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    Bayesian enhancement two-stage design for single-arm phase II clinical trials with binary and time-to-event endpoints.
    Biometrics 2018 Feb 21. Epub 2018 Feb 21.
    Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong.
    Simon's two-stage design is one of the most commonly used methods in phase II clinical trials with binary endpoints. The design tests the null hypothesis that the response rate is less than an uninteresting level, versus the alternative hypothesis that the response rate is greater than a desirable target level. From a Bayesian perspective, we compute the posterior probabilities of the null and alternative hypotheses given that a promising result is declared in Simon's design. Read More

    A wild bootstrap approach for the Aalen-Johansen estimator.
    Biometrics 2018 Feb 16. Epub 2018 Feb 16.
    Institute of Statistics, Ulm University, Ulm, Germany.
    We suggest a wild bootstrap resampling technique for nonparametric inference on transition probabilities in a general time-inhomogeneous Markov multistate model. We first approximate the limiting distribution of the Nelson-Aalen estimator by repeatedly generating standard normal wild bootstrap variates, while the data is kept fixed. Next, a transformation using a functional delta method argument is applied. Read More

    Toward a diagnostic toolkit for linear models with Gaussian-process distributed random effects.
    Biometrics 2018 Feb 13. Epub 2018 Feb 13.
    Department of Biostatistics, University of California, Los Angeles, California 90095, U.S.A.
    Gaussian processes (GPs) are widely used as distributions of random effects in linear mixed models, which are fit using the restricted likelihood or the closely related Bayesian analysis. This article addresses two problems. First, we propose tools for understanding how data determine estimates in these models, using a spectral basis approximation to the GP under which the restricted likelihood is formally identical to the likelihood for a gamma-errors GLM with identity link. Read More

    An alternative robust estimator of average treatment effect in causal inference.
    Biometrics 2018 Feb 13. Epub 2018 Feb 13.
    School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455, U.S.A.
    The problem of estimating the average treatment effects is important when evaluating the effectiveness of medical treatments or social intervention policies. Most of the existing methods for estimating the average treatment effect rely on some parametric assumptions about the propensity score model or the outcome regression model one way or the other. In reality, both models are prone to misspecification, which can have undue influence on the estimated average treatment effect. Read More

    Methods for multivariate recurrent event data with measurement error and informative censoring.
    Biometrics 2018 Feb 13. Epub 2018 Feb 13.
    Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.
    In multivariate recurrent event data regression, observation of recurrent events is usually terminated by other events that are associated with the recurrent event processes, resulting in informative censoring. Additionally, some covariates could be measured with errors. In some applications, an instrumental variable is observed in a subsample, namely a calibration sample, which can be applied for bias correction. Read More

    Generalized accelerated recurrence time model for multivariate recurrent event data with missing event type.
    Biometrics 2018 Feb 9. Epub 2018 Feb 9.
    Departments of Nutritional Sciences, University of Wisconsin-Madison, Madison, Wisconsin 53706, U.S.A.
    Recurrent events data are frequently encountered in biomedical follow-up studies. The generalized accelerated recurrence time (GART) model (Sun et al., 2016), which formulates covariate effects on the time scale of the mean function of recurrent events (i. Read More

    A multi-source adaptive platform design for testing sequential combinatorial therapeutic strategies.
    Biometrics 2018 Jan 22. Epub 2018 Jan 22.
    Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, U.S.A.
    Traditional paradigms for clinical translation are challenged in settings where multiple contemporaneous therapeutic strategies have been identified as potentially beneficial. Platform trials have emerged as an approach for sequentially comparing multiple trials using a single protocol. The Ebola virus disease outbreak in West Africa represents one recent example which utilized a platform design. Read More

    A scalable multi-resolution spatio-temporal model for brain activation and connectivity in fMRI data.
    Biometrics 2018 Jan 22. Epub 2018 Jan 22.
    Statistics Program, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
    Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different spatial scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data (on the order of hundreds of thousands of voxels) poses serious modeling challenges and considerable computational constraints. Read More

    Subtype classification and heterogeneous prognosis model construction in precision medicine.
    Biometrics 2018 Jan 22. Epub 2018 Jan 22.
    Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut 06511, U.S.A.
    Common diseases including cancer are heterogeneous. It is important to discover disease subtypes and identify both shared and unique risk factors for different disease subtypes. The advent of high-throughput technologies enriches the data to achieve this goal, if necessary statistical methods are developed. Read More

    Modeling survival distribution as a function of time to treatment discontinuation: A dynamic treatment regime approach.
    Biometrics 2018 Jan 22. Epub 2018 Jan 22.
    Duke University Medical Center, University in Durham, North Carolina, U.S.A.
    We consider estimating the effect that discontinuing a beneficial treatment will have on the distribution of a time to event clinical outcome, and in particular assessing whether there is a period of time over which the beneficial effect may continue after discontinuation. There are two major challenges. The first is to make a distinction between mandatory discontinuation, where by necessity treatment has to be terminated and optional discontinuation which is decided by the preference of the patient or physician. Read More

    A utility-based design for randomized comparative trials with ordinal outcomes and prognostic subgroups.
    Biometrics 2018 Jan 22. Epub 2018 Jan 22.
    Department of Thoracic and Cardiovascular Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A.
    A design is proposed for randomized comparative trials with ordinal outcomes and prognostic subgroups. The design accounts for patient heterogeneity by allowing possibly different comparative conclusions within subgroups. The comparative testing criterion is based on utilities for the levels of the ordinal outcome and a Bayesian probability model. Read More

    Regression analysis for secondary response variable in a case-cohort study.
    Biometrics 2017 Dec 29. Epub 2017 Dec 29.
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
    Case-cohort study design has been widely used for its cost-effectiveness. In any real study, there are always other important outcomes of interest beside the failure time that the original case-cohort study is based on. How to utilize the available case-cohort data to study the relationship of a secondary outcome with the primary exposure obtained through the case-cohort study is not well studied. Read More

    Semiparametric estimation of the accelerated mean model with panel count data under informative examination times.
    Biometrics 2017 Dec 29. Epub 2017 Dec 29.
    Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California 94158, U.S.A.
    Panel count data arise when the number of recurrent events experienced by each subject is observed intermittently at discrete examination times. The examination time process can be informative about the underlying recurrent event process even after conditioning on covariates. We consider a semiparametric accelerated mean model for the recurrent event process and allow the two processes to be correlated through a shared frailty. Read More

    General single-index survival regression models for incident and prevalent covariate data and prevalent data without follow-up.
    Biometrics 2017 Dec 21. Epub 2017 Dec 21.
    Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan.
    This article mainly focuses on analyzing covariate data from incident and prevalent cohort studies and a prevalent sample with only baseline covariates of interest and truncation times. Our major task in both research streams is to identify the effects of covariates on a failure time through very general single-index survival regression models without observing survival outcomes. With a strict increase of the survival function in the linear predictor, the ratio of incident and prevalent covariate densities is shown to be a non-degenerate and monotonic function of the linear predictor under covariate-independent truncation. Read More

    Detecting treatment differences in group sequential longitudinal studies with covariate adjustment.
    Biometrics 2017 Dec 18. Epub 2017 Dec 18.
    Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland 20892, U.S.A.
    In longitudinal studies comparing two treatments over a series of common follow-up measurements, there may be interest in determining if there is a treatment difference at any follow-up period when there may be a non-monotone treatment effect over time. To evaluate this question, Jeffries and Geller (2015) examined a number of clinical trial designs that allowed adaptive choice of the follow-up time exhibiting the greatest evidence of treatment difference in a group sequential testing setting with Gaussian data. The methods are applicable when a few measurements were taken at prespecified follow-up periods. Read More

    Sieve analysis using the number of infecting pathogens.
    Biometrics 2017 Dec 14. Epub 2017 Dec 14.
    Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, 550 N. Broadway, Baltimore, Maryland 21205, U.S.A.
    Assessment of vaccine efficacy as a function of the similarity of the infecting pathogen to the vaccine is an important scientific goal. Characterization of pathogen strains for which vaccine efficacy is low can increase understanding of the vaccine's mechanism of action and offer targets for vaccine improvement. Traditional sieve analysis estimates differential vaccine efficacy using a single identifiable pathogen for each subject. Read More

    C-learning: A new classification framework to estimate optimal dynamic treatment regimes.
    Biometrics 2017 Dec 11. Epub 2017 Dec 11.
    Department of Biostatistics, University of Michigan, Ann Arbor, U.S.A.
    A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error. Read More

    Dynamic borrowing through empirical power priors that control type I error.
    Biometrics 2017 Dec 11. Epub 2017 Dec 11.
    Department of Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands.
    In order for historical data to be considered for inclusion in the design and analysis of clinical trials, prospective rules are essential. Incorporation of historical data may be of particular interest in the case of small populations where available data is scarce and heterogeneity is not as well understood, and thus conventional methods for evidence synthesis might fall short. The concept of power priors can be particularly useful for borrowing evidence from a single historical study. Read More

    New semiparametric method for predicting high-cost patients.
    Biometrics 2017 Dec 11. Epub 2017 Dec 11.
    School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455, U.S.A.
    Motivated by the Medical Expenditure Panel Survey containing data from individuals' medical providers and employers across the United States, we propose a new semiparametric procedure for predicting whether a patient will incur high medical expenditure. Problems of the same nature arise in many other important applications where one would like to predict if a future response occurs at the upper (or lower) tail of the response distribution. The common practice is to artificially dichotomize the response variable and then apply an existing classification method such as binomial regression or a classification tree. Read More

    A regression framework for assessing covariate effects on the reproducibility of high-throughput experiments.
    Biometrics 2017 Nov 29. Epub 2017 Nov 29.
    Department of Statistics, Pennsylvania State University, University Park, Pennsylvania 16802, U.S.A.
    The outcome of high-throughput biological experiments is affected by many operational factors in the experimental and data-analytical procedures. Understanding how these factors affect the reproducibility of the outcome is critical for establishing workflows that produce replicable discoveries. In this article, we propose a regression framework, based on a novel cumulative link model, to assess the covariate effects of operational factors on the reproducibility of findings from high-throughput experiments. Read More

    Reader reaction on the fast small-sample kernel independence test for microbiome community-level association analysis.
    Biometrics 2017 Nov 29. Epub 2017 Nov 29.
    Division of Biostatistics, School of Public Health University of Minnesota, Minneapolis, Minnesota, U.S.A.
    Zhan et al. () presented a kernel RV coefficient (KRV) test to evaluate the overall association between host gene expression and microbiome composition, and showed its competitive performance compared to existing methods. In this article, we clarify the close relation of KRV to the existing generalized RV (GRV) coefficient, and show that KRV and GRV have very similar performance. Read More

    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

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