Annals of Applied Statistics最新文献

{"title":"MIXED MODELING APPROACH FOR CHARACTERIZING THE GENETIC EFFECTS IN A LONGITUDINAL PHENOTYPE.","authors":"Pei Zhang, Paul S Albert, Hyokyoung G Hong","doi":"10.1214/25-aoas2033","DOIUrl":"10.1214/25-aoas2033","url":null,"abstract":"<p><p>Approaches for estimating genetic effects at the individual level often focus on analyzing phenotypes at a single time point, with less attention given to longitudinal phenotypes. This paper introduces a mixed modeling approach that includes both genetic and individual-specific random effects, and is designed to estimate genetic effects on both the baseline and slope for a longitudinal trajectory. The inclusion of genetic effects on both baseline and slope, combined with the crossed structure of genetic and individual-specific random effects, creates complex dependencies across repeated measurements for all subjects. These complexities necessitate the development of novel estimation procedures for parameter estimation and individual-specific predictions of genetic effects on both baseline and slope. We employ an Average Information Restricted Maximum Likelihood (AI-ReML) algorithm to estimate the variance components corresponding to genetic and individual-specific effects for the baseline levels and rates of change for a longitudinal phenotype. The algorithm is used to characterizes the prostate-specific antigen (PSA) trajectories for participants who remained prostate cancer-free in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Understanding genetic and individual-specific variation in this population will provide insights for determining the role of genetics in cancer screening. Our results reveal significant genetic contributions to both the initial PSA levels and their progression over time, highlighting the role of these genetic factors on the variability of PSA across unaffected individuals. We show how genetic factors can be used to identify individuals prone to large baseline and increasing trajectories PSA values among individuals who are prostate cancer-free. In turn, we can identify groups of individuals who have a high probability of falsely screening positive for prostate cancer using well established cutoffs for early detection based on the level and rate of change in this biomarker. The results demonstrate the importance of incorporating genetic factors for monitoring PSA for more accurate prostate cancer detection.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 3","pages":"2070-2087"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12395449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BAYESIAN LEARNING OF CLINICALLY MEANINGFUL SEPSIS PHENOTYPES IN NORTHERN TANZANIA.","authors":"Alexander Dombowsky, David B Dunson, Deng B Madut, Matthew P Rubach, Amy H Herring","doi":"10.1214/25-aoas2045","DOIUrl":"10.1214/25-aoas2045","url":null,"abstract":"<p><p>Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Recently, researchers have hypothesized that sepsis consists of a heterogeneous spectrum of distinct subtypes, motivating several studies to identify clusters of sepsis patients that correspond to subtypes, with the long-term goal of using these clusters to design subtype-specific treatments. Therefore, clinicians rely on clusters having a concrete medical interpretation, usually corresponding to clinically meaningful regions of the sample space that have a concrete implication to practitioners. In this article, we propose Clustering Around Meaningful Regions (CLAMR), a Bayesian clustering approach that explicitly models the medical interpretation of each cluster center. CLAMR favors clusterings that can be summarized via meaningful feature values, leading to medically significant sepsis patient clusters. We also provide details on measuring the effect of each feature on the clustering using Bayesian hypothesis tests, so one can assess what features are relevant for cluster interpretation. Our focus is on clustering sepsis patients from Moshi, Tanzania, where patients are younger and the prevalence of HIV infection is higher than in previous sepsis subtyping cohorts.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 3","pages":"2193-2217"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12422288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BAYESIAN DIFFERENTIAL CAUSAL DIRECTED ACYCLIC GRAPHS FOR OBSERVATIONAL ZERO-INFLATED COUNTS WITH AN APPLICATION TO TWO-SAMPLE SINGLE-CELL DATA.","authors":"Junsouk Choi, Robert S Chapkin, Yang Ni","doi":"10.1214/25-aoas2042","DOIUrl":"10.1214/25-aoas2042","url":null,"abstract":"<p><p>Observational zero-inflated count data arise in a wide range of areas such as genomics. One of the common research questions is to identify causal relationships by learning the structure of a sparse directed acyclic graph (DAG). While structure learning of DAGs has been an active research area, existing methods do not adequately account for excessive zeros and therefore are not suitable for modeling zero-inflated count data. Moreover, it is often interesting to study differences in the causal networks for data collected from two experimental groups (control vs treatment). To explicitly account for zero-inflation and identify differential causal networks, we propose a novel Bayesian differential zero-inflated negative binomial DAG (DAG0) model. We prove that the causal relationships under the proposed DAG0 are fully identifiable from purely observational, cross-sectional data, using a general proof technique that is applicable beyond the proposed model. Bayesian inference based on parallel-tempered Markov chain Monte Carlo is developed to efficiently explore the multi-modal posterior landscape. We demonstrate the utility of the proposed DAG0 by comparing it with state-of-the-art alternative methods through extensive simulations. An application in a single-cell RNA-sequencing dataset generated under two experimental groups finds some interesting results that appear to be consistent with existing knowledge. A user-friendly R package that implements DAG0 is available at https://github.com/junsoukchoi/BayesDAG0.git.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 3","pages":"1908-1930"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12395422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SURROGATE SELECTION OVERSAMPLES EXPANDED T CELL CLONOTYPES.","authors":"Peng Yu, Yumin Lian, Elliot Xie, Cindy L Zuleger, Richard J Albertini, Mark R Albertini, Michael A Newton","doi":"10.1214/25-aoas2032","DOIUrl":"10.1214/25-aoas2032","url":null,"abstract":"<p><p>Surrogate selection is an experimental design that without sequencing any DNA can restrict a sample of cells to those carrying certain genomic mutations. In immunological disease studies, this design may provide a relatively easy approach to enrich a lymphocyte sample with cells relevant to the disease response because the emergence of neutral mutations associates with the proliferation history of clonal subpopulations. A statistical analysis of clonotype sizes provides a structured, quantitative perspective on this useful property of surrogate selection. Our model specification couples within-clonotype birth-death processes with an exchangeable model across clonotypes. Beyond enrichment questions about the surrogate selection design, our framework enables a study of sampling properties of elementary sample diversity statistics; it also points to new statistics that may usefully measure the burden of somatic genomic alterations associated with clonal expansion. We examine statistical properties of immunological samples governed by the coupled model specification, and we illustrate calculations in surrogate selection studies of melanoma and in single-cell genomic studies of T cell repertoires.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 3","pages":"1884-1907"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A DEEP NEURAL NETWORK TWO-PART MODEL AND FEATURE IMPORTANCE TEST FOR SEMI-CONTINUOUS DATA.","authors":"Baiming Zou, Xinlei Mi, Shiyu Wan, Di Wu, James G Xenakis, Jianhua Hu, Fei Zou","doi":"10.1214/25-aoas2013","DOIUrl":"10.1214/25-aoas2013","url":null,"abstract":"<p><p>Semi-continuous data frequently arise in clinical practice. For example, while many surgical patients still suffer from varying degrees of acute postoperative pain (POP) sometime after surgery (i.e., POP score > 0), others experience none (i.e., POP score = 0), indicating the existence of two distinct data processes at play. Existing parametric or semi-parametric two-part modeling methods for this type of semi-continuous data can fail to appropriately model the two underlying data processes as such methods rely heavily on (generalized) linear additive assumptions. However, many factors may interact to jointly influence the experience of POP non-additively and non-linearly. Motivated by this challenge and inspired by the flexibility of deep neural networks (DNN) to accurately approximate complex functions universally, we derive a DNN-based two-part model by adapting the conventional DNN methods with two additional components: a bootstrapping procedure along with a filtering algorithm to boost the stability of the conventional DNN, an approach we denote as sDNN. To improve the interpretability and transparency of sDNN, we further derive a feature importance testing procedure to identify important features associated with the outcome measurements of the two data processes, denoting this approach fsDNN. We show that fsDNN not only offers a statistical inference procedure for each feature under complex association but also that using the identified features can further improve the predictive performance of sDNN. The proposed sDNN- and fsDNN-based two-part models are applied to the analysis of real data from a POP study, in which application they clearly demonstrate advantages over the existing parametric and semi-parametric two-part models. Further, we conduct extensive numerical studies and draw comparisons with other machine learning methods to demonstrate that sDNN and fsDNN consistently outperform the existing two-part models and frequently used machine learning methods regardless of the data complexity. An R package implementing the proposed methods has been developed and is available in the Supplementary Material (Zou et al, 2025) and is also deposited on GitHub (https://github.com/BZou-lab/fsDNN).</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 2","pages":"1314-1331"},"PeriodicalIF":1.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12263096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144644080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BAYESIAN DATA AUGMENTATION FOR RECURRENT EVENTS UNDER INTERMITTENT ASSESSMENT IN OVERLAPPING INTERVALS WITH APPLICATIONS TO EMR DATA.","authors":"Xin Liu, Patrick M Schnell","doi":"10.1214/24-aoas2007","DOIUrl":"10.1214/24-aoas2007","url":null,"abstract":"<p><p>Electronic medical records (EMR) data contain rich information that can facilitate health-related studies but is collected primarily for purposes other than research. For recurrent events, EMR data often do not record event times or counts but only contain intermittently assessed and censored observations (i.e. upper and/or lower bounds for counts in a time interval) at uncontrolled times. This can result in non-contiguous or overlapping assessment intervals with censored event counts. Existing methods for analyzing intermittently assessed recurrent events assume disjoint assessment intervals with known counts (interval count data) due to a focus on prospective studies with controlled assessment times. We propose a Bayesian data augmentation method to analyze the complicated assessments in EMR data for recurrent events. Within a Gibbs sampler, event times are imputed by generating sets of event times from non-homogeneous Poisson processes and rejecting proposed sets that are incompatible with constraints imposed by assessment data. Based on the independent increments property of Poisson processes, we implement three techniques to speed up this rejection sampling imputation method for large EMR datasets: independent sampling by partitioning, truncated generation, and sequential sampling. In a simulation study we show our method accurately estimates parameters of log-linear Poisson process intensities. Although the proposed method can be applied generally to EMR data of recurrent events, our study is specifically motivated by identifying risk factors for falls due to cancer treatment and its supportive medications. We used the proposed method to analyze an EMR dataset comprising 5501 patients treated for breast cancer. Our analysis provides evidence supporting associations between certain risk factors (including classes of medications) and risk of falls.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 2","pages":"1332-1361"},"PeriodicalIF":1.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12393837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DYNAMIC PREDICTION WITH MULTIVARIATE LONGITUDINAL OUTCOMES AND LONGITUDINAL MAGNETIC RESONANCE IMAGING DATA.","authors":"Haotian Zou, Luo Xiao, Donglin Zeng, Sheng Luo","doi":"10.1214/24-aoas1970","DOIUrl":"10.1214/24-aoas1970","url":null,"abstract":"<p><p>Alzheimer's Disease (AD) is a common neurodegenerative disorder impairing multiple domains. Recent AD studies, for example, the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, collect multimodal data to better understand AD severity and progression. To facilitate precision medicine for high-risk individuals, it is essential to develop an AD predictive model that leverages multimodal data and provides accurate personalized predictions of dementia occurrences. In this article we propose a multivariate functional mixed model with longitudinal magnetic resonance imaging data (MFMM-LMRI) that jointly models longitudinal neurological scores, longitudinal voxelwise MRI data, and the survival outcome as dementia onset. We model longitudinal MRI data using the joint and individual variation explained (JIVE) approach. We investigate two functional forms linking the longitudinal and survival processes. We adopt the Markov chain Monte Carlo (MCMC) method to obtain posterior samples. We establish a dynamic prediction framework that predicts longitudinal trajectories and the probability of dementia occurrence. The simulation study with various sample sizes and event rates supports the validity of the method. We apply the MFMM-LMRI to the motivating ADNI study and conclude that additional ApoE-<i>ϵ</i>4 alleles and a higher latent disease profile are associated with a higher risk of dementia onset. We detect a significant association between the longitudinal MRI data and the survival outcome. The instantaneous model with longitudinal MRI data has the best fitting and predictive performance.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 1","pages":"505-528"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12206078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LOW-RANK LONGITUDINAL FACTOR REGRESSION WITH APPLICATION TO CHEMICAL MIXTURES.","authors":"Glenn Palmer, Amy H Herring, David B Dunson","doi":"10.1214/24-aoas1988","DOIUrl":"https://doi.org/10.1214/24-aoas1988","url":null,"abstract":"<p><p>Developmental epidemiology commonly focuses on assessing the association between multiple early life exposures and childhood health. Statistical analyses of data from such studies focus on inferring the contributions of individual exposures, while also characterizing time-varying and interacting effects. Such inferences are made more challenging by correlations among exposures, nonlinearity, and the curse of dimensionality. Motivated by studying the effects of prenatal bisphenol A (BPA) and phthalate exposures on glucose metabolism in adolescence using data from the ELEMENT study, we propose a low-rank longitudinal factor regression (LowFR) model for tractable inference on flexible longitudinal exposure effects. LowFR handles highly-correlated exposures using a Bayesian dynamic factor model, which is fit jointly with a health outcome via a novel factor regression approach. The model collapses on simpler and intuitive submodels when appropriate, while expanding to allow considerable flexibility in time-varying and interaction effects when supported by the data. After demonstrating LowFR's effectiveness in simulations, we use it to analyze the ELEMENT data and find that diethyl and dibutyl phthalate metabolite levels in trimesters 1 and 2 are associated with altered glucose metabolism in adolescence.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 1","pages":"769-797"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12013532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"INFERRING SYNERGISTIC AND ANTAGONISTIC INTERACTIONS IN MIXTURES OF EXPOSURES.","authors":"Shounak Chattopadhyay, Stephanie M Engel, David Dunson","doi":"10.1214/24-aoas1948","DOIUrl":"10.1214/24-aoas1948","url":null,"abstract":"<p><p>There is abundant interest in assessing the joint effects of multiple exposures on human health. This is often referred to as the mixtures problem in environmental epidemiology and toxicology. Classically, studies have examined the adverse health effects of different chemicals one at a time, but there is concern that certain chemicals may act together to amplify each other's effects. Such amplification is referred to as <i>synergistic</i> interaction, while chemicals that inhibit each other's effects have <i>antagonistic</i> interactions. Current approaches for assessing the health effects of chemical mixtures do not explicitly consider synergy or antagonism in the modeling, instead focusing on either parametric or unconstrained nonparametric dose response surface modeling. The parametric case can be too inflexible, while nonparametric methods face a curse of dimensionality that leads to overly wiggly and uninterpretable surface estimates. We propose a Bayesian approach that decomposes the response surface into additive main effects and pairwise interaction effects and then detects synergistic and antagonistic interactions. Variable selection decisions for each interaction component are also provided. This Synergistic Antagonistic Interaction Detection (SAID) framework is evaluated relative to existing approaches using simulation experiments and an application to data from NHANES.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 1","pages":"169-190"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12393835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HETEROGENEOUS TREATMENT AND SPILLOVER EFFECTS UNDER CLUSTERED NETWORK INTERFERENCE.","authors":"Falco J Bargagli-Stoffi, Costanza Tortú, Laura Forastiere","doi":"10.1214/24-aoas1913","DOIUrl":"10.1214/24-aoas1913","url":null,"abstract":"<p><p>The bulk of causal inference studies rule out the presence of interference between units. However, in many real-world scenarios, units are interconnected by social, physical, or virtual ties, and the effect of the treatment can spill from one unit to other connected individuals in the network. In this paper, we develop a machine learning method that uses tree-based algorithms and a Horvitz-Thompson estimator to assess the heterogeneity of treatment and spillover effects with respect to individual, neighborhood, and network characteristics in the context of clustered networks and interference within clusters. The proposed network causal tree (NCT) algorithm has several advantages. First, it allows the investigation of the heterogeneity of the treatment effect, avoiding potential bias due to the presence of interference. Second, understanding the heterogeneity of both treatment and spillover effects can guide policymakers in scaling up interventions, designing targeting strategies, and increasing cost-effectiveness. We investigate the performance of our NCT method using a Monte Carlo simulation study and illustrate its application to assess the heterogeneous effects of information sessions on the uptake of a new weather insurance policy in rural China.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 1","pages":"28-55"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12245184/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144610248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}