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Annals of Applied Statistics

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https://www.readbyqxmd.com/read/29081874/testing-high-dimensional-covariance-matrices-with-application-to-detecting-schizophrenia-risk-genes
#1
Lingxue Zhu, Jing Lei, Bernie Devlin, Kathryn Roeder
Scientists routinely compare gene expression levels in cases versus controls in part to determine genes associated with a disease. Similarly, detecting case-control differences in co-expression among genes can be critical to understanding complex human diseases; however statistical methods have been limited by the high dimensional nature of this problem. In this paper, we construct a sparse-Leading-Eigenvalue-Driven (sLED) test for comparing two high-dimensional covariance matrices. By focusing on the spectrum of the differential matrix, sLED provides a novel perspective that accommodates what we assume to be common, namely sparse and weak signals in gene expression data, and it is closely related with Sparse Principal Component Analysis...
September 2017: Annals of Applied Statistics
https://www.readbyqxmd.com/read/29081873/dynamic-prediction-for-multiple-repeated-measures-and-event-time-data-an-application-to-parkinson-s-disease
#2
Jue Wang, Sheng Luo, Liang Li
In many clinical trials studying neurodegenerative diseases such as Parkinson's disease (PD), multiple longitudinal outcomes are collected to fully explore the multidimensional impairment caused by this disease. If the outcomes deteriorate rapidly, patients may reach a level of functional disability sufficient to initiate levodopa therapy for ameliorating disease symptoms. An accurate prediction of the time to functional disability is helpful for clinicians to monitor patients' disease progression and make informative medical decisions...
September 2017: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28989557/allele-specific-copy-number-estimation-by-whole-exome-sequencing
#3
Hao Chen, Yuchao Jiang, Kara N Maxwell, Katherine L Nathanson, Nancy Zhang
Whole exome sequencing is currently a technology of choice in large-scale cancer genomics studies, where the priority is to identify cancer-associated variants in coding regions. We describe a method for estimating allele-specific copy number using whole exome sequencing data from tumor and matched normal.
June 2017: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28959370/integrative-sparse-k-means-with-overlapping-group-lasso-in-genomic-applications-for-disease-subtype-discovery
#4
Zhiguang Huo, George Tseng
Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse K-means (is-K means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso...
June 2017: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28943991/improving-efficiency-in-biomarker-incremental-value-evaluation-under-two-phase-designs
#5
Yingye Zheng, Marshall Brown, Anna Lok, Tianxi Cai
Cost-effective yet efficient designs are critical to the success of biomarker evaluation research. Two-phase sampling designs, under which expensive markers are only measured on a subsample of cases and non-cases within a prospective cohort, are useful in novel biomarker studies for preserving study samples and minimizing cost of biomarker assaying. Statistical methods for quantifying the predictiveness of biomarkers under two-phase studies have been proposed (Cai and Zheng, 2012; Liu, Cai and Zheng, 2012)...
June 2017: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28979611/forecasting-seasonal-influenza-with-a-state-space-sir-model
#6
Dave Osthus, Kyle S Hickmann, Petruţa C Caragea, Dave Higdon, Sara Y Del Valle
Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the Centers for Disease Control and Prevention's influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts. Despite the relatively rich surveillance data and the recurrent nature of seasonal influenza, forecasting the timing and intensity of seasonal influenza in the U...
March 2017: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28572869/covariate-adaptive-clustering-of-exposures-for-air-pollution-epidemiology-cohorts
#7
Joshua P Keller, Mathias Drton, Timothy Larson, Joel D Kaufman, Dale P Sandler, Adam A Szpiro
Cohort studies in air pollution epidemiology aim to establish associations between health outcomes and air pollution exposures. Statistical analysis of such associations is complicated by the multivariate nature of the pollutant exposure data as well as the spatial misalignment that arises from the fact that exposure data are collected at regulatory monitoring network locations distinct from cohort locations. We present a novel clustering approach for addressing this challenge. Specifically, we present a method that uses geographic covariate information to cluster multi-pollutant observations and predict cluster membership at cohort locations...
March 2017: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28408966/gene-network-reconstruction-using-global-local-shrinkage-priors
#8
Gwenaël G R Leday, Mathisca C M de Gunst, Gino B Kpogbezan, Aad W van der Vaart, Wessel N van Wieringen, Mark A van de Wiel
Reconstructing a gene network from high-throughput molecular data is an important but challenging task, as the number of parameters to estimate easily is much larger than the sample size. A conventional remedy is to regularize or penalize the model likelihood. In network models, this is often done locally in the neighbourhood of each node or gene. However, estimation of the many regularization parameters is often difficult and can result in large statistical uncertainties. In this paper we propose to combine local regularization with global shrinkage of the regularization parameters to borrow strength between genes and improve inference...
March 2017: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28367256/investigating-differences-in-brain-functional-networks-using-hierarchical-covariate-adjusted-independent-component-analysis
#9
Ran Shi, Ying Guo
Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis (ICA), which is a powerful method to reconstruct latent source signals from their linear mixtures. In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Existing ICA methods, however, cannot directly incorporate covariate effects in ICA decomposition...
December 2016: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28280520/linking-lung-airway-structure-to-pulmonary-function-via-composite-bridge-regression
#10
Kun Chen, Eric A Hoffman, Indu Seetharaman, Feiran Jiao, Ching-Long Lin, Kung-Sik Chan
The human lung airway is a complex inverted tree-like structure. Detailed airway measurements can be extracted from MDCT-scanned lung images, such as segmental wall thickness, airway diameter, parent-child branch angles, etc. The wealth of lung airway data provides a unique opportunity for advancing our understanding of the fundamental structure-function relationships within the lung. An important problem is to construct and identify important lung airway features in normal subjects and connect these to standardized pulmonary function test results such as FEV1%...
December 2016: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28090239/the-screening-and-ranking-algorithm-for-change-points-detection-in-multiple-samples
#11
Chi Song, Xiaoyi Min, Heping Zhang
The chromosome copy number variation (CNV) is the deviation of genomic regions from their normal copy number states, which may associate with many human diseases. Current genetic studies usually collect hundreds to thousands of samples to study the association between CNV and diseases. CNVs can be called by detecting the change-points in mean for sequences of array-based intensity measurements. Although multiple samples are of interest, the majority of the available CNV calling methods are single sample based...
December 2016: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28580047/quantifying-the-spatial-inequality-and-temporal-trends-in-maternal-smoking-rates-in-glasgow
#12
Duncan Lee, Andrew Lawson
Maternal smoking is well known to adversely affect birth outcomes, and there is considerable spatial variation in the rates of maternal smoking in the city of Glasgow, Scotland. This spatial variation is a partial driver of health inequalities between rich and poor communities, and it is of interest to determine the extent to which these inequalities have changed over time. Therefore in this paper we develop a Bayesian hierarchical model for estimating the spatio-temporal pattern in smoking incidence across Glasgow between 2000 and 2013, which can identify the changing geographical extent of clusters of areas exhibiting elevated maternal smoking incidences that partially drive health inequalities...
September 28, 2016: Annals of Applied Statistics
https://www.readbyqxmd.com/read/29057027/discussion-of-fiber-direction-estimation-in-diffusion-mri
#13
Jian Kang, Lexin Li
No abstract text is available yet for this article.
September 2016: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28804529/rejoinder-fiber-direction-estimation-smoothing-and-tracking-in-diffusion-mri
#14
COMMENT
Raymond K W Wong, Thomas C M Lee, Debashis Paul, Jie Peng
No abstract text is available yet for this article.
September 2016: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28638497/fiber-direction-estimation-smoothing-and-tracking-in-diffusion-mri
#15
Raymond K W Wong, Thomas C M Lee, Debashis Paul, Jie Peng
Diffusion magnetic resonance imaging is an imaging technology designed to probe anatomical architectures of biological samples in an in vivo and noninvasive manner through measuring water diffusion. The contribution of this paper is threefold. First, it proposes a new method to identify and estimate multiple diffusion directions within a voxel through a new and identifiable parametrization of the widely used multi-tensor model. Unlike many existing methods, this method focuses on the estimation of diffusion directions rather than the diffusion tensors...
September 2016: Annals of Applied Statistics
https://www.readbyqxmd.com/read/29081872/pseudo-value-approach-for-conditional-quantile-residual-lifetime-analysis-for-clustered-survival-and-competing-risks-data-with-applications-to-bone-marrow-transplant-data
#16
Kwang Woo Ahn, Brent R Logan
Quantile residual lifetime analysis is conducted to compare remaining lifetimes among groups for survival data. Evaluating residual lifetimes among groups after adjustment for covariates is often of interest. The current literature is limited to comparing two groups for independent data. We propose a pseudo-value approach to compare quantile residual lifetimes given covariates between multiple groups for independent and clustered survival data. The proposed method considers clustered event times and clustered censoring times in addition to independent event times and censoring times...
June 2016: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28367255/robust-hyperparameter-estimation-protects-against-hypervariable-genes-and-improves-power-to-detect-differential-expression
#17
Belinda Phipson, Stanley Lee, Ian J Majewski, Warren S Alexander, Gordon K Smyth
One of the most common analysis tasks in genomic research is to identify genes that are differentially expressed (DE) between experimental conditions. Empirical Bayes (EB) statistical tests using moderated genewise variances have been very effective for this purpose, especially when the number of biological replicate samples is small. The EB procedures can however be heavily influenced by a small number of genes with very large or very small variances. This article improves the differential expression tests by robustifying the hyperparameter estimation procedure...
June 2016: Annals of Applied Statistics
https://www.readbyqxmd.com/read/27746850/predictive-modeling-of-cholera-outbreaks-in-bangladesh
#18
Amanda A Koepke, Ira M Longini, M Elizabeth Halloran, Jon Wakefield, Vladimir N Minin
Despite seasonal cholera outbreaks in Bangladesh, little is known about the relationship between environmental conditions and cholera cases. We seek to develop a predictive model for cholera outbreaks in Bangladesh based on environmental predictors. To do this, we estimate the contribution of environmental variables, such as water depth and water temperature, to cholera outbreaks in the context of a disease transmission model. We implement a method which simultaneously accounts for disease dynamics and environmental variables in a Susceptible-Infected-Recovered-Susceptible (SIRS) model...
June 2016: Annals of Applied Statistics
https://www.readbyqxmd.com/read/27630755/feature-screening-for-time-varying-coefficient-models-with-ultrahigh-dimensional-longitudinal-data
#19
Wanghuan Chu, Runze Li, Matthew Reimherr
Motivated by an empirical analysis of the Childhood Asthma Management Project, CAMP, we introduce a new screening procedure for varying coefficient models with ultrahigh dimensional longitudinal predictor variables. The performance of the proposed procedure is investigated via Monte Carlo simulation. Numerical comparisons indicate that it outperforms existing ones substantially, resulting in significant improvements in explained variability and prediction error. Applying these methods to CAMP, we are able to find a number of potentially important genetic mutations related to lung function, several of which exhibit interesting nonlinear patterns around puberty...
June 2016: Annals of Applied Statistics
https://www.readbyqxmd.com/read/27326312/sequential-advantage-selection-for-optimal-treatment-regime
#20
Ailin Fan, Wenbin Lu, Rui Song
Variable selection for optimal treatment regime in a clinical trial or an observational study is getting more attention. Most existing variable selection techniques focused on selecting variables that are important for prediction, therefore some variables that are poor in prediction but are critical for decision-making may be ignored. A qualitative interaction of a variable with treatment arises when treatment effect changes direction as the value of this variable varies. The qualitative interaction indicates the importance of this variable for decision-making...
March 2016: Annals of Applied Statistics
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