dilution assays to determine solute concentration have found wide use in biomedical research. Many dilution assays return imprecise concentration estimates because they are only done to orders of magnitude. Previous statistical work has focused on how to design efficient experiments that can return more precise estima...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13032

    authors: Ferguson JM,Miura TA,Miller CR

    更新日期:2019-09-01 00:00:00

  • Approximate Bayesian inference for discretely observed continuous-time multi-state models.

    abstract::Inference for continuous time multi-state models presents considerable computational difficulties when the process is only observed at discrete time points with no additional information about the state transitions. In fact, for general multi-state Markov model, evaluation of the likelihood function is possible only v...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13019

    authors: Tancredi A

    更新日期:2019-09-01 00:00:00

  • Drawing inferences for high-dimensional linear models: A selection-assisted partial regression and smoothing approach.

    abstract::Drawing inferences for high-dimensional models is challenging as regular asymptotic theories are not applicable. This article proposes a new framework of simultaneous estimation and inferences for high-dimensional linear models. By smoothing over partial regression estimates based on a given variable selection scheme,...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13013

    authors: Fei Z,Zhu J,Banerjee M,Li Y

    更新日期:2019-06-01 00:00:00

  • Exact inference on the random-effects model for meta-analyses with few studies.

    abstract::We describe an exact, unconditional, non-randomized procedure for producing confidence intervals for the grand mean in a normal-normal random effects meta-analysis. The procedure targets meta-analyses based on too few primary studies, ≤ 7 , say, to ...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.12998

    authors: Michael H,Thornton S,Xie M,Tian L

    更新日期:2019-06-01 00:00:00

  • A Bayesian hidden Markov model for detecting differentially methylated regions.

    abstract::Alterations in DNA methylation have been linked to the development and progression of many diseases. The bisulfite sequencing technique presents methylation profiles at base resolution. Count data on methylated and unmethylated reads provide information on the methylation level at each CpG site. As more bisulfite sequ...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.13000

    authors: Ji T

    更新日期:2019-06-01 00:00:00

  • Sparse generalized eigenvalue problem with application to canonical correlation analysis for integrative analysis of methylation and gene expression data.

    abstract::We present a method for individual and integrative analysis of high dimension, low sample size data that capitalizes on the recurring theme in multivariate analysis of projecting higher dimensional data onto a few meaningful directions that are solutions to a generalized eigenvalue problem. We propose a general framew...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.12886

    authors: Safo SE,Ahn J,Jeon Y,Jung S

    更新日期:2018-12-01 00:00:00

  • Methods for multivariate recurrent event data with measurement error and informative censoring.

    abstract::In multivariate recurrent event data regression, observation of recurrent events is usually terminated by other events that are associated with the recurrent event processes, resulting in informative censoring. Additionally, some covariates could be measured with errors. In some applications, an instrumental variable ...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.12857

    authors: Yu H,Cheng YJ,Wang CY

    更新日期:2018-09-01 00:00:00

  • A multi-source adaptive platform design for testing sequential combinatorial therapeutic strategies.

    abstract::Traditional paradigms for clinical translation are challenged in settings where multiple contemporaneous therapeutic strategies have been identified as potentially beneficial. Platform trials have emerged as an approach for sequentially comparing multiple trials using a single protocol. The Ebola virus disease outbrea...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.12841

    authors: Kaizer AM,Hobbs BP,Koopmeiners JS

    更新日期:2018-09-01 00:00:00

  • Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using P-splines.

    abstract::In the field of cardio-thoracic surgery, valve function is monitored over time after surgery. The motivation for our research comes from a study which includes patients who received a human tissue valve in the aortic position. These patients are followed prospectively over time by standardized echocardiographic assess...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.12814

    authors: Andrinopoulou ER,Eilers PHC,Takkenberg JJM,Rizopoulos D

    更新日期:2018-06-01 00:00:00

  • Cox regression model with doubly truncated data.

    abstract::Truncation is a well-known phenomenon that may be present in observational studies of time-to-event data. While many methods exist to adjust for either left or right truncation, there are very few methods that adjust for simultaneous left and right truncation, also known as double truncation. We propose a Cox regressi...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.12809

    authors: Rennert L,Xie SX

    更新日期:2018-06-01 00:00:00

  • Functional multiple indicators, multiple causes measurement error models.

    abstract::Objective measures of oxygen consumption and carbon dioxide production by mammals are used to predict their energy expenditure. Since energy expenditure is not directly observable, it can be viewed as a latent construct with multiple physical indirect measures such as respiratory quotient, volumetric oxygen consumptio...

    journal_title:Biometrics

    pub_type: 杂志文章

    doi:10.1111/biom.12706

    authors: Tekwe CD,Zoh RS,Bazer FW,Wu G,Carroll RJ

    更新日期:2018-03-01 00:00:00

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