5,081 results match your criteria Biometrics [Journal]


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.

Unit of Biostatistics; Gertner Institute for Epidemiology & Health Policy Research, Sheba Medical Center, Tel-Hashomer, Ramat Gan, Israel.

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

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, USA.

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, FL, USA.

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, ID, USA.

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://dx.doi.org/10.1111/biom.13032DOI Listing
January 2019

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, USA.

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, MN 55455, USA.

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, z, is summarized or corrupted for reporting, yielding observed data y = T z where T is a known but non-invertible matrix. The observed vector y 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

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, USA.

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
1 Read

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, FL 32611, USA.

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 identities 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
3 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, MD, USA.

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 corresponds 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, USA.

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, USA.

Drawing inferences for high-dimensional models is challenging as regular asymptotic theories are not applicable. This paper 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 a low-dimensional least squares estimation. 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
9 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, New Haven, CT 06520, USA.

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
9 Reads

Linked Matrix Factorization.

Biometrics 2018 Dec 5. Epub 2018 Dec 5.

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

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
16 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, USA.

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, TX 77030, USA.

This manuscript 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, CT.

Multi-view data have been routinely collected in various _elds 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
10 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, USA.

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
1 Read

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, USA.

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
8 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, i.e. they are considered 'cured'. Read More

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http://dx.doi.org/10.1111/biom.12999DOI Listing
November 2018
1 Read

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, WA, USA.

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
3 Reads

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
2 Reads

Convex Clustering Analysis for Histogram-Valued Data.

Biometrics 2018 Nov 14. Epub 2018 Nov 14.

Dept. of Mathematics Education, Korea National U. 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 paper, 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
3 Reads

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.

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 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

A robust RUV-testing procedure via γ-divergence.

Authors:
Hung Hung

Biometrics 2018 Nov 14. Epub 2018 Nov 14.

Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taiwan.

Identification of differentially expressed genes (DE genes) is commonly conducted in modern biomedical research. However, unwanted variation inevitably arises during the data collection process, which can make the detection results heavily biased. Various methods have been suggested for removing the unwanted variation while keeping the biological variation to ensure a reliable analysis result. Read More

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http://doi.wiley.com/10.1111/biom.13002
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http://dx.doi.org/10.1111/biom.13002DOI Listing
November 2018
7 Reads

A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints.

Biometrics 2018 Nov 3. Epub 2018 Nov 3.

Department of Applied Statistics, Chung-Ang University, Seoul, Korea.

In application of diagnostic accuracy, it is possible that a priori information may exist regarding the test score distributions, either between different disease populations for a single test or between multiple correlated tests. Few have considered constrained diagnostic accuracy analysis when the true disease status is binary; almost none when the disease status is ordinal. Motivated by a study on diagnosing endometriosis, we propose an approach to estimating diagnostic accuracy measures that can incorporate different stochastic order constraints on the test scores when an ordinal true disease status is in consideration. Read More

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http://dx.doi.org/10.1111/biom.12997DOI Listing
November 2018
7 Reads

A non-randomized procedure for large-scale heterogeneous multiple discrete testing based on randomized tests.

Biometrics 2018 Nov 2. Epub 2018 Nov 2.

Department of Genetics, Washington University in St. Louis, Saint Louis, MO 63130, USA.

In the analysis of next-generation sequencing technology, massive discrete data are generated from short read counts with varying biological coverage. Conducting conditional hypothesis testing such as Fisher's Exact Test at every genomic region of interest thus leads to a heterogeneous multiple discrete testing problem. However, most existing multiple testing procedures for controlling the false discovery rate (FDR) assume that test statistics are continuous and become conservative for discrete tests. Read More

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http://doi.wiley.com/10.1111/biom.12996
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http://dx.doi.org/10.1111/biom.12996DOI Listing
November 2018
2 Reads

Log-ratio Lasso: Scalable, Sparse Estimation for Log-ratio Models.

Biometrics 2018 Nov 2. Epub 2018 Nov 2.

Departments of Biomedical Data Science and Statistics, Stanford University.

Positive-valued signal data is common in the biological and medical sciences, due to the prevalence of mass spectrometry other imaging techniques. With such data, only the relative intensities of the raw measurements are meaningful. It is desirable to consider models consisting of the log-ratios of all pairs of the raw features, since log-ratios are the simplest meaningful derived features. Read More

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http://doi.wiley.com/10.1111/biom.12995
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http://dx.doi.org/10.1111/biom.12995DOI Listing
November 2018
8 Reads

A Hybrid Phase I-II/III Clinical Trial Design Allowing Dose Re-Optimization in Phase III.

Biometrics 2018 Oct 26. Epub 2018 Oct 26.

Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA.

Conventionally, evaluation of a new drug, A, is done in three phases. Phase I is based on toxicity to determine a \maximum tolerable dose" (MTD) of A, phase II is conducted to decide whether A at the MTD is promising in terms of response probability, and if so a large randomized phase III trial is conducted to compare A to a control treatment, C; usually based on survival time or progression free survival time. It is widely recognized that this paradigm has many flaws. Read More

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http://doi.wiley.com/10.1111/biom.12994
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http://dx.doi.org/10.1111/biom.12994DOI Listing
October 2018
7 Reads

Bagging and Deep Learning in Optimal Individualized Treatment Rules.

Biometrics 2018 Oct 26. Epub 2018 Oct 26.

Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.

An ENsemble Deep Learning Optimal Treatment (EndLot) approach is proposed for personalized medicine problems. The statistical framework of the proposed method is based on the outcome weighted learning (OWL) framework which transforms the optimal decision rule problem into a weighted classification problem. We further employ an ensemble of deep neural networks (DNNs) to learn the optimal decision rule. Read More

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http://doi.wiley.com/10.1111/biom.12990
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http://dx.doi.org/10.1111/biom.12990DOI Listing
October 2018
11 Reads

Semiparametric Bayesian latent variable regression for skewed multivariate data.

Biometrics 2018 Oct 26. Epub 2018 Oct 26.

Department of Medicine, Brigham and Women's Hospital, Boston, MA.

For many real-life studies with skewed multivariate responses, the level of skewness and association structure assumptions are essential for evaluating the covariate effects on the response and its predictive distribution. We present a novel semiparametric multivariate model and associated Bayesian analysis for multivariate skewed responses. Similar to multivariate Gaussian densities, this multivariate model is closed under marginalization, allows a wide class of multivariate associations, and has meaningful physical interpretations of skewness levels and covariate effects on the marginal density. Read More

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http://doi.wiley.com/10.1111/biom.12989
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http://dx.doi.org/10.1111/biom.12989DOI Listing
October 2018
12 Reads

Instrumental Variable Based Estimation Under the Semiparametric Accelerated Failure Time Model.

Biometrics 2018 Oct 25. Epub 2018 Oct 25.

Department of Biomedical Data Science and The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Dartmouth, Hanover, New Hampshire, USA.

Randomized controlled trials are the gold standard for estimating causal effects of treatments or interventions, but in many cases are too costly, too difficult, or even unethical to conduct. Hence, many pressing medical questions can only be investigated using observational studies. However, direct statistical modeling of observational data can result in biased estimates of treatment effects due to unmeasured confounding. Read More

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http://doi.wiley.com/10.1111/biom.12985
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http://dx.doi.org/10.1111/biom.12985DOI Listing
October 2018
10 Reads

Longitudinal and Time-to-Drop-out Joint Models Can Lead to Seriously Biased Estimates when the Drop-out Mechanism Is at Random.

Biometrics 2018 Oct 25. Epub 2018 Oct 25.

Department of Hygiene and Epidemiology, National and Kapodistrian University of Athens, Greece.

Missing data are common in longitudinal studies. Likelihood-based methods ignoring the missingness mechanism are unbiased provided missingness is at random (MAR); under not-at-random missingness (MNAR), joint modeling is commonly used, often as part of sensitivity analyses. In our motivating example of modeling CD4 count trajectories during untreated HIV infection, CD4 counts are mainly censored due to treatment initiation, with the nature of this mechanism remaining debatable. Read More

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http://dx.doi.org/10.1111/biom.12986DOI Listing
October 2018
1 Read

Informative Group Testing for Multiplex Assays.

Biometrics 2018 Oct 24. Epub 2018 Oct 24.

School of Mathematical and Statistical Sciences, Clemson University, Clemson, South Carolina 29634, USA.

Infectious disease testing frequently takes advantage of two tools-group testing and multiplex assays-to make testing timely and cost effective. Until the work of Tebbs et al. (2013) and Hou et al. Read More

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http://doi.wiley.com/10.1111/biom.12988
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http://dx.doi.org/10.1111/biom.12988DOI Listing
October 2018
18 Reads

Automated Feature Selection of Predictors in Electronic Medical Records Data.

Biometrics 2018 Oct 24. Epub 2018 Oct 24.

Department of Biostatistics, Harvard University, Boston, MA 02115, USA.

The use of Electronic Health Records (EHR) for translational research can be challenging due to difficulty in extracting accurate disease phenotype data. Historically, EHR algorithms for annotating phenotypes have been either rule-based or trained with billing codes and gold standard labels curated via labor intensive medical chart review. These simplistic algorithms tend to have unpredictable portability across institutions and low accuracy for many disease phenotypes due to imprecise billing codes. Read More

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http://dx.doi.org/10.1111/biom.12987DOI Listing
October 2018
1 Read

Diagonal Likelihood Ratio Test for Equality of Mean Vectors in High-Dimensional Data.

Biometrics 2018 Oct 16. Epub 2018 Oct 16.

Statistics Program, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.

We propose a likelihood ratio test framework for testing normal mean vectors in high-dimensional data under two common scenarios: the one-sample test and the two-sample test with equal covariance matrices. We derive the test statistics under the assumption that the covariance matrices follow a diagonal matrix structure. In comparison with the diagonal Hotelling's tests, our proposed test statistics display some interesting characteristics. Read More

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http://doi.wiley.com/10.1111/biom.12984
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http://dx.doi.org/10.1111/biom.12984DOI Listing
October 2018
6 Reads

Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations.

Biometrics 2018 Oct 8. Epub 2018 Oct 8.

Department of Statistics, University of Auckland, Auckland 1010, New Zealand.

Capture-recapture methods for estimating wildlife population sizes almost always require their users to identify every detected animal. Many modern-day wildlife surveys detect animals without physical capture-visual detection by cameras is one such example. However, for every pair of detections, the surveyor faces a decision that is often fraught with uncertainty: are they linked to the same individual? An inability to resolve every such decision to a high degree of certainty prevents the use of standard capture-recapture methods, impeding the estimation of animal density. Read More

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http://dx.doi.org/10.1111/biom.12983DOI Listing
October 2018
1 Read

Multi-study Factor Analysis.

Biometrics 2018 Oct 5. Epub 2018 Oct 5.

Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.

We introduce a novel class of factor analysis methodologies for the joint analysis of multiple studies. The goal is to separately identify and estimate 1) common factors shared across multiple studies, and 2) study-specific factors. We develop an Expectation Conditional-Maximization algorithm for parameter estimates and we provide a procedure for choosing the numbers of common and specific factors. Read More

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http://doi.wiley.com/10.1111/biom.12974
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http://dx.doi.org/10.1111/biom.12974DOI Listing
October 2018
3 Reads

A Bayesian multi-dimensional couple-based latent risk model with an application to infertility.

Biometrics 2018 Sep 29. Epub 2018 Sep 29.

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892.

Motivated by the Longitudinal Investigation of Fertility and the Environment (LIFE) Study that investigated the association between exposure to a large number of environmental pollutants and human reproductive outcomes, we propose a joint latent risk class modeling framework with an interaction between female and male partners of a couple. This formulation introduces a dependence structure between the chemical patterns within a couple and between the chemical patterns and the risk of infertility. The specification of an interaction enables the interplay between the female and male's chemical patterns on the risk of infertility in a parsimonious way. Read More

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http://dx.doi.org/10.1111/biom.12972DOI Listing
September 2018
2 Reads

Semiparametric Regression Analysis of Length-Biased Interval-Censored Data.

Biometrics 2018 Sep 29. Epub 2018 Sep 29.

Department of Biostatistics, University of Washington, Seattle, Washington, USA.

In prevalent cohort design, subjects who have experienced an initial event but not the failure event are preferentially enrolled and the observed failure times are often length-biased. Moreover, the prospective follow-up may not be continuously monitored and failure times are subject to interval censoring. We study the nonparametric maximum likelihood estimation for the proportional hazards model with length-biased interval-censored data. Read More

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http://dx.doi.org/10.1111/biom.12970DOI Listing
September 2018
1 Read