5,097 results match your criteria Biometrics[Journal]


Assessing alignment between functional markers and ordinal outcomes based on broad sense agreement.

Biometrics 2019 Apr 18. Epub 2019 Apr 18.

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia.

Functional markers and their quantitative features (eg, maximum value, time to maximum, area under the curve [AUC], etc) are increasingly being used in clinical studies to diagnose diseases. It is thus of interest to assess the diagnostic utility of functional markers by assessing alignment between their quantitative features and an ordinal gold standard test that reflects the severity of disease. The concept of broad sense agreement (BSA) has recently been introduced for studying the relationship between continuous and ordinal measurements, and provides a promising tool to address such a question. Read More

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https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13063
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http://dx.doi.org/10.1111/biom.13063DOI Listing
April 2019
1 Read

Prediction analysis for microbiome sequencing data.

Biometrics 2019 Apr 17. Epub 2019 Apr 17.

SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Read More

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https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13061
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http://dx.doi.org/10.1111/biom.13061DOI Listing
April 2019
1 Read

Double-wavelet transform for multisubject task-induced functional magnetic resonance imaging data.

Biometrics 2019 Apr 15. Epub 2019 Apr 15.

Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.

The goal of this article is to model multisubject task-induced functional magnetic resonance imaging (fMRI) response among predefined regions of interest (ROIs) of the human brain. Conventional approaches to fMRI analysis only take into account temporal correlations, but do not rigorously model the underlying spatial correlation due to the complexity of estimating and inverting the high dimensional spatio-temporal covariance matrix. Other spatio-temporal model approaches estimate the covariance matrix with the assumption of stationary time series, which is not always feasible. Read More

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http://dx.doi.org/10.1111/biom.13055DOI Listing

Confidence bands for multiplicative hazards models: Flexible resampling approaches.

Biometrics 2019 Apr 15. Epub 2019 Apr 15.

Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark.

We propose new resampling-based approaches to construct asymptotically valid time-simultaneous confidence bands for cumulative hazard functions in multistate Cox models. In particular, we exemplify the methodology in detail for the simple Cox model with time-dependent covariates, where the data may be subject to independent right-censoring or left-truncation. We use simulations to investigate their finite sample behavior. Read More

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http://dx.doi.org/10.1111/biom.13059DOI Listing

Rejoinder to Discussions on: Model confidence bounds for variable selection.

Biometrics 2019 Apr 6. Epub 2019 Apr 6.

Department of Operations, Business Analytics, and Information Systems, University of Cincinnati.

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https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13020
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http://dx.doi.org/10.1111/biom.13020DOI Listing
April 2019
3 Reads

Discussion on "Model Confidence Bounds for Variable Selection" by Yang Li, Yuetian Luo, Davide Ferrari, Xiaonan Hu, and Yichen Qin.

Biometrics 2019 Apr 3. Epub 2019 Apr 3.

Departments of Mathematics and Computer Science, Université libre de Bruxelles, Brussels, Belgium.

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http://dx.doi.org/10.1111/biom.13022DOI Listing

Discussion of "A hybrid phase I-II/III clinical trial design allowing dose re-optimization in phase III" by Andrew G. Chapple and Peter F. Thall.

Biometrics 2019 Apr 3. Epub 2019 Apr 3.

Research Institute, NorthShore University HealthSystem, Evanston, Illinois.

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http://dx.doi.org/10.1111/biom.12992DOI Listing

Discussion of "A Hybrid Phase I-II/III Clinical Trial Design Allowing Dose Re-Optimization in Phase III" by Andrew G. Chapple and Peter F. Thall.

Biometrics 2019 Apr 3. Epub 2019 Apr 3.

Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland.

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http://dx.doi.org/10.1111/biom.12993DOI Listing

A Hybrid Omnibus Test for Generalized Semiparametric Single-Index Models with High-Dimensional Covariate Sets.

Biometrics 2019 Mar 11. Epub 2019 Mar 11.

Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843-3143, and School of Mathematical Sciences, University of Technology, Sydney, Broadway NSW 2007.

Numerous statistical methods have been developed for analyzing high-dimensional data. These methods often focus on variable selection approaches but are limited for the purpose of testing with high-dimensional data. They are often required to have explicit likelihood functions. Read More

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http://dx.doi.org/10.1111/biom.13054DOI Listing

Accounting for phenology in the analysis of animal movement.

Biometrics 2019 Mar 11. Epub 2019 Mar 11.

U.S. Geological Survey, Alaska Science Center, 4210 University Drive, Anchorage, AK 99508, USA.

The analysis of animal tracking data provides important scientific understanding and discovery in ecology. Observations of animal trajectories using telemetry devices provide researchers with information about the way animals interact with their environment and each other. For many species, specific geographical features in the landscape can have a strong effect on behavior. Read More

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http://dx.doi.org/10.1111/biom.13052DOI Listing
March 2019
1 Read

High Dimensional Mediation Analysis with Latent Variables.

Biometrics 2019 Mar 11. Epub 2019 Mar 11.

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA.

We propose a model for high dimensional mediation analysis that includes latent variables. We describe our model in the context of an epidemiologic study for incident breast cancer with a main exposure and a large number of biomarkers (i.e. Read More

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http://doi.wiley.com/10.1111/biom.13053
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http://dx.doi.org/10.1111/biom.13053DOI Listing
March 2019
4 Reads

Causal inference with interfering units for cluster and population level treatment allocation programs.

Biometrics 2019 Mar 11. Epub 2019 Mar 11.

Department of Statistics and Data Sciences and Department of Women's Health, University of Texas at Austin and Dell Medical School, Austin, Texas.

Interference arises when an individual's potential outcome depends on the individual treatment level, but also on the treatment level of others. A common assumption in the causal inference literature in the presence of interference is partial interference, implying that the population can be partitioned in clusters of individuals whose potential outcomes only depend on the treatment of units within the same cluster. Previous literature has defined average potential outcomes under counterfactual scenarios where treatments are randomly allocated to units within a cluster. Read More

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http://dx.doi.org/10.1111/biom.13049DOI Listing

Marginal analysis of ordinal clustered longitudinal data with informative cluster size.

Biometrics 2019 Mar 11. Epub 2019 Mar 11.

Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118.

The issue of informative cluster size (ICS) often arises in the analysis of dental data. ICS describes a situation where the outcome of interest is related to cluster size. Much of the work on modeling marginal inference in longitudinal studies with potential ICS has focused on continuous outcomes. Read More

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http://doi.wiley.com/10.1111/biom.13050
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http://dx.doi.org/10.1111/biom.13050DOI Listing
March 2019
5 Reads

A Bayesian random partition model for sequential refinement and coagulation.

Biometrics 2019 Feb 28. Epub 2019 Feb 28.

Departmento de Estadística, Pontificia Universidad Católica de Chile, Santiago, Chile.

We analyze time-course protein activation data to track the changes in protein expression over time after exposure to drugs such as protein inhibitors. Protein expression is expected to change over time in response to the intervention in different ways due to biological pathways. We therefore allow for clusters of proteins with different treatment effects, and allow these clusters to change over time. Read More

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http://dx.doi.org/10.1111/biom.13047DOI Listing
February 2019
3 Reads

Threshold selection for covariance estimation.

Biometrics 2019 Feb 28. Epub 2019 Feb 28.

Department of Biostatistics, University of Florida, Gainesville, Florida 32611.

Thresholding is a regularization method commonly used for covariance estimation, which provides consistent estimators if the population covariance satisfies certain sparsity condition (Bickel and Levina, 2008a; Cai and Liu, 2011). However, the performance of the thresholding estimators heavily depends on the threshold level. By minimizing the Frobenius risk of the adaptive thresholding estimator for covariances, we conduct a theoretical study for the optimal threshold level, and obtain its analytical expression. Read More

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http://dx.doi.org/10.1111/biom.13048DOI Listing
February 2019

Cross-sectional HIV Incidence Estimation Accounting for Heterogeneity Across Communities.

Biometrics 2019 Feb 12. Epub 2019 Feb 12.

Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts 02215, USA.

Accurate estimation of HIV incidence rates is crucial for the monitoring of HIV epidemics, the evaluation of prevention programs, and the design of prevention studies. Traditional cohort approaches to measure HIV incidence require repeatedly testing large cohorts of HIV uninfected individuals with a HIV diagnostic test (e.g. Read More

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http://dx.doi.org/10.1111/biom.13046DOI Listing
February 2019

Exact inference for integrated population modelling.

Biometrics 2019 Feb 12. Epub 2019 Feb 12.

National Centre for Statistical Ecology, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, Kent CT2 7FS, England.

Integrated population modelling is widely used in statistical ecology. It allows data from population time series and independent surveys to be analysed simultaneously. In classical analysis the time-series likelihood component can be conveniently approximated using Kalman filter methodology. Read More

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http://dx.doi.org/10.1111/biom.13045DOI Listing
February 2019

Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study.

Biometrics 2019 Feb 6. Epub 2019 Feb 6.

Department of Statistics, University of Florida, Gainesville, Florida.

In lifestyle intervention trials, where the goal is to change a participant's weight or modify their eating behavior, self-reported diet is a longitudinal outcome variable that is subject to measurement error. We propose a statistical framework for correcting for measurement error in longitudinal self-reported dietary data by combining intervention data with auxiliary data from an external biomarker validation study where both self-reported and recovery biomarkers of dietary intake are available. In this setting, dietary intake measured without error in the intervention trial is missing data and multiple imputation is used to fill in the missing measurements. Read More

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http://dx.doi.org/10.1111/biom.13044DOI Listing
February 2019
1 Read

Causal inference when counterfactuals depend on the proportion of all subjects exposed.

Biometrics 2019 Feb 4. Epub 2019 Feb 4.

Division of Biostatistics, University of California at Berkeley, Berkeley, California.

The assumption that no subject's exposure affects another subject's outcome, known as the no-interference assumption, has long held a foundational position in the study of causal inference. However, this assumption may be violated in many settings, and in recent years has been relaxed considerably. Often this has been achieved with either the aid of a known underlying network, or the assumption that the population can be partitioned into separate groups, between which there is no interference, and within which each subject's outcome may be affected by all the other subjects in the group via the proportion exposed (the stratified interference assumption). Read More

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http://dx.doi.org/10.1111/biom.13034DOI Listing
February 2019
1 Read

Familywise error control in multi-armed response-adaptive trials.

Biometrics 2019 Feb 4. Epub 2019 Feb 4.

MRC Biostatistics Unit, University of Cambridge, IPH Forvie Site, Robinson Way, Cambridge CB2 0SR, UK.

Response-adaptive designs allow the randomization probabilities to change during the course of a trial based on cumulated response data so that a greater proportion of patients can be allocated to the better performing treatments. A major concern over the use of response-adaptive designs in practice, particularly from a regulatory viewpoint, is controlling the type I error rate. In particular, we show that the naïve z-test can have an inflated type I error rate even after applying a Bonferroni correction. Read More

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http://dx.doi.org/10.1111/biom.13042DOI Listing
February 2019

An iterative penalized least squares approach to sparse canonical correlation analysis.

Authors:
Qing Mai Xin Zhang

Biometrics 2019 Feb 4. Epub 2019 Feb 4.

Department of Statistics, Florida State University, Tallahassee, Florida.

It is increasingly interesting to model the relationship between two sets of high-dimensional measurements with potentially high correlations. Canonical correlation analysis (CCA) is a classical tool that explores the dependency of two multivariate random variables and extracts canonical pairs of highly correlated linear combinations. Driven by applications in genomics, text mining, and imaging research, among others, many recent studies generalize CCA to high-dimensional settings. Read More

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http://dx.doi.org/10.1111/biom.13043DOI Listing
February 2019

A two-stage experimental design for dilution assays.

Biometrics 2019 Jan 28. Epub 2019 Jan 28.

Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho.

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 estimates, however this work has not considered the practical difficulties of implementing these designs in the laboratory. Read More

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http://doi.wiley.com/10.1111/biom.13032
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http://dx.doi.org/10.1111/biom.13032DOI Listing
January 2019
3 Reads

A cluster-adjusted rank-based test for a clinical trial concerning multiple endpoints with application to dietary intervention assessment.

Biometrics 2019 Jan 28. Epub 2019 Jan 28.

LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.

Multiple endpoints are often naturally clustered based on their scientific interpretations. Tests that compare these clustered outcomes between independent groups may lose efficiency if the cluster structures are not properly accounted for. For the two-sample generalized Behrens-Fisher hypothesis concerning multiple endpoints we propose a cluster-adjusted multivariate test procedure for the comparison and demonstrate its gain in efficiency over test procedures that ignore the clusters. Read More

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http://dx.doi.org/10.1111/biom.13029DOI Listing
January 2019
1.568 Impact Factor

Efficient methods for signal detection from correlated adverse events in clinical trials.

Biometrics 2019 Jan 28. Epub 2019 Jan 28.

Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

It is an important and yet challenging task to identify true signals from many adverse events that may be reported during the course of a clinical trial. One unique feature of drug safety data from clinical trials, unlike data from post-marketing spontaneous reporting, is that many types of adverse events are reported by only very few patients leading to rare events. Due to the limited study size, the p-values of testing whether the rate is higher in the treatment group across all types of adverse events are in general not uniformly distributed under the null hypothesis that there is no difference between the treatment group and the placebo group. Read More

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http://dx.doi.org/10.1111/biom.13031DOI Listing
January 2019

A Bayesian hierarchical model estimating CACE in meta-analysis of randomized clinical trials with noncompliance.

Biometrics 2019 Jan 28. Epub 2019 Jan 28.

Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455.

Noncompliance to assigned treatment is a common challenge in analysis and interpretation of randomized clinical trials. The complier average causal effect (CACE) approach provides a useful tool for addressing noncompliance, where CACE is defined as the average difference in potential outcomes for the response in the subpopulation of subjects who comply with their assigned treatments. In this article, we present a Bayesian hierarchical model to estimate the CACE in a meta-analysis of randomized clinical trials where compliance may be heterogeneous between studies. Read More

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http://dx.doi.org/10.1111/biom.13028DOI Listing
January 2019

Fast likelihood-based inference for latent count models using the saddlepoint approximation.

Biometrics 2019 Jan 28. Epub 2019 Jan 28.

Department of Statistics, University of Auckland, Private Bag 92019, Auckland, New Zealand.

Latent count models constitute an important modeling class in which a latent vector of counts, , is summarized or corrupted for reporting, yielding observed data where is a known but non-invertible matrix. The observed vector generally follows an unknown multivariate distribution with a complicated dependence structure. Latent count models arise in diverse fields, such as estimation of population size from capture-recapture studies; inference on multi-way contingency tables summarized by marginal totals; or analysis of route flows in networks based on traffic counts at a subset of nodes. Read More

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http://dx.doi.org/10.1111/biom.13030DOI Listing
January 2019
2 Reads

A statistical method for joint estimation of cis-eQTLs and parent-of-origin effects under family trio design.

Biometrics 2019 Jan 22. Epub 2019 Jan 22.

Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.

RNA sequencing allows one to study allelic imbalance of gene expression, which may be due to genetic factors or genomic imprinting (i.e., higher expression of maternal or paternal allele). Read More

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http://dx.doi.org/10.1111/biom.13026DOI Listing
January 2019
2 Reads

A sensitivity analysis approach for informative dropout using shared parameter models.

Biometrics 2019 Jan 22. Epub 2019 Jan 22.

Department of Statistics, University of Florida, Gainesville, Florida 32611.

Shared parameter models (SPMs) are a useful approach to addressing bias from informative dropout in longitudinal studies. In SPMs it is typically assumed that the longitudinal outcome process and the dropout time are independent, given random effects and observed covariates. However, this conditional independence assumption is unverifiable. Read More

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http://dx.doi.org/10.1111/biom.13027DOI Listing
January 2019
1 Read

Model confidence bounds for variable selection.

Biometrics 2019 Jan 16. Epub 2019 Jan 16.

Department of Operations, Business Analytics, and Information Systems, University of Cincinnati.

In this article, we introduce the concept of model confidence bounds (MCB) for variable selection in the context of nested models. Similarly to the endpoints in the familiar confidence interval for parameter estimation, the MCB identifies two nested models (upper and lower confidence bound models) containing the true model at a given level of confidence. Instead of trusting a single selected model obtained from a given model selection method, the MCB proposes a group of nested models as candidates and the MCB's width and composition enable the practitioner to assess the overall model selection uncertainty. Read More

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http://doi.wiley.com/10.1111/biom.13024
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http://dx.doi.org/10.1111/biom.13024DOI Listing
January 2019
7 Reads
1.568 Impact Factor

Inference for case-control studies with incident and prevalent cases.

Biometrics 2019 Jan 16. Epub 2019 Jan 16.

National Cancer Institute, National Institutes of Health, Rockville, Maryland.

We propose and study a fully efficient method to estimate associations of an exposure with disease incidence when both, incident cases and prevalent cases, i.e., individuals who were diagnosed with the disease at some prior time point and are alive at the time of sampling, are included in a case-control study. Read More

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http://dx.doi.org/10.1111/biom.13023DOI Listing
January 2019
3 Reads

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

Authors:
Andrea Tancredi

Biometrics 2019 Jan 16. Epub 2019 Jan 16.

Department of Methods and Models for Economics Territory and Finance, Sapienza University of Rome, Via del Castro Laurenziano 9, 00161, Rome, Italy.

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 via intensive numerical approximations. Moreover, in real applications, transitions between states may depend on the time since entry into the current state, and semi-Markov models, where the likelihood function is not available in closed form, should be fitted to the data. Read More

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http://dx.doi.org/10.1111/biom.13019DOI Listing
January 2019
2 Reads

Causal comparative effectiveness analysis of dynamic continuous-time treatment initiation rules with sparsely measured outcomes and death.

Biometrics 2019 Jan 14. Epub 2019 Jan 14.

Brown University School of Public Health, Providence, Rhode Island, 02912, USA.

Evidence supporting the current World Health Organization recommendations of early antiretroviral therapy (ART) initiation for adolescents is inconclusive. We leverage a large observational data and compare, in terms of mortality and CD4 cell count, the dynamic treatment initiation rules for HIV-infected adolescents. Our approaches extend the marginal structural model for estimating outcome distributions under dynamic treatment regimes (DTR), developed in Robins et al. Read More

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http://dx.doi.org/10.1111/biom.13018DOI Listing
January 2019
1 Read

Estimations of the Joint Distribution of Failure Time and Failure Type with Dependent Truncation.

Biometrics 2018 Dec 20. Epub 2018 Dec 20.

Institute of Statistics, National Tsing Hua University, Hsin-Chu 300, Taiwan.

In biomedical studies involving survival data, the observation of failure times is sometimes accompanied by a variable which describes the type of failure event (Kalbfleisch and Prentice, 2002). This paper considers two specific challenges which are encountered in the joint analysis of failure time and failure type. First, because the observation of failure times is subject to left truncation, the sampling bias extends to the failure type which is associated with the failure time. Read More

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http://doi.wiley.com/10.1111/biom.13017
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http://dx.doi.org/10.1111/biom.13017DOI Listing
December 2018
4 Reads

Dependence modeling for recurrent event times subject to right-censoring with D-vine copulas.

Biometrics 2018 Dec 14. Epub 2018 Dec 14.

Center for Statistics, I-BioStat, Universiteit Hasselt, Agoralaan 1, 3590 Diepenbeek, Belgium.

In many time-to-event studies, the event of interest is recurrent. Here, the data for each sample unit correspond to a series of gap times between the subsequent events. Given a limited follow-up period, the last gap time might be right-censored. Read More

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http://dx.doi.org/10.1111/biom.13014DOI Listing
December 2018
2 Reads

Distribution-free estimation of local growth rates around interval censored anchoring events.

Biometrics 2018 Dec 14. Epub 2018 Dec 14.

Department of Biostatistics, Indiana University Fairbanks School of Public Health, Indiana University School of Medicine, Indianapolis, Indiana 46202.

Biological processes are usually defined on timelines that are anchored by specific events. For example, cancer growth is typically measured by the change in tumor size from the time of oncogenesis. In the absence of such anchoring events, longitudinal assessments of the outcome lose their temporal reference. Read More

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http://dx.doi.org/10.1111/biom.13015DOI Listing
December 2018
1 Read

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

Biometrics 2018 Dec 14. Epub 2018 Dec 14.

Department of Biostatistics, University of Michigan, Ann Arbor, Michigan.

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, we reduce the problem to low-dimensional least squares estimations. Read More

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https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13013
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http://dx.doi.org/10.1111/biom.13013DOI Listing
December 2018
13 Reads

A modified partial likelihood score method for Cox regression with covariate error under the internal validation design.

Biometrics 2018 Dec 7. Epub 2018 Dec 7.

Department of Biostatistics, Yale School of Public Health and Department of Statistics, Yale University, 60 College Street, New Haven, Connecticut 06520.

We develop a new method for covariate error correction in the Cox survival regression model, given a modest sample of internal validation data. Unlike most previous methods for this setting, our method can handle covariate error of arbitrary form. Asymptotic properties of the estimator are derived. Read More

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http://doi.wiley.com/10.1111/biom.13012
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http://dx.doi.org/10.1111/biom.13012DOI Listing
December 2018
16 Reads

Linked matrix factorization.

Biometrics 2018 Dec 5. Epub 2018 Dec 5.

Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota 55455.

Several recent methods address the dimension reduction and decomposition of linked high-content data matrices. Typically, these methods consider one dimension, rows or columns, that is shared among the matrices. This shared dimension may represent common features measured for different sample sets (horizontal integration) or a common sample set with features from different platforms (vertical integration). Read More

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http://doi.wiley.com/10.1111/biom.13010
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http://dx.doi.org/10.1111/biom.13010DOI Listing
December 2018
30 Reads

Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals.

Biometrics 2018 Nov 29. Epub 2018 Nov 29.

Department of Epidemiology, Harvard-T.H. Chan School of Public Health, Boston, MA, USA.

We consider methods for causal inference in randomized trials nested within cohorts of trial-eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite-sample performance in simulation studies. Read More

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http://dx.doi.org/10.1111/biom.13009DOI Listing
November 2018
1 Read

Optimal design of multiple-objective Lot Quality Assurance Sampling (LQAS) plans.

Biometrics 2018 Nov 29. Epub 2018 Nov 29.

Department of Biostatistics, Fielding School of Public Health, UCLA, 10833 Le Conte Ave., Los Angeles, California 90095-1772.

Lot Quality Assurance Sampling (LQAS) plans are widely used for health monitoring purposes. We propose a systematic approach to design multiple-objective LQAS plans that meet user-specified type 1 and 2 error rates and targets for selected diagnostic accuracy metrics. These metrics may include sensitivity, specificity, positive predictive value, and negative predictive value in high or low anticipated prevalence rate populations. Read More

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http://dx.doi.org/10.1111/biom.13008DOI Listing
November 2018
2 Reads

Nonparametric group sequential methods for evaluating survival benefit from multiple short-term follow-up windows.

Biometrics 2018 Nov 20. Epub 2018 Nov 20.

The University of Texas MD Anderson Cancer Center, Department of Biostatistics, Houston, Texas 77030.

This article takes a fresh look at group sequential methods applied to two-sample tests of censored survival data and proposes an alternative method of defining and evaluating treatment benefit. Our method re-purposes traditional censored event time data into a sequence of short-term outcomes taken from (potentially overlapping) follow-up windows. A new two-sample restricted means test based on this restructured follow-up data is proposed along with group sequential methods for its use in the clinical trial setting. Read More

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http://dx.doi.org/10.1111/biom.13007DOI Listing
November 2018
2 Reads

Integrative multi-view regression: Bridging group-sparse and low-rank models.

Biometrics 2018 Nov 20. Epub 2018 Nov 20.

Department of Statistics, University of Connecticut, Storrs, Connecticut.

Multi-view data have been routinely collected in various fields of science and engineering. A general problem is to study the predictive association between multivariate responses and multi-view predictor sets, all of which can be of high dimensionality. It is likely that only a few views are relevant to prediction, and the predictors within each relevant view contribute to the prediction collectively rather than sparsely. Read More

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http://dx.doi.org/10.1111/biom.13006DOI Listing
November 2018
14 Reads

A smoothing-based goodness-of-fit test of covariance for functional data.

Biometrics 2018 Nov 19. Epub 2018 Nov 19.

Department of Statistics, North Carolina State University, Raleigh, North Carolina.

Functional data methods are often applied to longitudinal data as they provide a more flexible way to capture dependence across repeated observations. However, there is no formal testing procedure to determine if functional methods are actually necessary. We propose a goodness-of-fit test for comparing parametric covariance functions against general nonparametric alternatives for both irregularly observed longitudinal data and densely observed functional data. Read More

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http://dx.doi.org/10.1111/biom.13005DOI Listing
November 2018
2 Reads

A Bayesian hidden Markov model for detecting differentially methylated regions.

Authors:
Tieming Ji

Biometrics 2018 Nov 15. Epub 2018 Nov 15.

Department of Statistics, University of Missouri at Columbia, Columbia, Missouri 65211.

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. Read More

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http://doi.wiley.com/10.1111/biom.13000
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http://dx.doi.org/10.1111/biom.13000DOI Listing
November 2018
12 Reads

The single-index/Cox mixture cure model.

Biometrics 2018 Nov 14. Epub 2018 Nov 14.

Institute of Statistics, Biostatistics and Actuarial Sciences, UCLouvain, Louvain-la-Neuve, Belgium.

In survival analysis, it often happens that a certain fraction of the subjects under study never experience the event of interest, that is, they are considered "cured." In the presence of covariates, a common model for this type of data is the mixture cure model, which assumes that the population consists of two subpopulations, namely the cured and the non-cured ones, and it writes the survival function of the whole population given a set of covariates as a mixture of the survival function of the cured subjects (which equals one), and the survival function of the non-cured ones. In the literature, one usually assumes that the mixing probabilities follow a logistic model. Read More

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http://dx.doi.org/10.1111/biom.12999DOI Listing
November 2018
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Composite kernel machine regression based on likelihood ratio test for joint testing of genetic and gene-environment interaction effect.

Biometrics 2018 Nov 14. Epub 2018 Nov 14.

Public Health Sciences Division,, Fred Hutchinson Cancer Research Center, Seattle, Washington.

Most common human diseases are a result from the combined effect of genes, the environmental factors, and their interactions such that including gene-environment (GE) interactions can improve power in gene mapping studies. The standard strategy is to test the SNPs, one-by-one, using a regression model that includes both the SNP effect and the GE interaction. However, the SNP-by-SNP approach has serious limitations, such as the inability to model epistatic SNP effects, biased estimation, and reduced power. Read More

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http://doi.wiley.com/10.1111/biom.13003
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http://dx.doi.org/10.1111/biom.13003DOI Listing
November 2018
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Causal inference accounting for unobserved confounding after outcome regression and doubly robust estimation.

Biometrics 2018 Nov 14. Epub 2018 Nov 14.

Department of Statistics, USBE, Umeå  University, 901 87, Umeå, Sweden.

Causal inference with observational data can be performed under an assumption of no unobserved confounders (unconfoundedness assumption). There is, however, seldom clear subject-matter or empirical evidence for such an assumption. We therefore develop uncertainty intervals for average causal effects based on outcome regression estimators and doubly robust estimators, which provide inference taking into account both sampling variability and uncertainty due to unobserved confounders. Read More

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http://dx.doi.org/10.1111/biom.13001DOI Listing
November 2018
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Convex clustering analysis for histogram-valued data.

Biometrics 2018 Nov 14. Epub 2018 Nov 14.

Department of Mathematics Education, Korea National University of Education, Cheongju, Chungbuk, 28173, Korea.

In recent years, there has been increased interest in symbolic data analysis, including for exploratory analysis, supervised and unsupervised learning, time series analysis, etc. Traditional statistical approaches that are designed to analyze single-valued data are not suitable because they cannot incorporate the additional information on data structure available in symbolic data, and thus new techniques have been proposed for symbolic data to bridge this gap. In this article, we develop a regularized convex clustering approach for grouping histogram-valued data. Read More

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http://dx.doi.org/10.1111/biom.13004DOI Listing
November 2018
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Exact inference on the random-effects model for meta-analyses with few studies.

Biometrics 2018 Nov 14. Epub 2018 Nov 14.

Department of Biomedical Data Science, Stanford University, Stanford, California.

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, , say, to allow for the conventional asymptotic estimators, e.g. Read More

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http://doi.wiley.com/10.1111/biom.12998
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http://dx.doi.org/10.1111/biom.12998DOI Listing
November 2018
11 Reads