Biometrics最新文献_第6页

DAG trend filtering for genomic denoising via higher-order Bayesian networks and DAG shrinkage processes. 基于高阶贝叶斯网络和DAG收缩过程的DAG趋势滤波基因组去噪。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag150

Weixuan Zhu, Fan Liao, Yang Ni

引用次数: 0

Estimating covariate effects on functional connectivity using voxel-level fMRI data. 使用体素级fMRI数据估计功能连通性的协变量效应。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag151

Wei Zhao, Brian J Reich, Emily C Hector

{"title":"Estimating covariate effects on functional connectivity using voxel-level fMRI data.","authors":"Wei Zhao, Brian J Reich, Emily C Hector","doi":"10.1093/biomtc/ujag151","DOIUrl":"https://doi.org/10.1093/biomtc/ujag151","url":null,"abstract":"Functional connectivity (FC) analysis of resting-state fMRI data provides a framework for characterizing brain networks and their association with participant-level covariates. Due to the high dimensionality of neuroimaging data, standard approaches often average signals within regions of interest (ROIs), which ignores the underlying spatiotemporal dependence among voxels and can lead to biased or inefficient inference. We propose to use a summary statistic-the empirical voxel-wise correlations between ROIs-and, crucially, model the complex covariance structure among these correlations through a new positive definite covariance function. Building on this foundation, we develop a computationally efficient two-step estimation procedure that enables scalable statistical inference on covariate effects on region-level connectivity. Simulation studies show calibrated uncertainty quantification and substantial gains in validity of the statistical inference over the standard averaging method. With data from the Autism Brain Imaging Data Exchange, we show that autism spectrum disorder is associated with altered FC between attention-related ROIs after adjusting for age and gender. The proposed framework offers an interpretable and statistically rigorous approach to estimation of covariate effects on FC suitable for large-scale neuroimaging studies.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148944201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Flexible copula-based variable selection for interval-censored semi-competing risks data: application to aging research. 区间截尾半竞争风险数据的柔性copula变量选择：在老龄化研究中的应用。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag166

Yuyao Zhang, Naijia Fan, Huiping Zheng, Ying Ding, Tao Sun

{"title":"Flexible copula-based variable selection for interval-censored semi-competing risks data: application to aging research.","authors":"Yuyao Zhang, Naijia Fan, Huiping Zheng, Ying Ding, Tao Sun","doi":"10.1093/biomtc/ujag166","DOIUrl":"https://doi.org/10.1093/biomtc/ujag166","url":null,"abstract":"Semi-competing risks data, where a non-terminal event (e.g., disability) may be censored by a terminal event (e.g., death), are common in aging research. These data pose unique analytical challenges when the non-terminal event is interval-censored, and a large number of covariates have distinct effects on each outcome. Existing variable selection methods typically focus on right-censored data and often rely on computationally intensive tuning procedures, limiting their utility in aging studies. We propose a flexible and computationally efficient copula-based variable selection framework for interval-censored semi-competing risks data. The method incorporates three key components: (1) a two-parameter copula that flexibly captures both upper and lower tail dependence between non-terminal and terminal events; (2) semiparametric transformation models for the marginal distributions, accommodating common specifications such as proportional hazards and proportional odds; and (3) a tuning-free variable selection procedure based on minimizing an approximated information criterion. To enable high-dimensional estimation, we develop a new coordinate-wise optimization procedure combined with sieve estimation, which decomposes the high-dimensional problem into low-dimensional subproblems. The asymptotic properties of the proposed estimators are also established. Simulation studies demonstrate that the proposed method achieves accurate variable selection with substantial computational gains. Applied to the Chinese Longitudinal Healthy Longevity and Happy Family Study, it identifies key comorbidities and lifestyle factors associated with disability and mortality, offering novel insights into aging trajectories. The framework provides an interpretable and scalable tool for aging research involving chronic functional decline and intermittent follow-up.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149057382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Optimal subsampling methods for large clustered imbalanced data based on generalized estimating equations. 基于广义估计方程的大型聚类不平衡数据的最优次抽样方法。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag163

Tingting Yu, Sehwan Kim, Rui Wang

{"title":"Optimal subsampling methods for large clustered imbalanced data based on generalized estimating equations.","authors":"Tingting Yu, Sehwan Kim, Rui Wang","doi":"10.1093/biomtc/ujag163","DOIUrl":"https://doi.org/10.1093/biomtc/ujag163","url":null,"abstract":"Generalized estimating equations (GEEs) are widely used for modeling clustered data while accounting for within-cluster correlations. However, fitting GEEs on massive datasets can be computationally prohibitive. Subsampling offers a practical solution, but efficient subsampling strategies for large clustered data, particularly with rare events, remain underexplored. Simple random subsampling can lead to a substantial loss of rare-event information, resulting in inefficient estimation, computational instability, and increased finite-sample bias. Additionally, intra-cluster correlation complicates inference on subsampled data, requiring methods that preserve correlation structure while adjusting for unequal sampling probabilities. To address these challenges, we develop an individual-level sampling scheme and derive optimal subsampling probabilities based on GEEs. We propose a subsampling-based weighted GEE (SaWGEE) estimator that efficiently approximates the full-data GEE estimator and establish its asymptotic properties as the number or size of clusters grows. The optimal subsampling probabilities minimize the asymptotic mean-square-error or its upper bound of the SaWGEE estimator, conditional on observed data, under an independent or exchangeable working correlation structure. Simulation studies evaluate the performance of the SaWGEE estimator against conventional subsampling strategies, such as random or 1:1 case-control sampling, and the standard full-data GEEs when feasible. We apply the proposed method to the 2022 CDC survey data, where observations are clustered by state, to construct a risk-adjustment model for heart attack incidence.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149057539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

One-at-a-time knockoffs. 一次性的仿冒品。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag119

Charlie K Guan, Zhimei Ren, Daniel W Apley

{"title":"One-at-a-time knockoffs.","authors":"Charlie K Guan, Zhimei Ren, Daniel W Apley","doi":"10.1093/biomtc/ujag119","DOIUrl":"https://doi.org/10.1093/biomtc/ujag119","url":null,"abstract":"We propose one-at-a-time knockoffs (OATK), a new methodology for detecting important explanatory variables in linear regression problems (including ridge and lasso) while controlling the false discovery rate (FDR). For each explanatory variable, OATK generates a knockoff design matrix that preserves the Gram matrix by replacing one-at-a-time only the single corresponding column of the original design matrix. OATK is a substantial relaxation and simplification of the knockoff filter, which simultaneously generates all columns of the knockoff design matrix to satisfy a much larger set of constraints. To test each variable's importance, statistics are then constructed by comparing the original versus knockoff coefficients. Under a mild correlation assumption on the original design matrix, we prove that OATK asymptotically controls the FDR at any desired level. Moreover, numerical results across a variety of simulation examples and a real genetics data set demonstrate that OATK provides good FDR control and regularly achieves (often substantially) higher power than existing approaches. In particular, in a genome-wide association study, OATK yielded more uniquely discovered genetic mutations associated with virological response to HIV therapeutics that were corroborated in clinical studies. Generating knockoffs one-at-a-time also has substantial computational advantages and facilitates additional enhancements, such as conditional calibration or derandomization, to further improve power and consistency of FDR control.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 regularized multi-state model for covariate selection with interval-censored survival data. 区间截尾生存数据协变量选择的正则化多状态模型。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag075

Ariane Bercu, Agathe Guilloux, Cécile Proust-Lima, Hélène Jacqmin-Gadda

{"title":"A regularized multi-state model for covariate selection with interval-censored survival data.","authors":"Ariane Bercu, Agathe Guilloux, Cécile Proust-Lima, Hélène Jacqmin-Gadda","doi":"10.1093/biomtc/ujag075","DOIUrl":"10.1093/biomtc/ujag075","url":null,"abstract":"In population-based cohorts, disease diagnoses are typically censored by intervals as made during scheduled follow-up visits. The exact disease onset time is thus unknown, and in the presence of semi-competing risk of death, subjects may also die in between two visits before any diagnosis can be made. Illness-death models can be used to handle uncertainty about illness timing and the possible absence of diagnosis due to death. However, they are so far limited in the number of covariates. We developed a regularized estimation procedure for illness-death models with interval-censored illness diagnosis that performs variable selection in the case of high-dimensional predictors. We considered a proximal gradient hybrid algorithm maximizing the regularized likelihood with an elastic-net penalty. The algorithm simultaneously estimates the regression parameters of the three transitions under proportional transition intensities with transition-specific penalty parameters determined in an outer gridsearch. The algorithm, implemented in the R package HIDeM, shows high performances in predicting illness probability, as well as correct selection of transition-specific risk factors across different simulation scenarios. In comparison, the cause-specific competing risk model neglecting interval-censoring systematically showed worse predictive ability and tended to select irrelevant illness predictors, originally associated with death. Applied to the population-based cohort Three-City, the method identified predictors of clinical dementia onset among a large set of brain imaging, cognitive, and clinical markers.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147980383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

TITE-safety: a robust time-to-event safety monitoring approach for clinical trials. ti -safety：一种用于临床试验的可靠的时间-事件安全监测方法。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag097

Michael J Martens, Qinghua Lian, Brent R Logan

{"title":"TITE-safety: a robust time-to-event safety monitoring approach for clinical trials.","authors":"Michael J Martens, Qinghua Lian, Brent R Logan","doi":"10.1093/biomtc/ujag097","DOIUrl":"10.1093/biomtc/ujag097","url":null,"abstract":"Safety evaluation is an essential component of clinical trials. To protect study participants, these studies often implement safety stopping rules that will halt the trial if an excessive number of toxicity events occur. Existing safety monitoring methods usually treat these events as binary outcomes. A strategy that instead handles these as time-to-event (TITE) endpoints can offer higher power and a reduced time to signal of excess risk, but must manage additional complexities including censoring and competing risks. We propose the TITE-safety approach for safety monitoring, which incorporates TITE information while handling repeated analyses, censored observations, and competing risks appropriately. This strategy is applied to develop stopping rules using score tests, Bayesian beta-extended binomial models, and sequential probability ratio tests. Operating characteristics of these methods are studied via simulation for common phase 2 and 3 trial scenarios. Across simulation settings, the proposed techniques offer reductions in expected toxicities of 20% or more compared to binary data methods and maintain the type I error rate near the nominal level for various event time distributions. These methods are demonstrated through a redesign of the safety monitoring scheme for Blood and Marrow Transplant Clinical Trials Network 0601, a single arm, phase 2 trial that evaluated bone marrow transplant as treatment for sickle cell disease. Our R package \"stoppingrule\" offers functions to construct and evaluate these stopping rules, providing valuable tools for trial design to investigators.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13215099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148040867","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}

引用次数: 0

INTACT: a method for integration of longitudinal physical activity data from multiple sources. 一种整合来自多个来源的纵向体育活动数据的方法。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag112

Jingru Zhang, Erjia Cui, Hongzhe Li, Haochang Shou

{"title":"INTACT: a method for integration of longitudinal physical activity data from multiple sources.","authors":"Jingru Zhang, Erjia Cui, Hongzhe Li, Haochang Shou","doi":"10.1093/biomtc/ujag112","DOIUrl":"10.1093/biomtc/ujag112","url":null,"abstract":"Wearable devices and digital phenotyping are increasingly used in observational and interventional studies to measure real-time biosignals such as physical activity. However, integrating and comparing data across studies and cohorts remains challenging due to variability in device types, acquisition protocols, and preprocessing methods. A key challenge is removing unwanted study- or device-specific effects while preserving meaningful biological signals. These difficulties are exacerbated by the longitudinal and within-day correlations inherent in high-resolution time-varying data collected from wearable sensors. To address this, we propose INTACT (INtegration of Time-varying data from weArable sensors for physiCal acTivity), a novel method for harmonizing time-varying physical activity intensity data from accelerometers. INTACT models shared information through common eigenvalues and eigenfunctions while allowing for source-specific scale and rotation adjustments. We apply the proposed method to two real-world applications: (1) integration of accelerometer data from two waves of the National Health and Nutrition Examination Survey (NHANES), measured using different devices and reported in different units; and (2) integration of NHANES accelerometry data with accelerometer and gyroscope measures from commercial devices. Across both applications, INTACT outperforms existing approaches in mitigating source effects while preserving biological variation, enabling more reliable cross-study comparisons of physical activity patterns.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13292648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148263140","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}

引用次数: 0

Measurement error-robust causal inference via constructed instrumental variables. 测量误差-通过构造的工具变量进行稳健的因果推理。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag057

Caleb H Miles, Linda Valeri, Brent Coull

{"title":"Measurement error-robust causal inference via constructed instrumental variables.","authors":"Caleb H Miles, Linda Valeri, Brent Coull","doi":"10.1093/biomtc/ujag057","DOIUrl":"10.1093/biomtc/ujag057","url":null,"abstract":"Measurement error can often be harmful when estimating causal effects. Two scenarios in which this is the case are in the estimation of (a) the average treatment effect when confounders are measured with error, and (b) the natural indirect effect when the exposure and/or confounders are measured with error. Methods adjusting for measurement error typically require external data or knowledge about the measurement error distribution. Here, we propose methodology not requiring any such information. Instead, we show that when the outcome regression is linear in the error-prone variables, consistent estimation of these causal effects can be recovered using constructed instrumental variables (IVs) under certain conditions. These variables, which are functions of only the observed data, behave like IVs for the error-prone variables. Using data from a study of the effects of prenatal exposure to heavy metals on growth and neurodevelopment in Bangladeshi mother-infant pairs, we apply our methodology to estimate (a) the effect of lead exposure on birth length while controlling for maternal protein intake, and (b) lead exposure's role in mediating the effect of maternal protein intake on birth length. Protein intake is calculated from food journal entries, and is suspected to be highly prone to measurement error.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13521038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147697423","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}

引用次数: 0

Rejoinder to the discussion on ''Nonparanormal Adjusted Marginal Inference''. 对“非超自然调整边际推论”讨论的回答。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag069

Susanne Dandl, Torsten Hothorn

引用次数: 0