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Matthew Stephens
SummaryWe introduce a new Empirical Bayes approach for large-scale hypothesis testing, including estimating false discovery rates (FDRs), and effect sizes. This approach has two key differences from existing approaches to FDR analysis. First, it assumes that the distribution of the actual (unobserved) effects is unimodal, with a mode at 0. This "unimodal assumption" (UA), although natural in many contexts, is not usually incorporated into standard FDR analysis, and we demonstrate how incorporating it brings many benefits...
October 17, 2016: Biostatistics
Glenn Heller, Venkatraman E Seshan, Chaya S Moskowitz, Mithat Gönen
SummaryThe area under the curve (AUC) statistic is a common measure of model performance in a binary regression model. Nested models are used to ascertain whether the AUC statistic increases when new factors enter the model. The regression coefficient estimates used in the AUC statistics are computed using the maximum rank correlation methodology. Typically, inference for the difference in AUC statistics from nested models is derived under asymptotic normality. In this work, it is demonstrated that the asymptotic normality is true only when at least one of the new factors is associated with the binary outcome...
September 21, 2016: Biostatistics
Emilio Carrizosa, Alba V Olivares-Nadal, Pepa Ramírez-Cobo
SummaryVector autoregressive (VAR) models constitute a powerful and well studied tool to analyze multivariate time series. Since sparseness, crucial to identify and visualize joint dependencies and relevant causalities, is not expected to happen in the standard VAR model, several sparse variants have been introduced in the literature. However, in some cases it might be of interest to control some dimensions of the sparsity, as e.g. the number of causal features allowed in the prediction. To authors extent none of the existent methods endows the user with full control over the different aspects of the sparsity of the solution...
September 21, 2016: Biostatistics
Ying Huang, Peter B Gilbert, Rong Fu, Holly Janes
SummaryBiomarker endpoints measuring vaccine-induced immune responses are essential to HIV vaccine development because of their potential to predict the effect of a vaccine in preventing HIV infection. A vaccine's immune response profile observed in phase I immunogenicity studies is a key factor in determining whether it is advanced for further study in phase II and III efficacy trials. The multiplicity of immune variables and scientific uncertainty in their relative importance, however, pose great challenges to the development of formal algorithms for selecting vaccines to study further...
September 20, 2016: Biostatistics
Lei Huang, Philip T Reiss, Luo Xiao, Vadim Zipunnikov, Martin A Lindquist, Ciprian M Crainiceanu
SummaryMany modern neuroimaging studies acquire large spatial images of the brain observed sequentially over time. Such data are often stored in the forms of matrices. To model these matrix-variate data we introduce a class of separable processes using explicit latent process modeling. To account for the size and two-way structure of the data, we extend principal component analysis to achieve dimensionality reduction at the individual level. We introduce necessary identifiability conditions for each model and develop scalable estimation procedures...
August 29, 2016: Biostatistics
Sunghwan Kim, Steffi Oesterreich, Seyoung Kim, Yongseok Park, George C Tseng
SummaryWith the rapid advances in technologies of microarray and massively parallel sequencing, data of multiple omics sources from a large patient cohort are now frequently seen in many consortium studies. Effective multi-level omics data integration has brought new statistical challenges. One important biological objective of such integrative analysis is to cluster patients in order to identify clinically relevant disease subtypes, which will form basis for tailored treatment and personalized medicine. Several methods have been proposed in the literature for this purpose, including the popular iCluster method used in many cancer applications...
August 22, 2016: Biostatistics
Ruitao Lin, Guosheng Yin
Under the framework of Bayesian model selection, we propose a nonparametric overdose control (NOC) design for dose finding in phase I clinical trials. Each dose assignment is guided via a feasibility bound, which thereby can control the number of patients allocated to excessively toxic dose levels. Several aspects of the NOC design are explored, including the coherence property in dose assignment, calibration of design parameters, and selection of the maximum tolerated dose (MTD). We further propose a fractional NOC (fNOC) design in conjunction with a so-called fractional imputation approach, to account for late-onset toxicity outcomes...
August 22, 2016: Biostatistics
Zhenke Wu, Maria Deloria-Knoll, Scott L Zeger
The Pneumonia Etiology Research for Child Health (PERCH) study seeks to use modern measurement technology to infer the causes of pneumonia for which gold-standard evidence is unavailable. Based on case-control data, the article describes a latent variable model designed to infer the etiology distribution for the population of cases, and for an individual case given her measurements. We assume each observation is drawn from a mixture model for which each component represents one disease class. The model conisidered here addresses a major limitation of the traditional latent class approach by taking account of residual dependence among multivariate binary outcomes given disease class, hence reducing estimation bias, retaining efficiency and offering more valid inference...
August 22, 2016: Biostatistics
Jean Morrison, Noah Simon, Daniela Witten
SummaryGenomic phenotypes, such as DNA methylation and chromatin accessibility, can be used to characterize the transcriptional and regulatory activity of DNA within a cell. Recent technological advances have made it possible to measure such phenotypes very densely. This density often results in spatial structure, in the sense that measurements at nearby sites are very similar. In this article, we consider the task of comparing genomic phenotypes across experimental conditions, cell types, or disease subgroups...
August 5, 2016: Biostatistics
Malka Gorfine, Yair Goldberg, Ya'acov Ritov
SummarySince survival data occur over time, often important covariates that we wish to consider also change over time. Such covariates are referred as time-dependent covariates. Quantile regression offers flexible modeling of survival data by allowing the covariates to vary with quantiles. This article provides a novel quantile regression model accommodating time-dependent covariates, for analyzing survival data subject to right censoring. Our simple estimation technique assumes the existence of instrumental variables...
August 2, 2016: Biostatistics
Eugen Pircalabelu, Gerda Claeskens, Lourens J Waldorp
SummaryWe have developed a method for estimating brain networks from fMRI datasets that have not all been measured using the same set of brain regions. Some of the coarse scale regions have been split in smaller subregions. The proposed penalized estimation procedure selects undirected graphical models with similar structures that combine information from several subjects and several coarseness scales. Both within-scale edges and between-scale edges that identify possible connections between a large region and its subregions are estimated...
October 2016: Biostatistics
Joseph Antonelli, Matthew Cefalu, Luke Bornn
SummaryIn environmental epidemiology, exposures are not always available at subject locations and must be predicted using monitoring data. The monitor locations are often outside the control of researchers, and previous studies have shown that "preferential sampling" of monitoring locations can adversely affect exposure prediction and subsequent health effect estimation. We adopt a slightly different definition of preferential sampling than is typically seen in the literature, which we call population-based preferential sampling...
October 2016: Biostatistics
Gianluca Frasso, Philippe Lambert
SummaryThe 2014 Ebola outbreak in Sierra Leone is analyzed using a susceptible-exposed-infectious-removed (SEIR) epidemic compartmental model. The discrete time-stochastic model for the epidemic evolution is coupled to a set of ordinary differential equations describing the dynamics of the expected proportions of subjects in each epidemic state. The unknown parameters are estimated in a Bayesian framework by combining data on the number of new (laboratory confirmed) Ebola cases reported by the Ministry of Health and prior distributions for the transition rates elicited using information collected by the WHO during the follow-up of specific Ebola cases...
October 2016: Biostatistics
Jonathan W Bartlett, Jeremy M G Taylor
Studies often follow individuals until they fail from one of a number of competing failure types. One approach to analyzing such competing risks data involves modeling the cause-specific hazards as functions of baseline covariates. A common issue that arises in this context is missing values in covariates. In this setting, we first establish conditions under which complete case analysis (CCA) is valid. We then consider application of multiple imputation to handle missing covariate values, and extend the recently proposed substantive model compatible version of fully conditional specification (SMC-FCS) imputation to the competing risks setting...
October 2016: Biostatistics
Marie-Karelle Riviere, Sebastian Ueckert, France Mentré
Non-linear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal design based on the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. In recent years, estimation algorithms for NLMEMs have transitioned from linearization toward more exact higher-order methods. Optimal design, on the other hand, has mainly relied on first-order (FO) linearization to calculate the FIM. Although efficient in general, FO cannot be applied to complex non-linear models and with difficulty in studies with discrete data...
October 2016: Biostatistics
Brisa N Sánchez, Meihua Wu, Peter X K Song, Wen Wang
Advances in high throughput technology have accelerated the use of hundreds to millions of biomarkers to construct classifiers that partition patients into different clinical conditions. Prior to classifier development in actual studies, a critical need is to determine the sample size required to reach a specified classification precision. We develop a systematic approach for sample size determination in high-dimensional (large [Formula: see text] small [Formula: see text]) classification analysis. Our method utilizes the probability of correct classification (PCC) as the optimization objective function and incorporates the higher criticism thresholding procedure for classifier development...
October 2016: Biostatistics
Federico Ambrogi, Thomas H Scheike
High-dimensional regression has become an increasingly important topic for many research fields. For example, biomedical research generates an increasing amount of data to characterize patients' bio-profiles (e.g. from a genomic high-throughput assay). The increasing complexity in the characterization of patients' bio-profiles is added to the complexity related to the prolonged follow-up of patients with the registration of the occurrence of possible adverse events. This information may offer useful insight into disease dynamics and in identifying subset of patients with worse prognosis and better response to the therapy...
October 2016: Biostatistics
Jennifer A Sinnott, Tianxi Cai
When a moderate number of potential predictors are available and a survival model is fit with regularization to achieve variable selection, providing accurate inference on the predicted survival can be challenging. We investigate inference on the predicted survival estimated after fitting a Cox model under regularization guaranteeing the oracle property. We demonstrate that existing asymptotic formulas for the standard errors of the coefficients tend to underestimate the variability for some coefficients, while typical resampling such as the bootstrap tends to overestimate it; these approaches can both lead to inaccurate variance estimation for predicted survival functions...
October 2016: Biostatistics
Elisa Sheng, Daniela Witten, Xiao-Hua Zhou
In a multivariate setting, we consider the task of identifying features whose correlations with the other features differ across conditions. Such correlation shifts may occur independently of mean shifts, or differences in the means of the individual features across conditions. Previous approaches for detecting correlation shifts consider features simultaneously, by computing a correlation-based test statistic for each feature. However, since correlations involve two features, such approaches do not lend themselves to identifying which feature is the culprit...
October 2016: Biostatistics
Ziwen Tan, Guoyou Qin, Haibo Zhou
Outcome-dependent sampling (ODS) designs have been well recognized as a cost-effective way to enhance study efficiency in both statistical literature and biomedical and epidemiologic studies. A partially linear additive model (PLAM) is widely applied in real problems because it allows for a flexible specification of the dependence of the response on some covariates in a linear fashion and other covariates in a nonlinear non-parametric fashion. Motivated by an epidemiological study investigating the effect of prenatal polychlorinated biphenyls exposure on children's intelligence quotient (IQ) at age 7 years, we propose a PLAM in this article to investigate a more flexible non-parametric inference on the relationships among the response and covariates under the ODS scheme...
October 2016: Biostatistics
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