Biostatistics最新文献_第2页

Statistical inference for high-dimensional generalized estimating equations. 高维广义估计方程的统计推断。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag013

Lu Xia, Ali Shojaie

{"title":"Statistical inference for high-dimensional generalized estimating equations.","authors":"Lu Xia, Ali Shojaie","doi":"10.1093/biostatistics/kxag013","DOIUrl":"10.1093/biostatistics/kxag013","url":null,"abstract":"Regression analysis of correlated data, where multiple correlated responses are recorded on the same unit, is ubiquitous in many scientific areas. With the advent of new technologies, in particular high-throughput omics profiling assays, such correlated data increasingly consist of a large number of variables compared with the available sample size. Motivated by recent longitudinal proteomics studies of COVID-19, we propose a novel inference procedure for linear functionals of high-dimensional regression coefficients in generalized estimating equations, which are widely used to analyze correlated data. Our estimator for this more general inferential target, obtained via constructing projected estimating equations, is shown to be asymptotically normally distributed under mild regularity conditions. We also introduce a data-driven cross-validation procedure to select the tuning parameter for estimating the projection direction, which is not addressed in the existing procedures. We illustrate the utility of the proposed procedure in providing confidence intervals for associations of individual proteins and severe COVID risk scores obtained based on high-dimensional proteomics data, and demonstrate its robust finite-sample performance, especially in estimation bias and confidence interval coverage, via extensive simulations.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A data integration framework to improve the efficiency of survival analysis by leveraging multiple external studies with multifaceted heterogeneity. 一个数据集成框架，通过利用具有多方面异质性的多个外部研究来提高生存分析的效率。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag035

Daxuan Deng, Chixiang Chen, Lijun Zhang, Vernon M Chinchilli, Ming Wang

{"title":"A data integration framework to improve the efficiency of survival analysis by leveraging multiple external studies with multifaceted heterogeneity.","authors":"Daxuan Deng, Chixiang Chen, Lijun Zhang, Vernon M Chinchilli, Ming Wang","doi":"10.1093/biostatistics/kxag035","DOIUrl":"10.1093/biostatistics/kxag035","url":null,"abstract":"In survival analysis, integrating multiple data sources holds significant potential to increase event counts, improve statistical efficiency, and enhance scientific discovery. However, challenges arise when external datasets differ in data forms, distributions, and covariate availability. We propose a novel learning framework that strengthens statistical inference of internal Cox regression coefficients through efficient data integration. Our framework is robust integration and accommodates multifaceted data heterogeneity through transformation, working models, penalized approach, and weighting that adaptively selects homogeneous components. The resulting estimators are consistent, asymptotically normal, more efficient than internal-only estimators, and robust to working model misspecification and data heterogeneity. Simulation studies confirm our framework's favorable properties. We applied our method to study dementia risk among black individuals in the Religious Orders Study and the Rush Memory and Aging Project, integrating information from external cohorts, including the Alzheimer's Disease Neuroimaging Initiative and the National Alzheimer's Coordinating Center. The analysis identified key risk factors with improved efficiency and demonstrated clinical relevance.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13645435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149129245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-augmented multistate modeling for chronic disease processes using cross-sectional studies: application to HPV and cervical precancer. 使用横断面研究的慢性疾病过程数据增强多状态建模：应用于HPV和宫颈癌前病变。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag022

Fangya Mao, Nicole G Campos, Li C Cheung

{"title":"Data-augmented multistate modeling for chronic disease processes using cross-sectional studies: application to HPV and cervical precancer.","authors":"Fangya Mao, Nicole G Campos, Li C Cheung","doi":"10.1093/biostatistics/kxag022","DOIUrl":"10.1093/biostatistics/kxag022","url":null,"abstract":"Understanding the population-level natural history of human papillomavirus (HPV) infection and cervical precancer is essential for informing effective prevention strategies. Multistate models provide a principled framework for characterizing disease progression by modeling transitions among discrete health states. However, intensity-based multistate analyses typically require longitudinal data, which are often costly and logistically difficult to obtain, whereas cross-sectional data are more widely available, particularly in resource-limited settings. We develop a semi-Markov multistate model that captures HPV infection dynamics and cervical precancer progression, including recurrent transitions between HPV-infected and uninfected states. To enable estimation from cross-sectional data, we propose a two-step approach that combines local cross-sectional observations with external information on transferable transition intensities. First, biologically driven transition intensities are estimated from external data under a transferability assumption. Second, these estimates are incorporated into an approximate likelihood to infer population-specific transition intensities shaped by local epidemiological and behavioral factors. Simulation studies and applications to 2 populations demonstrate the performance of the proposed method and its practical utility.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13351745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148426702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Shortcomings of deep learning for distributional predictors: a note. 分布预测器深度学习的缺点：注释。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxaf051

Bonnie B Smith, Abhirup Datta, Brian Caffo

{"title":"Shortcomings of deep learning for distributional predictors: a note.","authors":"Bonnie B Smith, Abhirup Datta, Brian Caffo","doi":"10.1093/biostatistics/kxaf051","DOIUrl":"10.1093/biostatistics/kxaf051","url":null,"abstract":"A number of domains in biomedical research use data with a large number of predictors all representing the same type of measurement. Often, an important summary is the within-person distribution of these predictors. Here we focus on settings where the mean relationship between outcome and predictors is fully captured by this distribution and, more generally, on problems where the goal is to learn a mapping that is invariant under permutations of the input vector. We compare unstructured neural networks, which do not explicitly incorporate the permutation invariance property, versus networks that we call ordered predictors neural networks. We show in simulations that the unstructured deep learning approach can yield higher prediction errors, compared to the approach that explicitly leverages the invariance to simplify the learning task. Additionally, in the context of neural Bayes estimation, in which neural networks are used to construct point estimators, we show that ordered predictors neural networks can yield substantially more precise estimators. We therefore recommend that, when permutation invariance is known or suspected to hold, investigators use a learning or statistical modeling approach that can leverage the invariance, rather than an unstructured deep learning approach.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12815889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146004720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multilevel functional quantile principal component analysis. 多级功能分位数主成分分析。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag017

Álvaro Méndez-Civieta, Ying Wei, Jeff Goldsmith

{"title":"Multilevel functional quantile principal component analysis.","authors":"Álvaro Méndez-Civieta, Ying Wei, Jeff Goldsmith","doi":"10.1093/biostatistics/kxag017","DOIUrl":"10.1093/biostatistics/kxag017","url":null,"abstract":"Accelerometer studies typically include repeated 24-h observations over several days and more recently have collected information on participants for weeks, months, or years. Meanwhile, there is growing evidence to suggest that components of physical activity beyond average or expected values are important for quantifying and understanding activity. Motivated by these emerging aspects, we introduce Multilevel Functional Quantile Principal Component Analysis (MFQPCA), a novel dimension-reduction technique that extends Functional Quantile Principal Component Analysis to hierarchical functional data. We examine accelerometry data collected for 4 to 9 months from congestive heart failure patients, where daily activity profiles are nested within individuals and exhibit substantial variability: participants often differ in the timing and intensity of physical activity behaviors across days and from each other, and capturing information beyond expected-value curves is necessary for a robust quantification of diurnal patterns. To this end, MFQPCA decomposes quantile-specific variability into between-participant and within-participant components, estimating shared patterns and corresponding scores at each level and quantile of interest. Our method robustly captures complex distributional features in hierarchical data, disentangles between- and within-participant sources of variability at different quantile levels, and facilitates the study of longitudinal changes in these distributions. We produce participant- and participant-day-level 10%, 30%, 50%, 70% and 90% quantile curves over 24 h, revealing that day-to-day variability dominates sedentary periods, whereas between-participant differences grow in vigorous activity. An efficient alternating minimization algorithm is implemented in the open-source R package FunQ, making MFQPCA readily applicable to a wide range of hierarchical functional data settings.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13268125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148260729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-dimensional test for one-sided hypotheses. 单侧假设的高维检验。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag016

Rongrong Wang, Shrabanti Chowdhury, Hanwen Huang, Xiaoling Wang, Deepak N Ayyala, Santu Ghosh

{"title":"High-dimensional test for one-sided hypotheses.","authors":"Rongrong Wang, Shrabanti Chowdhury, Hanwen Huang, Xiaoling Wang, Deepak N Ayyala, Santu Ghosh","doi":"10.1093/biostatistics/kxag016","DOIUrl":"10.1093/biostatistics/kxag016","url":null,"abstract":"Advances in data acquisition technologies and computational resources have significantly improved the analysis of large datasets across various domains. These datasets often feature a high number of variables but a limited number of observations, commonly referred to as \"high-dimensional data.\" Analyzing such data requires innovative statistical methods to further scientific insights. Mean vector testing plays a vital role in many scientific investigations. Although considerable research has been devoted to developing efficient methods for two-sided mean vector tests in high-dimensional data-such as those used to identify differentially expressed genes-one-sided high-dimensional mean vector tests have not been as thoroughly explored. These tests are particularly valuable for detecting significantly up-regulated or down-regulated gene sets within predefined groups. Addressing this need, we introduce a new method: the Sum Max-Component (SMC) test. We have explored the asymptotic behavior of the SMC test statistic as both the sample size and the data dimensions grow indefinitely. SMC test method has undergone extensive validation in finite sample scenarios, where it has proven to be effective, showing competitive rates for both type I error and power. To illustrate its practical applications, we have employed the SMC test in a gene set enrichment analysis on the proteomic data of ovarian cancer patients from the National Cancer Institute's CPTAC study, underscoring its potential value in ovarian cancer biology. Multivariate one-sided test, High-dimensional data, Gaussian-type tails, Exponential-type tails, enrichment analysis, high-grade serous ovarian cancer.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148058317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A nutritionally informed model for Bayesian variable selection with metabolite response variables. 具有代谢反应变量的贝叶斯变量选择的营养信息模型。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag024

Dylan Clark-Boucher, Brent A Coull, Harrison T Reeder, Fenglei Wang, Qi Sun, Jacqueline R Starr, Kyu Ha Lee

{"title":"A nutritionally informed model for Bayesian variable selection with metabolite response variables.","authors":"Dylan Clark-Boucher, Brent A Coull, Harrison T Reeder, Fenglei Wang, Qi Sun, Jacqueline R Starr, Kyu Ha Lee","doi":"10.1093/biostatistics/kxag024","DOIUrl":"10.1093/biostatistics/kxag024","url":null,"abstract":"Understanding the pathways through which diet affects human metabolism is a central task in nutritional epidemiology. This article proposes novel methodology to identify food items associated with blood metabolites in 2 cohorts of healthcare professionals. We analyze 244 metabolites characterized by statistical complexities that include skewness, left-censoring, and structural missingness. Though existing methods can address such factors in low-dimensional settings, they cannot exploit the nutritional or statistical relationships among the 30 considered food intake variables, and they are unsuitable for performing high-dimensional inference. To address these challenges, we develop a novel Bayesian variable selection framework for metabolite response variables based on a skew-normal censored mixture model, while exploiting substantive information on the considered food items via a Markov random field prior. Applying this methodology to the cohort data identifies multiple metabolite-diet associations that are consistent with previous research as well as several potentially novel associations that were not detected using standard methods. The proposed approach is implemented in the R package multimetab, facilitating its use in high-dimensional metabolomic analyses.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13379639/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148474461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SAM-HC: a Bayesian nonparametric construction of hybrid control for randomized clinical trials using external data. SAM-HC：使用外部数据的随机临床试验的贝叶斯非参数混合对照结构。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag003

Dehua Bi, Tianjian Zhou, Wei Zhong, Yuan Ji

引用次数: 0

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference. 面向最优环境政策：任意二部网络干扰下的政策学习。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxaf041

Raphael C Kim, Falco J Bargagli-Stoffi, Kevin L Chen, Rachel C Nethery

{"title":"Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.","authors":"Raphael C Kim, Falco J Bargagli-Stoffi, Kevin L Chen, Rachel C Nethery","doi":"10.1093/biostatistics/kxaf041","DOIUrl":"10.1093/biostatistics/kxaf041","url":null,"abstract":"The substantial effect of air pollution on cardiovascular disease and mortality burdens is well-established. Emissions-reducing interventions on coal-fired power plants-a major source of hazardous air pollution-have proven to be an effective, but costly, strategy for reducing pollution-related health burdens. Targeting the power plants that achieve maximum health benefits while satisfying realistic cost constraints is challenging. The primary difficulty lies in quantifying the health benefits of intervening at particular plants. This is further complicated because interventions are applied on power plants, while health impacts occur in potentially distant communities, a setting known as bipartite network interference (BNI). In this paper, we introduce novel policy learning methods based on Q- and A-Learning to determine the optimal policy under arbitrary BNI. We derive asymptotic properties and demonstrate finite sample efficacy in simulations. We apply our novel methods to a comprehensive dataset of Medicare claims, power plant data, and pollution transport networks. Our goal is to determine the optimal strategy for installing power plant scrubbers to minimize ischemic heart disease (IHD) hospitalizations under various cost constraints. We find that annual IHD hospitalization rates could be reduced in a range from 23.37 to 55.30 per 10,000 person-years through optimal policies under different cost constraints.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13268124/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148260682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensitivity analysis for contamination in egocentric-network randomized trials with interference. 具有干扰的自我中心网络随机试验污染敏感性分析。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag031

Bar Weinstein, Daniel Nevo

{"title":"Sensitivity analysis for contamination in egocentric-network randomized trials with interference.","authors":"Bar Weinstein, Daniel Nevo","doi":"10.1093/biostatistics/kxag031","DOIUrl":"10.1093/biostatistics/kxag031","url":null,"abstract":"Egocentric-network randomized trials (ENRTs) are increasingly used to estimate causal effects under interference when measuring complete sociocentric network data is infeasible. ENRTs rely on egocentric network sampling, where a set of egos is first sampled, and each ego recruits a subset of its neighbors as alters. Treatments are then randomized across egos. While the observed ego-networks are disjoint by design, the underlying population network may contain edges connecting them, leading to contamination. Under a design-based framework, we show that the Horvitz-Thompson estimators of direct and indirect effects are biased whenever contamination is present. To address this, we derive bias-corrected estimators and propose a novel sensitivity analysis framework based on sensitivity parameters representing the probability or expected number of missing edges. This framework is implemented via both grid sensitivity analysis and probabilistic bias analysis, providing researchers with a flexible tool to assess the robustness of the causal estimators to contamination. We apply our methodology to the HIV Prevention Trials Network 037 study, finding that ignoring contamination may lead to underestimation of indirect effects and overestimation of direct effects.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13461678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148714726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0