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JOINT MODELING OF MULTISTATE AND NONPARAMETRIC MULTIVARIATE LONGITUDINAL DATA. Ann. Appl. Stat. (IF 1.3) Pub Date : 2024-08-05 L U You,Falastin Salami,Carina Törn,Åke Lernmark,Roy Tamura
It is oftentimes the case in studies of disease progression that subjects can move into one of several disease states of interest. Multistate models are an indispensable tool to analyze data from such studies. The Environmental Determinants of Diabetes in the Young (TEDDY) is an observational study of at-risk children from birth to onset of type-1 diabetes (T1D) up through the age of 15. A joint model
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PATIENT RECRUITMENT USING ELECTRONIC HEALTH RECORDS UNDER SELECTION BIAS: A TWO-PHASE SAMPLING FRAMEWORK. Ann. Appl. Stat. (IF 1.3) Pub Date : 2024-08-05 Guanghao Zhang,Lauren J Beesley,Bhramar Mukherjee,X U Shi
Electronic health records (EHRs) are increasingly recognized as a cost-effective resource for patient recruitment in clinical research. However, how to optimally select a cohort from millions of individuals to answer a scientific question of interest remains unclear. Consider a study to estimate the mean or mean difference of an expensive outcome. Inexpensive auxiliary covariates predictive of the
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Selecting Invalid Instruments to Improve Mendelian Randomization with Two-Sample Summary Data. Ann. Appl. Stat. (IF 1.3) Pub Date : 2024-04-05 Ashish Patel,Francis J DiTraglia,Verena Zuber,Stephen Burgess
Mendelian randomization (MR) is a widely-used method to estimate the causal relationship between a risk factor and disease. A fundamental part of any MR analysis is to choose appropriate genetic variants as instrumental variables. Genome-wide association studies often reveal that hundreds of genetic variants may be robustly associated with a risk factor, but in some situations investigators may have
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TENSOR QUANTILE REGRESSION WITH LOW-RANK TENSOR TRAIN ESTIMATION. Ann. Appl. Stat. (IF 1.3) Pub Date : 2024-04-05 Zihuan Liu,Cheuk Yin Lee,Heping Zhang
Neuroimaging studies often involve predicting a scalar outcome from an array of images collectively called tensor. The use of magnetic resonance imaging (MRI) provides a unique opportunity to investigate the structures of the brain. To learn the association between MRI images and human intelligence, we formulate a scalar-on-image quantile regression framework. However, the high dimensionality of the
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A SIMPLE AND FLEXIBLE TEST OF SAMPLE EXCHANGEABILITY WITH APPLICATIONS TO STATISTICAL GENOMICS. Ann. Appl. Stat. (IF 1.3) Pub Date : 2024-01-31 Alan J Aw,Jeffrey P Spence,Yun S Song
In scientific studies involving analyses of multivariate data, basic but important questions often arise for the researcher: Is the sample exchangeable, meaning that the joint distribution of the sample is invariant to the ordering of the units? Are the features independent of one another, or perhaps the features can be grouped so that the groups are mutually independent? In statistical genomics, these
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ANOPOW FOR REPLICATED NONSTATIONARY TIME SERIES IN EXPERIMENTS. Ann. Appl. Stat. (IF 1.3) Pub Date : 2024-01-31 Zeda Li,Yu Ryan Yue,Scott A Bruce
We propose a novel analysis of power (ANOPOW) model for analyzing replicated nonstationary time series commonly encountered in experimental studies. Based on a locally stationary ANOPOW Cramér spectral representation, the proposed model can be used to compare the second-order time-varying frequency patterns among different groups of time series and to estimate group effects as functions of both time
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USING SIMULTANEOUS REGRESSION CALIBRATION TO STUDY THE EFFECT OF MULTIPLE ERROR-PRONE EXPOSURES ON DISEASE RISK UTILIZING BIOMARKERS DEVELOPED FROM A CONTROLLED FEEDING STUDY. Ann. Appl. Stat. (IF 1.3) Pub Date : 2024-01-31 Yiwen Zhang,Ran Dai,Ying Huang,Ross Prentice,Cheng Zheng
Systematic measurement error in self-reported data creates important challenges in association studies between dietary intakes and chronic disease risks, especially when multiple dietary components are studied jointly. The joint regression calibration method has been developed for measurement error correction when objectively measured biomarkers are available for all dietary components of interest
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BAYESIAN LEARNING OF COVID-19 VACCINE SAFETY WHILE INCORPORATING ADVERSE EVENTS ONTOLOGY. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-10-30 Bangyao Zhao,Yuan Zhong,Jian Kang,Lili Zhao
While vaccines are crucial to end the COVID-19 pandemic, public confidence in vaccine safety has always been vulnerable. Many statistical methods have been applied to VAERS (Vaccine Adverse Event Reporting System) database to study the safety of COVID-19 vaccines. However, none of these methods considered the adverse event (AE) ontology. AEs are naturally related; for example, events of retching, dysphagia
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A BAYESIAN DECISION FRAMEWORK FOR OPTIMIZING SEQUENTIAL COMBINATION ANTIRETROVIRAL THERAPY IN PEOPLE WITH HIV. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-10-30 Wei Jin,Yang Ni,Jane O'Halloran,Amanda B Spence,Leah H Rubin,Yanxun Xu
Numerous adverse effects (e.g., depression) have been reported for combination antiretroviral therapy (cART) despite its remarkable success in viral suppression in people with HIV (PWH). To improve long-term health outcomes for PWH, there is an urgent need to design personalized optimal cART with the lowest risk of comorbidity in the emerging field of precision medicine for HIV. Large-scale HIV studies
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BUILDING A DOSE TOXO-EQUIVALENCE MODEL FROM A BAYESIAN META-ANALYSIS OF PUBLISHED CLINICAL TRIALS. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-10-30 Elizabeth A Sigworth,Samuel M Rubinstein,Jeremy L Warner,Yong Chen,Qingxia Chen
In clinical practice medications are often interchanged in treatment protocols when a patient negatively reacts to their first line of therapy. Although switching between medications is common, clinicians often lack structured guidance when choosing the initial dose and frequency of a new medication, given the former with respect to risk of adverse events. In this paper we propose to establish this
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ADDRESSING SELECTION BIAS AND MEASUREMENT ERROR IN COVID-19 CASE COUNT DATA USING AUXILIARY INFORMATION. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-10-30 Walter Dempsey
Coronavirus case-count data has influenced government policies and drives most epidemiological forecasts. Limited testing is cited as the key driver behind minimal information on the COVID-19 pandemic. While expanded testing is laudable, measurement error and selection bias are the two greatest problems limiting our understanding of the COVID-19 pandemic; neither can be fully addressed by increased
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A RIEMANN MANIFOLD MODEL FRAMEWORK FOR LONGITUDINAL CHANGES IN PHYSICAL ACTIVITY PATTERNS. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-10-30 Jingjing Zou,Tuo Lin,Chongzhi Di,John Bellettiere,Marta M Jankowska,Sheri J Hartman,Dorothy D Sears,Andrea Z LaCroix,Cheryl L Rock,Loki Natarajan
Physical activity (PA) is significantly associated with many health outcomes. The wide usage of wearable accelerometer-based activity trackers in recent years has provided a unique opportunity for in-depth research on PA and its relations with health outcomes and interventions. Past analysis of activity tracker data relies heavily on aggregating minute-level PA records into day-level summary statistics
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A DYNAMIC SPATIAL FILTERING APPROACH TO MITIGATE UNDERESTIMATION BIAS IN FIELD CALIBRATED LOW-COST SENSOR AIR POLLUTION DATA. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-10-30 Claire Heffernan,Roger PenG,Drew R Gentner,Kirsten Koehler,Abhirup Datta
Low-cost air pollution sensors, offering hyper-local characterization of pollutant concentrations, are becoming increasingly prevalent in environmental and public health research. However, low-cost air pollution data can be noisy, biased by environmental conditions, and usually need to be field-calibrated by collocating low-cost sensors with reference-grade instruments. We show, theoretically and empirically
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BAYESIAN HIERARCHICAL MODELING AND ANALYSIS FOR ACTIGRAPH DATA FROM WEARABLE DEVICES. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-10-30 Pierfrancesco Alaimo Di Loro,Marco Mingione,Jonah Lipsitt,Christina M Batteate,Michael Jerrett,Sudipto Banerjee
The majority of Americans fail to achieve recommended levels of physical activity, which leads to numerous preventable health problems such as diabetes, hypertension, and heart diseases. This has generated substantial interest in monitoring human activity to gear interventions toward environmental features that may relate to higher physical activity. Wearable devices, such as wrist-worn sensors that
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A DYNAMIC ADDITIVE AND MULTIPLICATIVE EFFECTS NETWORK MODEL WITH APPLICATION TO THE UNITED NATIONS VOTING BEHAVIORS. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-10-30 Bomin Kim,Xiaoyue Niu,David Hunter,Xun CaO
Motivated by a study of United Nations voting behaviors, we introduce a regression model for a series of networks that are correlated over time. Our model is a dynamic extension of the additive and multiplicative effects network model (AMEN) of Hoff (2021). In addition to incorporating a temporal structure, the model accommodates two types of missing data thus allows the size of the network to vary
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GENERALIZED MATRIX DECOMPOSITION REGRESSION: ESTIMATION AND INFERENCE FOR TWO-WAY STRUCTURED DATA. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-10-30 Yue Wang,Ali Shojaie,Timothy Randolph,Parker Knight,Jing Ma
Motivated by emerging applications in ecology, microbiology, and neuroscience, this paper studies high-dimensional regression with two-way structured data. To estimate the high-dimensional coefficient vector, we propose the generalized matrix decomposition regression (GMDR) to efficiently leverage auxiliary information on row and column structures. GMDR extends the principal component regression (PCR)
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Estimating Causal Effects of HIV Prevention Interventions with Interference in Network-based Studies among People Who Inject Drugs. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-09-07 TingFang Lee,Ashley L Buchanan,Natallia V Katenka,Laura Forastiere,M Elizabeth Halloran,Samuel R Friedman,Georgios Nikolopoulos
Evaluating causal effects in the presence of interference is challenging in network-based studies of hard-to-reach populations. Like many such populations, people who inject drugs (PWID) are embedded in social networks and often exert influence on others in their network. In our setting, the study design is observational with a non-randomized network-based HIV prevention intervention. Information is
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DYNAMIC PREDICTION OF RESIDUAL LIFE WITH LONGITUDINAL COVARIATES USING LONG SHORT-TERM MEMORY NETWORKS. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-09-07 Grace Rhodes,Marie Davidian,Wenbin Lu
Sepsis, a complex medical condition that involves severe infections with life-threatening organ dysfunction, is a leading cause of death worldwide. Treatment of sepsis is highly challenging. When making treatment decisions, clinicians and patients desire accurate predictions of mean residual life (MRL) that leverage all available patient information, including longitudinal biomarker data. Biomarkers
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BAYESIAN INFERENCE AND DYNAMIC PREDICTION FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-09-07 Haotian Zou,Donglin Zeng,Luo Xiao,Sheng Luo
Alzheimer's disease (AD) is a complex neurological disorder impairing multiple domains such as cognition and daily functions. To better understand the disease and its progression, many AD research studies collect multiple longitudinal outcomes that are strongly predictive of the onset of AD dementia. We propose a joint model based on a multivariate functional mixed model framework (referred to as MFMM-JM)
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LOCUS: A REGULARIZED BLIND SOURCE SEPARATION METHOD WITH LOW-RANK STRUCTURE FOR INVESTIGATING BRAIN CONNECTIVITY. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-05-01 Yikai Wang,Ying Guo
Network-oriented research has been increasingly popular in many scientific areas. In neuroscience research, imaging-based network connectivity measures have become the key for understanding brain organizations, potentially serving as individual neural fingerprints. There are major challenges in analyzing connectivity matrices, including the high dimensionality of brain networks, unknown latent sources
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CO-CLUSTERING OF SPATIALLY RESOLVED TRANSCRIPTOMIC DATA. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-05-01 Andrea Sottosanti,Davide Risso
Spatial transcriptomics is a groundbreaking technology that allows the measurement of the activity of thousands of genes in a tissue sample and maps where the activity occurs. This technology has enabled the study of the spatial variation of the genes across the tissue. Comprehending gene functions and interactions in different areas of the tissue is of great scientific interest, as it might lead to
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Robust joint modelling of left-censored longitudinal data and survival data with application to HIV vaccine studies. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-05-01 Tingting Yu,Lang Wu,Jin Qiu,Peter B Gilbert
In jointly modelling longitudinal and survival data, the longitudinal data may be complex in the sense that they may contain outliers and may be left censored. Motivated from an HIV vaccine study, we propose a robust method for joint models of longitudinal and survival data, where the outliers in longitudinal data are addressed using a multivariate t-distribution for b-outliers and using an M-estimator
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IDENTIFICATION OF IMMUNE RESPONSE COMBINATIONS ASSOCIATED WITH HETEROGENEOUS INFECTION RISK IN THE IMMUNE CORRELATES ANALYSIS OF HIV VACCINE STUDIES. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-05-01 Chaeryon Kang,Ying Huang
In HIV vaccine/prevention research, probing into the vaccine-induced immune responses that can help predict the risk of HIV infection provides valuable information for the development of vaccine regimens. Previous correlate analysis of the Thai vaccine trial aided the discovery of interesting immune correlates related to the risk of developing an HIV infection. The present study aimed to identify the
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DYNAMIC RISK PREDICTION TRIGGERED BY INTERMEDIATE EVENTS USING SURVIVAL TREE ENSEMBLES. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-05-01 Yifei Sun,Sy Han Chiou,Colin O Wu,Meghan McGarry,Chiung-Yu Huang
With the availability of massive amounts of data from electronic health records and registry databases, incorporating time-varying patient information to improve risk prediction has attracted great attention. To exploit the growing amount of predictor information over time, we develop a unified framework for landmark prediction using survival tree ensembles, where an updated prediction can be performed
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BAYESIAN ANALYSIS FOR IMBALANCED POSITIVE-UNLABELLED DIAGNOSIS CODES IN ELECTRONIC HEALTH RECORDS. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-05-01 Ru Wang,Ye Liang,Zhuqi Miao,Tieming Liu
With the increasing availability of electronic health records (EHR), significant progress has been made on developing predictive inference and algorithms by health data analysts and researchers. However, the EHR data are notoriously noisy due to missing and inaccurate inputs despite the information is abundant. One serious problem is that only a small portion of patients in the database has confirmatory
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TEAM: A MULTIPLE TESTING ALGORITHM ON THE AGGREGATION TREE FOR FLOW CYTOMETRY ANALYSIS. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-01-24 John A Pura,Xuechan Li,Cliburn Chan,Jichun Xie
In immunology studies, flow cytometry is a commonly used multivariate single-cell assay. One key goal in flow cytometry analysis is to detect the immune cells responsive to certain stimuli. Statistically, this problem can be translated into comparing two protein expression probability density functions (pdfs) before and after the stimulus; the goal is to pinpoint the regions where these two pdfs differ
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Probabilistic HIV recency classification-a logistic regression without labeled individual level training data. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-01-24 Ben Sheng,Changcheng Li,Le Bao,Runze Li
Accurate HIV incidence estimation based on individual recent infection status (recent vs long-term infection) is important for monitoring the epidemic, targeting interventions to those at greatest risk of new infection, and evaluating existing programs of prevention and treatment. Starting from 2015, the Population-based HIV Impact Assessment (PHIA) individual-level surveys are implemented in the most-affected
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MODELING CELL POPULATIONS MEASURED BY FLOW CYTOMETRY WITH COVARIATES USING SPARSE MIXTURE OF REGRESSIONS. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-01-24 By Sangwon Hyun,Mattias Rolf Cape,Francois Ribalet,Jacob Bien
The ocean is filled with microscopic microalgae, called phytoplankton, which together are responsible for as much photosynthesis as all plants on land combined. Our ability to predict their response to the warming ocean relies on understanding how the dynamics of phytoplankton populations is influenced by changes in environmental conditions. One powerful technique to study the dynamics of phytoplankton
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Fitting stochastic epidemic models to gene genealogies using linear noise approximation. Ann. Appl. Stat. (IF 1.3) Pub Date : 2023-01-24 Mingwei Tang,Gytis Dudas,Trevor Bedford,Vladimir N Minin
Phylodynamics is a set of population genetics tools that aim at reconstructing demographic history of a population based on molecular sequences of individuals sampled from the population of interest. One important task in phylodynamics is to estimate changes in (effective) population size. When applied to infectious disease sequences such estimation of population size trajectories can provide information
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Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-12-01 Oliver M Crook,Kathryn S Lilley,Laurent Gatto,Paul D W Kirk
Understanding sub-cellular protein localisation is an essential component in the analysis of context specific protein function. Recent advances in quantitative mass-spectrometry (MS) have led to high resolution mapping of thousands of proteins to sub-cellular locations within the cell. Novel modelling considerations to capture the complex nature of these data are thus necessary. We approach analysis
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TWO-SAMPLE TESTS FOR MULTIVARIATE REPEATED MEASUREMENTS OF HISTOGRAM OBJECTS WITH APPLICATIONS TO WEARABLE DEVICE DATA. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-09-26 Jingru Zhang,Kathleen R Merikangas,Hongzhe Li,Haochang Shou
Repeated observations have become increasingly common in biomedical research and longitudinal studies. For instance, wearable sensor devices are deployed to continuously track physiological and biological signals from each individual over multiple days. It remains of great interest to appropriately evaluate how the daily distribution of biosignals might differ across disease groups and demographics
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AN OMNIBUS TEST FOR DETECTION OF SUBGROUP TREATMENT EFFECTS VIA DATA PARTITIONING. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-09-26 Yifei Sun,Xuming He,Jianhua Hu
Late-stage clinical trials have been conducted primarily to establish the efficacy of a new treatment in an intended population. A corollary of population heterogeneity in clinical trials is that a treatment might be effective for one or more subgroups, rather than for the whole population of interest. As an example, the phase III clinical trial of panitumumab in metastatic colorectal cancer patients
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A SPATIAL CAUSAL ANALYSIS OF WILDLAND FIRE-CONTRIBUTED PM2.5 USING NUMERICAL MODEL OUTPUT. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-09-26 Alexandra Larsen,Shu Yang,Brian J Reich,Ana G Rappold
Wildland fire smoke contains hazardous levels of fine particulate matter (PM2.5), a pollutant shown to adversely effect health. Estimating fire attributable PM2.5 concentrations is key to quantifying the impact on air quality and subsequent health burden. This is a challenging problem since only total PM2.5 is measured at monitoring stations and both fire-attributable PM2.5 and PM2.5 from all other
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EXTENDED STOCHASTIC BLOCK MODELS WITH APPLICATION TO CRIMINAL NETWORKS. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-09-26 Sirio Legramanti,Tommaso Rigon,Daniele Durante,David B Dunson
Reliably learning group structures among nodes in network data is challenging in several applications. We are particularly motivated by studying covert networks that encode relationships among criminals. These data are subject to measurement errors, and exhibit a complex combination of an unknown number of core-periphery, assortative and disassortative structures that may unveil key architectures of
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Bayesian Inference for Brain Activity from Functional Magnetic Resonance Imaging Collected at Two Spatial Resolutions. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-09-26 Andrew S Whiteman,Andreas J Bartsch,Jian Kang,Timothy D Johnson
Neuroradiologists and neurosurgeons increasingly opt to use functional magnetic resonance imaging (fMRI) to map functionally relevant brain regions for noninvasive presurgical planning and intraoperative neuronavigation. This application requires a high degree of spatial accuracy, but the fMRI signal-to-noise ratio (SNR) decreases as spatial resolution increases. In practice, fMRI scans can be collected
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BAYESIAN HIERARCHICAL RANDOM-EFFECTS META-ANALYSIS AND DESIGN OF PHASE I CLINICAL TRIALS. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-09-26 Ruitao Lin,Haolun Shi,Guosheng Yin,Peter F Thall,Ying Yuan,Christopher R Flowers
We propose a curve-free random-effects meta-analysis approach to combining data from multiple phase I clinical trials to identify an optimal dose. Our method accounts for between-study heterogeneity that may stem from different study designs, patient populations, or tumor types. We also develop a meta-analytic-predictive (MAP) method based on a power prior that incorporates data from multiple historical
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SCALAR ON NETWORK REGRESSION VIA BOOSTING. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-09-26 Emily L Morris,Kevin He,Jian Kang
Neuroimaging studies have a growing interest in learning the association between the individual brain connectivity networks and their clinical characteristics. It is also of great interest to identify the sub brain networks as biomarkers to predict the clinical symptoms, such as disease status, potentially providing insight on neuropathology. This motivates the need for developing a new type of regression
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MODEL-BASED DISTANCE EMBEDDING WITH APPLICATIONS TO CHROMOSOMAL CONFORMATION BIOLOGY. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-07-19 Yuping Zhang,Disheng Mao,Zhengqing Ouyang
Recent development of high-throughput biotechnologies, such as Hi-C, have enabled genome-wide measurement of chromosomal conformation. The interaction signals among genomic loci are contaminated with noises. It remains largely unknown how well the underlying chromosomal conformation can be elucidated, based on massive and noisy measurements. We propose a new model-based distance embedding (MDE) framework
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A NOVEL FRAMEWORK TO ESTIMATE MULTIDIMENSIONAL MINIMUM EFFECTIVE DOSES USING ASYMMETRIC POSTERIOR GAIN AND ϵ-TAPERING. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-07-19 Ying Kuen Cheung,Thevaa Chandereng,Keith M Diaz
In this article we address the problem of estimating minimum effective doses in dose-finding clinical trials of multidimensional treatment. We are motivated by a behavioral intervention trial where we introduce sedentary breaks to subjects with a goal to reduce their glucose level monitored over 8 hours. Each sedentary break regimen is defined by two elements: break frequency and break duration. The
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BAYESIAN FUNCTIONAL REGISTRATION OF FMRI ACTIVATION MAPS. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-07-19 Guoqing Wang,Abhirup Datta,Martin A Lindquist
Functional magnetic resonance imaging (fMRI) has provided invaluable insight into our understanding of human behavior. However, large inter-individual differences in both brain anatomy and functional localization after anatomical alignment remain a major limitation in conducting group analyses and performing population level inference. This paper addresses this problem by developing and validating
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A BAYESIAN HIERARCHICAL MODEL FOR COMBINING MULTIPLE DATA SOURCES IN POPULATION SIZE ESTIMATION. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-07-19 Jacob Parsons,Xiaoyue Niu,Le Bao
To combat the HIV/AIDS pandemic effectively, targeted interventions among certain key populations play a critical role. Examples of such key populations include sex workers, people who inject drugs, and men who have sex with men. While having accurate estimates for the size of these key populations is important, any attempt to directly contact or count members of these populations is difficult. As
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SENSITIVITY ANALYSIS FOR EVALUATING PRINCIPAL SURROGATE ENDPOINTS RELAXING THE EQUAL EARLY CLINICAL RISK ASSUMPTION. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-07-19 Ying Huang,Yingying Zhuang,Peter Gilbert
This article addresses the evaluation of post-randomization immune response biomarkers as principal surrogate endpoints of a vaccine's protective effect, based on data from randomized vaccine trials. An important metric for quantifying a biomarker's principal surrogacy in vaccine research is the vaccine efficacy curve, which shows a vaccine's efficacy as a function of potential biomarker values if
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CRITICAL WINDOW VARIABLE SELECTION FOR MIXTURES: ESTIMATING THE IMPACT OF MULTIPLE AIR POLLUTANTS ON STILLBIRTH. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-07-19 Joshua L Warren,Howard H Chang,Lauren K Warren,Matthew J Strickland,Lyndsey A Darrow,James A Mulholland
Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable subpopulations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. We extend critical window variable selection
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BAYESIAN SEMIPARAMETRIC LONG MEMORY MODELS FOR DISCRETIZED EVENT DATA. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-07-19 Antik Chakraborty,Otso Ovaskainen,David B Dunson
We introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed methodology is motivated by a study of bird vocalizations in the Amazon rain forest; the timings of vocalizations exhibit self-similarity and long range dependence. This rules out Poisson process based models where the rate function itself is not long range dependent. The proposed
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DIRICHLET-TREE MULTINOMIAL MIXTURES FOR CLUSTERING MICROBIOME COMPOSITIONS. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-07-19 Jialiang Mao,L I Ma
Studying the human microbiome has gained substantial interest in recent years, and a common task in the analysis of these data is to cluster microbiome compositions into subtypes. This subdivision of samples into subgroups serves as an intermediary step in achieving personalized diagnosis and treatment. In applying existing clustering methods to modern microbiome studies including the American Gut
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LARGE-SCALE MULTIVARIATE SPARSE REGRESSION WITH APPLICATIONS TO UK BIOBANK. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-07-19 Junyang Qian,Yosuke Tanigawa,Ruilin Li,Robert Tibshirani,Manuel A Rivas,Trevor Hastie
In high-dimensional regression problems, often a relatively small subset of the features are relevant for predicting the outcome, and methods that impose sparsity on the solution are popular. When multiple correlated outcomes are available (multitask), reduced rank regression is an effective way to borrow strength and capture latent structures that underlie the data. Our proposal is motivated by the
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A FLEXIBLE SENSITIVITY ANALYSIS APPROACH FOR UNMEASURED CONFOUNDING WITH MULTIPLE TREATMENTS AND A BINARY OUTCOME WITH APPLICATION TO SEER-MEDICARE LUNG CANCER DATA. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-06-13 Liangyuan Hu,Jungang Zou,Chenyang Gu,Jiayi Ji,Michael Lopez,Minal Kale
In the absence of a randomized experiment, a key assumption for drawing causal inference about treatment effects is the ignorable treatment assignment. Violations of the ignorability assumption may lead to biased treatment effect estimates. Sensitivity analysis helps gauge how causal conclusions will be altered in response to the potential magnitude of departure from the ignorability assumption. However
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KERNEL MACHINE AND DISTRIBUTED LAG MODELS FOR ASSESSING WINDOWS OF SUSCEPTIBILITY TO ENVIRONMENTAL MIXTURES IN CHILDREN'S HEALTH STUDIES. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-06-13 Ander Wilson,Hsiao-Hsien Leon Hsu,Yueh-Hsiu Mathilda Chiu,Robert O Wright,Rosalind J Wright,Brent A Coull
Exposures to environmental chemicals during gestation can alter health status later in life. Most studies of maternal exposure to chemicals during pregnancy have focused on a single chemical exposure observed at high temporal resolution. Recent research has turned to focus on exposure to mixtures of multiple chemicals, generally observed at a single time point. We consider statistical methods for analyzing
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COMPOSITE MIXTURE OF LOG-LINEAR MODELS WITH APPLICATION TO PSYCHIATRIC STUDIES. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-06-13 Emanuele Aliverti,David B Dunson
Psychiatric studies of suicide provide fundamental insights on the evolution of severe psychopathologies, and contribute to the development of early treatment interventions. Our focus is on modelling different traits of psychosis and their interconnections, focusing on a case study on suicide attempt survivors. Such aspects are recorded via multivariate categorical data, involving a large numbers of
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BOUNDING THE LOCAL AVERAGE TREATMENT EFFECT IN AN INSTRUMENTAL VARIABLE ANALYSIS OF ENGAGEMENT WITH A MOBILE INTERVENTION. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-03-28 Andrew J Spieker,Robert A Greevy,Lyndsay A Nelson,Lindsay S Mayberry
Estimation of local average treatment effects in randomized trials typically relies upon the exclusion restriction assumption in cases where we are unwilling to rule out the possibility of unmeasured confounding. Under this assumption, treatment effects are mediated through the post-randomization variable being conditioned upon, and directly attributable to neither the randomization itself nor its
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PARTITIONING AROUND MEDOIDS CLUSTERING AND RANDOM FOREST CLASSIFICATION FOR GIS-INFORMED IMPUTATION OF FLUORIDE CONCENTRATION DATA. Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-03-28 Yu Gu,John S Preisser,Donglin Zeng,Poojan Shrestha,Molina Shah,Miguel A Simancas-Pallares,Jeannie Ginnis,Kimon Divaris
Community water fluoridation is an important component of oral health promotion, as fluoride exposure is a well-documented dental caries-preventive agent. Direct measurements of domestic water fluoride content provide valuable information regarding individuals' fluoride exposure and thus caries risk; however, they are logistically challenging to carry out at a large scale in oral health research. This
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Estimating the effectiveness of permanent price reductions for competing products using multivariate Bayesian structural time series models Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-03-01 Fiammetta Menchetti,Iavor Bojinov
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The role of intrinsic dimension in high-resolution player tracking data—Insights in basketball Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-03-01 Edgar Santos-Fernandez,Francesco Denti,Kerrie Mengersen,Antonietta Mira
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In-game win probabilities for the National Rugby League Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-03-01 Tianyu Guan,Robert Nguyen,Jiguo Cao,Tim Swartz
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Preelectoral polls variability: A hierarchical Bayesian model to assess the role of house effects with application to Italian elections Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-03-01 Domenico De Stefano,Francesco Pauli,Nicola Torelli
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Multivariate mixed membership modeling: Inferring domain-specific risk profiles Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-03-01 Massimiliano Russo,Burton H. Singer,David B. Dunson
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Bayesian adjustment for preferential testing in estimating infection fatality rates, as motivated by the COVID-19 pandemic Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-03-01 Harlan Campbell,Perry de Valpine,Lauren Maxwell,Valentijn M. T. de Jong,Thomas P. A. Debray,Thomas Jaenisch,Paul Gustafson
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Bayesian nonparametric multivariate spatial mixture mixed effects models with application to American Community Survey special tabulations Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-03-01 Ryan Janicki,Andrew M. Raim,Scott H. Holan,Jerry J. Maples
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Identifying intergenerational patterns of correlated methylation sites Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-03-01 Xichen Mou,Hongmei Zhang,S. Hasan Arshad
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Bidimensional linked matrix factorization for pan-omics pan-cancer analysis Ann. Appl. Stat. (IF 1.3) Pub Date : 2022-03-01 Eric F. Lock,Jun Young Park,Katherine A. Hoadley