Annals of Applied Statistics最新文献

{"title":"FAST VARIABLE SELECTION FOR DISTRIBUTIONAL REGRESSION WITH APPLICATION TO CONTINUOUS GLUCOSE MONITORING DATA.","authors":"Alexander Coulter, Rashmi N Aurora, Naresh M Punjabi, Irina Gaynanova","doi":"10.1214/25-aoas2038","DOIUrl":"10.1214/25-aoas2038","url":null,"abstract":"<p><p>With the growing prevalence of diabetes and the associated public health burden, it is crucial to identify modifiable factors that could improve patients' glycemic control. In this work, we seek to examine associations between medication usage, concurrent comorbidities, and glycemic control, utilizing data from continuous glucose monitors (CGMs). CGMs provide high-frequency interstitial glucose measurements, but reducing data to simple statistical summaries is common in clinical studies, resulting in substantial information loss. Recent advancements in the Fréchet regression framework allow to utilize more information by treating the full distributional representation of CGM data as the response, while sparsity regularization enables variable selection. However, the methodology does not scale to large datasets. Crucially, rigorous inference is not possible because the asymptotic behavior of the underlying estimates is unknown, while the application of resampling-based inference methods is computationally infeasible. We develop a new algorithm for sparse distributional regression by deriving a new explicit characterization of the gradient and Hessian of the underlying objective function, while also utilizing rotations on the sphere to perform feasible updates. The updated method is up to 10000+ fold faster than the original approach, opening the door for applying sparse distributional regression to large-scale datasets and enabling previously unattainable resampling-based inference. We combine our algorithm with stability selection to perform variable selection inference on CGM data from patients with type 2 diabetes and obstructive sleep apnea. We find a significant association between sulfonylurea medication and glucose variability without evidence of association with glucose mean. We also find that overnight oxygen desaturation variability has a stronger association with glucose regulation than overall oxygen desaturation levels.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 3","pages":"2105-2128"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12700301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145758247","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":"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":"INTEGRATIVE ECOLOGICAL REGRESSION ANALYSIS OF U.S. COUNTY AND STATE LEVEL COVID-19 DEATH DATA FOR STUDYING HEALTH DISPARITY ASSOCIATIONS.","authors":"Daniel Li, Xihong Lin","doi":"10.1214/25-aoas2055","DOIUrl":"10.1214/25-aoas2055","url":null,"abstract":"<p><p>It is of substantial interest to study health disparity associations with COVID-19 death rates. Although high-quality individual-level COVID-19 epidemiological data have been difficult to collect on a national scale, all United States (U.S.) counties have reported total COVID-19 death counts. A standard ecological analysis would then regress county total death counts by county-level covariates such as age, sex, and race percentages. However, such an analysis is limited by ecological bias and fallacy in which estimated county-level associations are different from individual-level associations. Fortunately, state-level age, sex, and race specific COVID-19 death counts are also available for all U.S. states, so this information can be integrated with county-level data for more informative ecological analyses. We propose an approximate log-linear random effects model to jointly model county-level total death counts and state-level age, sex, and race specific death counts. We then develop a penalized composite log-likelihood method for parameter estimation and perform simulation studies to evaluate our proposed approach. Lastly, we analyze COVID-19 death data from the entire U.S., show how incorporating state-level counts can prevent ecological bias and fallacy, and illustrate the heterogeneity in health disparity associations across different U.S. states.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 3","pages":"2320-2338"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12900166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146203683","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":"AVERAGED PREDICTION MODELS (APM): IDENTIFYING CAUSAL EFFECTS IN CONTROLLED PRE-POST SETTINGS WITH APPLICATION TO GUN POLICY.","authors":"Thomas Leavitt, Laura A Hatfield","doi":"10.1214/25-aoas2011","DOIUrl":"10.1214/25-aoas2011","url":null,"abstract":"<p><p>To investigate causal impacts, many researchers use controlled pre-post designs that compare over-time differences between a population exposed to a policy change and an unexposed comparison group. However, researchers using these designs often disagree about the \"correct\" specification of the causal model, perhaps most notably in analyses to identify the effects of gun policies on crime. To help settle these model specification debates, we propose a general identification framework that unifies a variety of models researchers use in practice. In this framework, which nests \"brand name\" designs like Difference-in-Differences as special cases, we use models to predict untreated outcomes and then correct the treated group's predictions using the comparison group's observed prediction errors. Our point identifying assumption is that treated and comparison groups would have equal prediction errors (in expectation) under no treatment. To choose among candidate models, we propose a data-driven procedure based on models' robustness to violations of this point identifying assumption. Our selection procedure averages over candidate models, weighting by each model's posterior probability of being the most robust given its differential average prediction errors in the pre-period. This approach offers a way out of debates over the \"correct\" model by choosing on robustness instead and has the desirable property of being feasible in the \"locked box\" of pre-intervention data only. We apply our methodology to the gun policy debate, focusing specifically on Missouri's 2007 repeal of its permit-to-purchase law, and provide an R package (apm) for implementation.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 3","pages":"1826-1846"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12633725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145589860","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":"CONTRASTIVE LINEAR REGRESSION.","authors":"Boyang Zhang, Sarah Nyquist, Andrew Jones, Barbara E Engelhardt, Didong Li","doi":"10.1214/24-aoas1977","DOIUrl":"10.1214/24-aoas1977","url":null,"abstract":"<p><p>Contrastive dimension reduction methods have been developed for case-control study data to identify variation that is enriched in the foreground (case) data <math><mi>X</mi></math> relative to the background (control) data <math><mi>Y</mi></math> . Here we develop contrastive regression for the setting where there is a response variable <math><mi>r</mi></math> associated with each foreground observation. This situation occurs frequently when, for example, the unaffected controls do not have a disease grade or intervention dosage, but the affected cases have a disease grade or intervention dosage, as in autism severity, solid tumors stages, polyp sizes, or warfarin dosages. Our contrastive regression model captures shared low-dimensional variation between the predictors in the case and control groups and then explains the case-specific response variables through the variance that remains in the predictors after shared variation is removed. We show that, in one single-cell RNA sequencing dataset on cellular differentiation in chronic rhinosinusitis with and without nasal polyps and in another single-nucleus RNA sequencing dataset on autism severity in postmortem brain samples from donors with and without autism, our contrastive linear regression performs feature ranking and identifies biologically-informative predictors associated with response that cannot be identified using other approaches.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 3","pages":"1868-1883"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12692120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745602","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":"NETWORK-BASED MODELING OF EMOTIONAL EXPRESSIONS FOR MULTIPLE CANCERS VIA A LINGUISTIC ANALYSIS OF AN ONLINE HEALTH COMMUNITY.","authors":"Xinyan Fan, Mengque Liu, Shuangge Ma","doi":"10.1214/25-aoas2047","DOIUrl":"10.1214/25-aoas2047","url":null,"abstract":"<p><p>The diagnosis and treatment of cancer can evoke a variety of adverse emotions. Online health communities (OHCs) provide a safe platform for cancer patients and those closely related to express emotions without fear of judgement or stigma. In the literature, linguistic analysis of OHCs is usually limited to a single disease and based on methods with various technical limitations. In this article we analyze posts from September 2003 to September 2022 on eight cancers that are publicly available at the American Cancer Society's Cancer Survivors Network (CSN). We propose a novel network analysis technique based on low-rank matrices. The proposed approach decomposes the emotional expression semantic networks into an across-cancer time-independent component (which describes the \"baseline\" that is shared by multiple cancers), a cancer-specific time-independent component (which describes cancer-specific properties), and an across-cancer time-dependent component (which accommodates temporal effects on multiple cancer communities). For the second and third components, respectively, we consider a novel clustering structure and a change point structure. A penalization approach is developed, and its theoretical and computational properties are carefully established. The analysis of the CSN data leads to sensible networks and deeper insights into emotions for cancer overall and specific cancer types.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 3","pages":"2218-2236"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12525517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309914","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":"BIOMARKER DETECTION FOR DISEASE CLASSIFICATION IN LONGITUDINAL MICROBIOME DATA.","authors":"Chao Cheng, Hanteng Ma, Yujie Zhong, Anne-Catrin Uhlemann, Xingdong Feng, Jianhua Hu","doi":"10.1214/24-aoas1995","DOIUrl":"10.1214/24-aoas1995","url":null,"abstract":"<p><p>The microbiome has been found to have a close relationship with human health. Advancements in sequencing technologies have enabled in-depth studies of microbial communities and their associations with various diseases. When analyzing microbiome data, it is common to perform compositional scale normalization to ensure statistical validity. This requires special treatment to address the unique characteristics of microbiome data. Furthermore, biomedical studies often involve repeated measurements of microbial samples, which adds complexity to the data analysis. In this paper we focus on a liver transplant microbiome study. The main objective is to investigate the association between the colonization status of multidrug-resistant bacteria (MDRB) and the longitudinal microbial abundance profile. To accomplish this, we employ a regularized functional logistic regression model in our analysis. Specifically, we utilize the log-contrast model with a low-rank approximation to handle the compositional covariates and nonconvex penalties to select the important components in the covariate space. We propose an efficient estimation algorithm and establish the oracle property of the estimator. We name this new development as Functional Compositional data Quadratic Method (FCQM). We demonstrate the promise of the proposed method with extensive simulation studies and the liver transplant application.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 2","pages":"943-966"},"PeriodicalIF":1.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13078646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693648","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}