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    Discussion of "quantifying publication bias in meta-analysis" by Liu et al.
    Biometrics 2017 Nov 15. Epub 2017 Nov 15.
    Department of Biostatistics, Brown University, Providence, Rhode Island, U.S.A.
    Inspection and analysis of funnel plots cannot reliably identify publication and reporting bias, the non-publication of results that are not statistically significant. Instead, researchers should thoroughly and systematically search available information sources such as databases, registries and unpublished reports. Even then, it is not possible to ever know whether a systematic review has uncovered all available studies, but the search can inform attempts to construct plausible statistical models of the missing data mechanism. Read More

    Quantifying publication bias in meta-analysis.
    Biometrics 2017 Nov 15. Epub 2017 Nov 15.
    Division of Biostatistics, University of Minnesota, Minneapolis 55455, Minnesota, U.S.A.
    Publication bias is a serious problem in systematic reviews and meta-analyses, which can affect the validity and generalization of conclusions. Currently, approaches to dealing with publication bias can be distinguished into two classes: selection models and funnel-plot-based methods. Selection models use weight functions to adjust the overall effect size estimate and are usually employed as sensitivity analyses to assess the potential impact of publication bias. Read More

    Covariate-adjusted Spearman's rank correlation with probability-scale residuals.
    Biometrics 2017 Nov 13. Epub 2017 Nov 13.
    Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, U.S.A.
    It is desirable to adjust Spearman's rank correlation for covariates, yet existing approaches have limitations. For example, the traditionally defined partial Spearman's correlation does not have a sensible population parameter, and the conditional Spearman's correlation defined with copulas cannot be easily generalized to discrete variables. We define population parameters for both partial and conditional Spearman's correlation through concordance-discordance probabilities. Read More

    Integrated powered density: Screening ultrahigh dimensional covariates with survival outcomes.
    Biometrics 2017 Nov 9. Epub 2017 Nov 9.
    Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
    Modern biomedical studies have yielded abundant survival data with high-throughput predictors. Variable screening is a crucial first step in analyzing such data, for the purpose of identifying predictive biomarkers, understanding biological mechanisms, and making accurate predictions. To nonparametrically quantify the relevance of each candidate variable to the survival outcome, we propose integrated powered density (IPOD), which compares the differences in the covariate-stratified distribution functions. Read More

    Testing for gene-environment interaction under exposure misspecification.
    Biometrics 2017 Nov 9. Epub 2017 Nov 9.
    Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, U.S.A.
    Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. Read More

    Reader Reaction: A note on testing and estimation in marker-set association study using semiparametric quantile regression kernel machine.
    Biometrics 2017 Nov 2. Epub 2017 Nov 2.
    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A.
    Kong et al. (2016, Biometrics 72, 364-371) presented a quantile regression kernel machine (QRKM) test for robust analysis of genetic marker-set association studies. A potential limitation of QRKM is the permutation-based test design may be unscalable for the massive sizes of modern datasets. Read More

    Data-driven confounder selection via Markov and Bayesian networks.
    Biometrics 2017 Nov 2. Epub 2017 Nov 2.
    Department of Statistics, USBE, Umeå University, SE-901 87 Umeå, Sweden.
    To unbiasedly estimate a causal effect on an outcome unconfoundedness is often assumed. If there is sufficient knowledge on the underlying causal structure then existing confounder selection criteria can be used to select subsets of the observed pretreatment covariates, X, sufficient for unconfoundedness, if such subsets exist. Here, estimation of these target subsets is considered when the underlying causal structure is unknown. Read More

    Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using P-splines.
    Biometrics 2017 Nov 1. Epub 2017 Nov 1.
    Department of Biostatistics, Erasmus MC, Rotterdam, The Netherlands.
    In the field of cardio-thoracic surgery, valve function is monitored over time after surgery. The motivation for our research comes from a study which includes patients who received a human tissue valve in the aortic position. These patients are followed prospectively over time by standardized echocardiographic assessment of valve function. Read More

    Optimal treatment assignment to maximize expected outcome with multiple treatments.
    Biometrics 2017 Oct 31. Epub 2017 Oct 31.
    Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, U.S.A.
    When there is substantial heterogeneity of treatment effectiveness, it is crucial to identify individualized treatment assignment rules for comparative treatment selection. Traditional approaches directly model clinical outcome and define optimal treatment rule according to the interactions between treatment and covariates. This approach relies on the success of separating the main effects from the covariate-treatment interaction effects, which may not be easy. Read More

    Statistical inference in a growth curve quantile regression model for longitudinal data.
    Biometrics 2017 Oct 31. Epub 2017 Oct 31.
    Department of Biostatistics, University of Iowa, Iowa City, Iowa 52242, U.S.A.
    This article describes a polynomial growth curve quantile regression model that provides a comprehensive assessment about the treatment effects on the changes of the distribution of outcomes over time. The proposed model has the flexibility, as it allows the degree of a polynomial to vary across quantiles. A high degree polynomial model fits the data adequately, yet it is not desirable due to the complexity of the model. Read More

    Modeling associations between latent event processes governing time series of pulsing hormones.
    Biometrics 2017 Oct 31. Epub 2017 Oct 31.
    Department of Obstetrics and Gynecology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, U.S.A.
    This work is motivated by a desire to quantify relationships between two time series of pulsing hormone concentrations. The locations of pulses are not directly observed and may be considered latent event processes. The latent event processes of pulsing hormones are often associated. Read More

    Cox regression model with doubly truncated data.
    Biometrics 2017 Oct 26. Epub 2017 Oct 26.
    Department of Biostatistics and Epidemiology, University of Pennsylvania, 607 Blockley Hall, 423 Guardian Drive, Philadelphia, Philadelphia 19104, U.S.A.
    Truncation is a well-known phenomenon that may be present in observational studies of time-to-event data. While many methods exist to adjust for either left or right truncation, there are very few methods that adjust for simultaneous left and right truncation, also known as double truncation. We propose a Cox regression model to adjust for this double truncation using a weighted estimating equation approach, where the weights are estimated from the data both parametrically and nonparametrically, and are inversely proportional to the probability that a subject is observed. Read More

    Estimation of cis-eQTL effect sizes using a log of linear model.
    Biometrics 2017 Oct 26. Epub 2017 Oct 26.
    Bioinformatics Research Center and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, U.S.A.
    The study of expression Quantitative Trait Loci (eQTL) is an important problem in genomics and biomedicine. While detection (testing) of eQTL associations has been widely studied, less work has been devoted to the estimation of eQTL effect size. To reduce false positives, detection methods frequently rely on linear modeling of rank-based normalized or log-transformed gene expression data. Read More

    Risk prediction for heterogeneous populations with application to hospital admission prediction.
    Biometrics 2017 Oct 26. Epub 2017 Oct 26.
    Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin 53792, U.S.A.
    This article is motivated by the increasing need to model risk for large hospital and health care systems that provide services to diverse and complex patients. Often, heterogeneity across a population is determined by a set of factors such as chronic conditions. When these stratifying factors result in overlapping subpopulations, it is likely that the covariate effects for the overlapping groups have some similarity. Read More

    A two-stage model for wearable device data.
    Biometrics 2017 Oct 10. Epub 2017 Oct 10.
    Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205, U.S.A.
    Recent advances of wearable computing technology have allowed continuous health monitoring in large observational studies and clinical trials. Examples of data collected by wearable devices include minute-by-minute physical activity proxies measured by accelerometers or heart rate. The analysis of data generated by wearable devices has so far been quite limited to crude summaries, for example, the mean activity count over the day. Read More

    Heterogeneous reciprocal graphical models.
    Biometrics 2017 Oct 10. Epub 2017 Oct 10.
    Program for Computational Genomics and Medicine, NorthShore University HealthSystem, Illinois, U.S.A.
    We develop novel hierarchical reciprocal graphical models to infer gene networks from heterogeneous data. In the case of data that can be naturally divided into known groups, we propose to connect graphs by introducing a hierarchical prior across group-specific graphs, including a correlation on edge strengths across graphs. Thresholding priors are applied to induce sparsity of the estimated networks. Read More

    Bayesian variable selection for multistate Markov models with interval-censored data in an ecological momentary assessment study of smoking cessation.
    Biometrics 2017 Oct 11. Epub 2017 Oct 11.
    Department of Family and Preventive Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, U.S.A.
    The application of sophisticated analytical methods to intensive longitudinal data, collected with ecological momentary assessments (EMA), has helped researchers better understand smoking behaviors after a quit attempt. Unfortunately, the wealth of information captured with EMAs is typically underutilized in practice. Thus, novel methods are needed to extract this information in exploratory research studies. Read More

    A GLM-based latent variable ordination method for microbiome samples.
    Biometrics 2017 Oct 9. Epub 2017 Oct 9.
    Department of Biostatistics and Epidemiology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania 19104, U.S.A.
    Distance-based ordination methods, such as principal coordinates analysis (PCoA), are widely used in the analysis of microbiome data. However, these methods are prone to pose a potential risk of misinterpretation about the compositional difference in samples across different populations if there is a difference in dispersion effects. Accounting for high sparsity and overdispersion of microbiome data, we propose a GLM-based Ordination Method for Microbiome Samples (GOMMS) in this article. Read More

    Regularity of a renewal process estimated from binary data.
    Biometrics 2017 Oct 9. Epub 2017 Oct 9.
    Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, U.S.A.
    Assessment of the regularity of a sequence of events over time is important for clinical decision-making as well as informing public health policy. Our motivating example involves determining the effect of an intervention on the regularity of HIV self-testing behavior among high-risk individuals when exact self-testing times are not recorded. Assuming that these unobserved testing times follow a renewal process, the goals of this work are to develop suitable methods for estimating its distributional parameters when only the presence or absence of at least one event per subject in each of several observation windows is recorded. Read More

    Clustering distributions with the marginalized nested Dirichlet process.
    Biometrics 2017 Sep 28. Epub 2017 Sep 28.
    NorthShore University HealthSystem, Evanston and University of Chicago, U.S.A.
    We introduce a marginal version of the nested Dirichlet process to cluster distributions or histograms. We apply the model to cluster genes by patterns of gene-gene interaction. The proposed approach is based on the nested partition that is implied in the original construction of the nested Dirichlet process. Read More

    Fully Bayesian spectral methods for imaging data.
    Biometrics 2017 Sep 28. Epub 2017 Sep 28.
    North Carolina State University, Raleigh, North Carolina, U.S.A.
    Medical imaging data with thousands of spatially correlated data points are common in many fields. Methods that account for spatial correlation often require cumbersome matrix evaluations which are prohibitive for data of this size, and thus current work has either used low-rank approximations or analyzed data in blocks. We propose a method that accounts for nonstationarity, functional connectivity of distant regions of interest, and local signals, and can be applied to large multi-subject datasets using spectral methods combined with Markov Chain Monte Carlo sampling. Read More

    Case-only approach to identifying markers predicting treatment effects on the relative risk scale.
    Biometrics 2017 Sep 28. Epub 2017 Sep 28.
    Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A.
    Retrospectively measuring markers on stored baseline samples from participants in a randomized controlled trial (RCT) may provide high quality evidence as to the value of the markers for treatment selection. Originally developed for approximating gene-environment interactions in the odds ratio scale, the case-only method has recently been advocated for assessing gene-treatment interactions on rare disease endpoints in randomized clinical trials. In this article, the case-only approach is shown to provide a consistent and efficient estimator of marker by treatment interactions and marker-specific treatment effects on the relative risk scale. Read More

    Modeling the causal effect of treatment initiation time on survival: Application to HIV/TB co-infection.
    Biometrics 2017 Sep 28. Epub 2017 Sep 28.
    Moi University School of Medicine, Eldoret 30100, Kenya.
    The timing of antiretroviral therapy (ART) initiation for HIV and tuberculosis (TB) co-infected patients needs to be considered carefully. CD4 cell count can be used to guide decision making about when to initiate ART. Evidence from recent randomized trials and observational studies generally supports early initiation but does not provide information about effects of initiation time on a continuous scale. Read More

    Estimation and evaluation of linear individualized treatment rules to guarantee performance.
    Biometrics 2017 Sep 28. Epub 2017 Sep 28.
    Department of Biostatistics, Columbia University, New York, NY, U.S.A.
    In clinical practice, an informative and practically useful treatment rule should be simple and transparent. However, because simple rules are likely to be far from optimal, effective methods to construct such rules must guarantee performance, in terms of yielding the best clinical outcome (highest reward) among the class of simple rules under consideration. Furthermore, it is important to evaluate the benefit of the derived rules on the whole sample and in pre-specified subgroups (e. Read More

    Experimental design for multi-drug combination studies using signaling networks.
    Biometrics 2017 Sep 28. Epub 2017 Sep 28.
    Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington, DC 20057, U.S.A.
    Combinations of multiple drugs are an important approach to maximize the chance for therapeutic success by inhibiting multiple pathways/targets. Analytic methods for studying drug combinations have received increasing attention because major advances in biomedical research have made available large number of potential agents for testing. The preclinical experiment on multi-drug combinations plays a key role in (especially cancer) drug development because of the complex nature of the disease, the need to reduce development time and costs. Read More

    Adaptive designs for the one-sample log-rank test.
    Biometrics 2017 Sep 22. Epub 2017 Sep 22.
    Institute of Biostatistics and Clinical Research, University of Muenster, 48149 Muenster, Germany.
    Traditional designs in phase IIa cancer trials are single-arm designs with a binary outcome, for example, tumor response. In some settings, however, a time-to-event endpoint might appear more appropriate, particularly in the presence of loss to follow-up. Then the one-sample log-rank test might be the method of choice. Read More

    Empirical null estimation using zero-inflated discrete mixture distributions and its application to protein domain data.
    Biometrics 2017 Sep 22. Epub 2017 Sep 22.
    National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, U.S.A.
    In recent mutation studies, analyses based on protein domain positions are gaining popularity over gene-centric approaches since the latter have limitations in considering the functional context that the position of the mutation provides. This presents a large-scale simultaneous inference problem, with hundreds of hypothesis tests to consider at the same time. This article aims to select significant mutation counts while controlling a given level of Type I error via False Discovery Rate (FDR) procedures. Read More

    Monte Carlo methods for nonparametric regression with heteroscedastic measurement error.
    Biometrics 2017 Sep 15. Epub 2017 Sep 15.
    Department of Environmental Health Sciences, University of California, Berkeley, California 94720, U.S.A.
    Nonparametric regression is a fundamental problem in statistics but challenging when the independent variable is measured with error. Among the first approaches was an extension of deconvoluting kernel density estimators for homescedastic measurement error. The main contribution of this article is to propose a new simulation-based nonparametric regression estimator for the heteroscedastic measurement error case. Read More

    A semiparametric likelihood-based method for regression analysis of mixed panel-count data.
    Biometrics 2017 Sep 15. Epub 2017 Sep 15.
    Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, U.S.A.
    Panel-count data arise when each study subject is observed only at discrete time points in a recurrent event study, and only the numbers of the event of interest between observation time points are recorded (Sun and Zhao, 2013). However, sometimes the exact number of events between some observation times is unknown and what we know is only whether the event of interest has occurred. In this article, we will refer this type of data to as mixed panel-count data and propose a likelihood-based semiparametric regression method for their analysis by using the nonhomogeneous Poisson process assumption. Read More

    Efficiency of two sample tests via the restricted mean survival time for analyzing event time observations.
    Biometrics 2017 Sep 12. Epub 2017 Sep 12.
    Department of Biostatistics, Harvard University, Boston, Massachusetts 02115, U.S.A.
    In comparing two treatments with the event time observations, the hazard ratio (HR) estimate is routinely used to quantify the treatment difference. However, this model dependent estimate may be difficult to interpret clinically especially when the proportional hazards (PH) assumption is violated. An alternative estimation procedure for treatment efficacy based on the restricted means survival time or t-year mean survival time (t-MST) has been discussed extensively in the statistical and clinical literature. Read More

    Sample size determination for multilevel hierarchical designs using generalized linear mixed models.
    Biometrics 2017 Sep 12. Epub 2017 Sep 12.
    Division of Epidemiology and Biostatistics, Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois 60612, U.S.A.
    A unified statistical methodology of sample size determination is developed for hierarchical designs that are frequently used in many areas, particularly in medical and health research studies. The solid foundation of the proposed methodology opens a new horizon for power analysis in presence of various conditions. Important features such as joint significance testing, unequal allocations of clusters across intervention groups, and differential attrition rates over follow up time points are integrated to address some useful questions that investigators often encounter while conducting such studies. Read More

    Continuous-time capture-recapture in closed populations.
    Biometrics 2017 Sep 12. Epub 2017 Sep 12.
    Department of Mathematics and Statistics, University of Otago, New Zealand.
    The standard approach to fitting capture-recapture data collected in continuous time involves arbitrarily forcing the data into a series of distinct discrete capture sessions. We show how continuous-time models can be fitted as easily as discrete-time alternatives. The likelihood is factored so that efficient Markov chain Monte Carlo algorithms can be implemented for Bayesian estimation, available online in the R package ctime. Read More

    Analysis of restricted mean survival time for length-biased data.
    Biometrics 2017 Sep 8. Epub 2017 Sep 8.
    Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A.
    In clinical studies with time-to-event outcomes, the restricted mean survival time (RMST) has attracted substantial attention as a summary measurement for its straightforward clinical interpretation. When the data are subject to length-biased sampling, which is frequently encountered in observational cohort studies, existing methods to estimate the RMST are not applicable. In this article, we consider nonparametric and semiparametric regression methods to estimate the RMST under the setting of length-biased sampling. Read More

    Inverse probability weighted Cox regression for doubly truncated data.
    Biometrics 2017 Sep 8. Epub 2017 Sep 8.
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, U.S.A.
    Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. Read More

    FLCRM: Functional linear cox regression model.
    Biometrics 2017 Sep 1. Epub 2017 Sep 1.
    Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Texas, U.S.A.
    We consider a functional linear Cox regression model for characterizing the association between time-to-event data and a set of functional and scalar predictors. The functional linear Cox regression model incorporates a functional principal component analysis for modeling the functional predictors and a high-dimensional Cox regression model to characterize the joint effects of both functional and scalar predictors on the time-to-event data. We develop an algorithm to calculate the maximum approximate partial likelihood estimates of unknown finite and infinite dimensional parameters. Read More

    A pairwise likelihood augmented Cox estimator for left-truncated data.
    Biometrics 2017 Aug 29. Epub 2017 Aug 29.
    Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.
    Survival data collected from a prevalent cohort are subject to left truncation and the analysis is challenging. Conditional approaches for left-truncated data could be inefficient as they ignore the information in the marginal likelihood of the truncation times. Length-biased sampling methods may improve the estimation efficiency but only when the underlying truncation time is uniform; otherwise, they may generate biased estimates. Read More

    Eigenvalue significance testing for genetic association.
    Biometrics 2017 Aug 29. Epub 2017 Aug 29.
    Bioinformatics Research Center and Departments of Statistics and Biological Sciences, North Carolina State University, North Carolina, U.S.A.
    Genotype eigenvectors are widely used as covariates for control of spurious stratification in genetic association. Significance testing for the accompanying eigenvalues has typically been based on a standard Tracy-Widom limiting distribution for the largest eigenvalue, derived under white-noise assumptions. It is known that even modest local correlation among markers inflates the largest eigenvalues, even in the absence of true stratification. Read More

    Spatial capture-mark-resight estimation of animal population density.
    Biometrics 2017 Aug 23. Epub 2017 Aug 23.
    Department of Conservation, Private Bag 5, Nelson 7042, New Zealand.
    Sightings of previously marked animals can extend a capture-recapture dataset without the added cost of capturing new animals for marking. Combined marking and resighting methods are therefore an attractive option in animal population studies, and there exist various likelihood-based non-spatial models, and some spatial versions fitted by Markov chain Monte Carlo sampling. As implemented to date, the focus has been on modeling sightings only, which requires that the spatial distribution of pre-marked animals is known. Read More

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