5,110 results match your criteria Biometrics [Journal]


Using a surrogate marker for early testing of a treatment effect.

Biometrics 2019 Apr 22. Epub 2019 Apr 22.

Stanford University, Department of Biomedical Data Science, Stanford, California, U.S.A.

The development of methods to identify, validate and use surrogate markers to test for a treatment effect has been an area of intense research interest given the potential for valid surrogate markers to reduce the required costs and follow-up times of future studies. Several quantities and procedures have been proposed to assess the utility of a surrogate marker. However, few methods have been proposed to address how one might use the surrogate marker information to test for a treatment effect at an earlier time point, especially in settings where the primary outcome and the surrogate marker are subject to censoring. Read More

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https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13067
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http://dx.doi.org/10.1111/biom.13067DOI Listing
April 2019
1 Read

Bayesian Modeling of Air Pollution Extremes Using Nested Multivariate Max-Stable Processes.

Biometrics 2019 Apr 22. Epub 2019 Apr 22.

King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Science and Engineering Division (CEMSE), Thuwal, Saudi Arabia.

Capturing the potentially strong dependence among the peak concentrations of multiple air pollutants across a spatial region is crucial for assessing the related public health risks. In order to investigate the multivariate spatial dependence properties of air pollution extremes, we introduce a new class of multivariate max-stable processes. Our proposed model admits a hierarchical tree-based formulation, in which the data are conditionally independent given some latent nested positive stable random factors. Read More

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http://dx.doi.org/10.1111/biom.13051DOI Listing

Multiclass Linear Discriminant Analysis with Ultrahigh-Dimensional Features.

Biometrics 2019 Apr 22. Epub 2019 Apr 22.

Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, U.S.A.

Within the framework of Fisher's discriminant analysis, we propose a multiclass classification method which embeds variable screening for ultrahigh-dimensional predictors. Leveraging inter-feature correlations, we show that the proposed linear classifier recovers informative features with probability tending to one and can asymptotically achieve a zero misclassification rate. We evaluate the finite sample performance of the method via extensive simulations and use this method to classify post-transplantation rejection types based on patientsâĂŹ gene expressions. Read More

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http://dx.doi.org/10.1111/biom.13065DOI Listing

Granger mediation analysis of multiple time series with an application to fMRI.

Authors:
Yi Zhao Xi Luo

Biometrics 2019 Apr 22. Epub 2019 Apr 22.

Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, Texas, U.S.A.

This paper presents Granger Mediation Analysis (GMA), a new framework for causal mediation analysis of multiple time series. This framework is motivated by a functional magnetic resonance imaging (fMRI) experiment where we are interested in estimating the mediation effects between a randomized stimulus time series and brain activity time series from two brain regions. The independent observation assumption is thus unrealistic for this type of time series data. Read More

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http://dx.doi.org/10.1111/biom.13056DOI Listing

Detection of differentially expressed genes in discrete single-cell RNA sequencing data using a hurdle model with correlated random effects.

Biometrics 2019 Apr 22. Epub 2019 Apr 22.

Department of Biostatistics, University of Florida, Gainesville, Florida, U.S.A.

Single-cell RNA sequencing (scRNA-seq) technologies are revolutionary tools allowing researchers to examine gene expression at the level of a single cell. Traditionally, transcriptomic data have been analyzed from bulk samples, masking the heterogeneity now seen across individual cells. Even within the same cellular population, genes can be highly expressed in some cells but not expressed (or lowly expressed) in others. Read More

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http://dx.doi.org/10.1111/biom.13074DOI Listing

Analysis of covariance (ANCOVA) in randomized trials: More precision and valid confidence intervals, without model assumptions.

Biometrics 2019 Apr 22. Epub 2019 Apr 22.

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Maryland 21205, Baltimore, USA.

"Covariate adjustment" in the randomized trial context refers to an estimator of the average treatment effect that adjusts for chance imbalances between study arms in baseline variables (called "covariates"). The baseline variables could include, e.g. Read More

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https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13062
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http://dx.doi.org/10.1111/biom.13062DOI Listing

Integrative analysis of genetical genomics data incorporating network structures.

Biometrics 2019 Apr 22. Epub 2019 Apr 22.

Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824.

In a living organism, tens of thousands of genes are expressed and interact with each other to achieve necessary cellular functions. Gene regulatory networks contain information on regulatory mechanisms and the functions of gene expressions. Thus, incorporating network structures, discerned either through biological experiments or statistical estimations, could potentially increase the selection and estimation accuracy of genes associated with a phenotype of interest. Read More

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https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13072
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http://dx.doi.org/10.1111/biom.13072DOI Listing
April 2019
1 Read

Scalable estimation and regularization for the logistic normal multinomial model.

Biometrics 2019 Apr 22. Epub 2019 Apr 22.

School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing 100871, China.

Clustered multinomial data are prevalent in a variety of applications such as microbiome studies, where metagenomic sequencing data are summarized as multinomial counts for a large number of bacterial taxa per subject. Count normalization with ad hoc zero adjustment tends to result in poor estimates of abundances for taxa with zero or small counts. To account for heterogeneity and overdispersion in such data, we suggest using the logistic normal multinomial (LNM) model with an arbitrary correlation structure to simultaneously estimate the taxa compositions by borrowing information across subjects. Read More

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http://dx.doi.org/10.1111/biom.13071DOI Listing

Variance component tests of multivariate mediation effects under composite null hypotheses.

Authors:
Yen-Tsung Huang

Biometrics 2019 Apr 22. Epub 2019 Apr 22.

Institute of Statistical Science, Academia Sinica, 128 Academia Road Section 2, Nankang, Taipei 11529, Taiwan.

Mediation effects of multiple mediators are determined by two associations: one between an exposure and mediators (S-M) and the other between the mediators and an outcome conditional on the exposure (M-Y). The test for mediation effects is conducted under a composite null hypothesis, i.e. Read More

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http://dx.doi.org/10.1111/biom.13073DOI Listing

Fast bayesian inference in large gaussian graphical models.

Biometrics 2019 Apr 22. Epub 2019 Apr 22.

MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, U.K.

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 bypasses the exploration of the model space. Specifically, we introduce closed-form Bayes factors under the Gaussian conjugate model to evaluate the null hypotheses of marginal and conditional independence between variables. Read More

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http://dx.doi.org/10.1111/biom.13064DOI Listing

Copula-based semiparametric models for spatio-temporal data.

Biometrics 2019 Apr 22. Epub 2019 Apr 22.

Department of Statistical Science, Baylor University, Waco, TX, 76798, USA.

The joint analysis of spatial and temporal processes poses computational challenges due to the data's high dimensionality. Furthermore, such data are commonly non-Gaussian. In this paper, we introduce a copula-based spatio-temporal model for analyzing spatio-temporal data and propose a semiparametric estimator. Read More

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https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13066
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http://dx.doi.org/10.1111/biom.13066DOI Listing

Empirical-likelihood-based criteria for model selection on marginal analysis of longitudinal data with dropout missingness.

Biometrics 2019 Apr 20. Epub 2019 Apr 20.

Division of Biostatistics and Bioinformatics, Department of Public Health Sciences Penn State College of Medicine, Hershey, PA, U.S.A.

Longitudinal data are common in clinical trials and observational studies, where missing outcomes due to dropouts are always encountered. Under such context with the assumption of missing at random, the weighted generalized estimating equations (WGEE) approach is widely adopted for marginal analysis. Model selection on marginal mean regression is a crucial aspect of data analysis, and identifying an appropriate correlation structure for model fitting may also be of interest and importance. Read More

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http://dx.doi.org/10.1111/biom.13060DOI Listing

Improved methods for estimating abundance and related demographic parameters from mark-resight data.

Biometrics 2019 Apr 20. Epub 2019 Apr 20.

New Zealand Department of Conservation, Wellington, New Zealand.

Over the past decade there has been much methodological development for the estimation of abundance and related demographic parameters using so-called "mark-resight" data. Often viewed as a less invasive and less expensive alternative to conventional mark-recapture, mark-resight methods jointly model marked individual encounters and counts of unmarked individuals, and recent extensions accommodate common challenges associated with imperfect detection. When these challenges include both individual detection heterogeneity and an unknown marked sample size, we demonstrate several deficiencies associated with the most widely used mark-resight models currently implemented in the popular capture-recapture freeware Program MARK. Read More

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http://dx.doi.org/10.1111/biom.13058DOI Listing

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
5 Reads

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
April 2019
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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
6 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
4 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
8 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
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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
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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
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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
14 Reads