Biometrics最新文献

Generative estimation of conditional survival function. 条件生存函数的生成估计。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag164

Xingyu Zhou, Wen Su, Changyu Liu, Yuling Jiao, Xingqiu Zhao, Jian Huang

引用次数: 0

Meta-analytical federated surveillance for incubation period with interval- and right-censored data with stochastic variational inference. 用随机变分推理的间隔截尾和右截尾数据进行潜伏期的荟萃分析联合监测。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag172

Daisuke Yoneoka, Takayuki Kawashima, Yuta Tanoue, Shuhei Nomura, Akifumi Eguchi

{"title":"Meta-analytical federated surveillance for incubation period with interval- and right-censored data with stochastic variational inference.","authors":"Daisuke Yoneoka, Takayuki Kawashima, Yuta Tanoue, Shuhei Nomura, Akifumi Eguchi","doi":"10.1093/biomtc/ujag172","DOIUrl":"10.1093/biomtc/ujag172","url":null,"abstract":"Accurate estimation of incubation period is fundamental to surveillance for infectious diseases. This task faces two major challenges: (1) surveillance data are often coarsely observed, comprising interval- and right-censored observations, and (2) patient-level data are frequently fragmented across multiple facilities and cannot be pooled due to privacy constraints. While traditional meta-analysis (MA) aggregates summary statistics, its one-shot nature limits the ability to fit complex hierarchical models. We propose a novel federated Bayesian framework, Federated Stochastic Variational Inference (Fed-SVI), which extends the summary-statistic philosophy of MA into an iterative, model-based federated approach. Our method fits a hierarchical lognormal model, explicitly modeling heterogeneity while estimating a global distribution. To fit this model privately, Fed-SVI iteratively exchanges summary statistics (local variational parameters). Each facility analytically computes these local parameters, rigorously incorporating the likelihood contributions of coarsely-observed data via expectations of the truncated normal distribution. A central server aggregates these parameters using stochastic natural-gradient ascent, enabling efficient information borrowing and mini-batch updates. Through extensive simulations, we demonstrate that Fed-SVI achieves accuracy nearly identical to a fully centralized analysis, while substantially outperforming naive federated or MA methods that discard censored information. We apply Fed-SVI to a distributed COVID-19 line list from 177 facilities, yielding results consistent with centralized estimates. Fed-SVI provides a scalable, accurate, and privacy-preserving solution for robust surveillance in modern data networks.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 4","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149134773","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

Correction to: Group sequential clinical trial designs for composite endpoints incorporating recurrent and terminal events. 校正：包含复发性和终末期事件的复合终点组序贯临床试验设计。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag189

引用次数: 0

Linear projection inference for Individual Variable Importance. 个体变量重要性的线性投影推理。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag183

Xinjie Zhou, Zhengbang Li

{"title":"Linear projection inference for Individual Variable Importance.","authors":"Xinjie Zhou, Zhengbang Li","doi":"10.1093/biomtc/ujag183","DOIUrl":"https://doi.org/10.1093/biomtc/ujag183","url":null,"abstract":"Quantifying how the predictive value of biomarkers varies across patient characteristics is central to precision medicine. While Individual Variable Importance (IVI) offers a rigorous framework for this task, existing non-parametric estimators often prove unstable and inefficient in clinical studies with limited sample sizes ($Nlt 500$). To address this, we propose a Linear Projection IVI framework that prioritizes inferential stability over functional flexibility. We formulate the estimator as a first-order approximation to high-dimensional importance surfaces using linear interaction structures. Theoretical results establish that, even under model misspecification, the estimator converges to a well-defined pseudo-true parameter corresponding to the best linear projection of the underlying IVI function. We derive closed-form expressions for local and global importance, prove $sqrt{N}$-consistency, and introduce a principled \"Gatekeeper\" hypothesis test. This procedure utilizes a robust joint Wald statistic to control Type I error for heterogeneity detection prior to subgroup stratification, effectively preventing spurious discovery. Simulation studies demonstrate superior finite-sample efficiency relative to kernel-based alternatives and robustness to binary outcome misspecification. Applications to NHANES and Pima Indian diabetes datasets illustrate the framework's utility in distinguishing reproducible biological signals from noise in data-constrained settings.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 4","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149317620","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

Improving interpretation of latent class models for diagnostic tests: distinguishing target condition from measurands using directed acyclic graphs. 改进诊断测试的潜类模型的解释：使用有向无环图从测量中区分目标条件。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag185

Nandini Dendukuri, Ian Schiller, Else Margreet Bijker, Michael Libman, Paul Gustafson, Patrick M Bossuyt, Joanna Merckx

{"title":"Improving interpretation of latent class models for diagnostic tests: distinguishing target condition from measurands using directed acyclic graphs.","authors":"Nandini Dendukuri, Ian Schiller, Else Margreet Bijker, Michael Libman, Paul Gustafson, Patrick M Bossuyt, Joanna Merckx","doi":"10.1093/biomtc/ujag185","DOIUrl":"https://doi.org/10.1093/biomtc/ujag185","url":null,"abstract":"In the absence of a perfect diagnostic test for a target condition, one or more imperfect tests may be used to arrive at a clinical diagnosis. Latent class analysis can be used to model data from imperfect tests with the objective of estimating test accuracy and target condition prevalence. Such models typically assume there are 2 latent classes underlying the observed data-target condition positive and target condition negative. However, as we illustrate, this would be an oversimplification when any of the observed tests has a measurand that is different from the target condition. We show how a directed acyclic graph can be used to illustrate the relationships between the relevant variables-the observed imperfect test results, their latent measurands, the latent target condition, covariates affecting observed or latent variables, and sources of conditional dependence. We then show how to determine the underlying latent classes and their labels. Finally, we examine how consideration of the measurand of each test impacts the likelihood and identifiability of the latent class model. Using simulation studies, we show how ignoring measurands, which are distinct from the target condition, can lead to mislabeling of the latent classes. We illustrate the value of the proposed approach by re-analyzing 2 datasets used in previously published latent class analyses of tests for pediatric tuberculosis and leptospirosis.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 4","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149340605","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 Bayesian deep learning framework for brain dynamic functional connectivity linked with cognition. 脑动态功能连接与认知的贝叶斯深度学习框架。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag174

Xinyuan Tian, Yuanshuang Guo, Shengxian Ding, Yifei Zhang, Denise Esserman, Yize Zhao

{"title":"A Bayesian deep learning framework for brain dynamic functional connectivity linked with cognition.","authors":"Xinyuan Tian, Yuanshuang Guo, Shengxian Ding, Yifei Zhang, Denise Esserman, Yize Zhao","doi":"10.1093/biomtc/ujag174","DOIUrl":"10.1093/biomtc/ujag174","url":null,"abstract":"Recent neuroimaging studies have revealed that brain functional networks exhibit temporal fluctuations during cognitive tasks, sparking growing interest in dynamic functional connectivity (FC). However, few existing methods effectively incorporate temporal dynamics when examining the relationship between brain connectivity and cognition. In this work, we propose a Bayesian deep learning framework that decomposes FC into dynamic and stationary components, thereby disentangling transient fluctuations from stable network structures. By leveraging deep kernel learning, the model flexibly represents transient functions as continuous processes, allowing the data to guide the discovery of meaningful temporal patterns beyond conventional assumptions. Building on this representation, the dynamic component is modeled via a thresholded Gaussian process, which captures smooth temporal variations while inducing sparsity to highlight significant temporal signals. Simultaneously, the stationary component is modeled using a deep neural network with an integrated thresholding layer, enabling the learning of complex nonlinear relationships and enhancing interpretability by emphasizing key connections. This integrative approach balances model flexibility, temporal resolution, and interpretability, providing a powerful tool for predicting cognitive outcomes from functional neuroimaging data. We demonstrate that our method outperforms existing approaches in both prediction accuracy and feature selection through extensive simulations. Applied to data from the Adolescent Brain Cognitive Development study, our framework uncovers dynamic brain signals during cognitive tasks that significantly contribute to variations in cognitive performance.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 4","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13644470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149126655","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

Dissecting microbial community structure and heterogeneity via multivariate covariate-adjusted clustering. 通过多变量协变量调整聚类剖析微生物群落结构和异质性。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag186

Zhongmao Liu, Xiaohui Yin, Yanjiao Zhou, Gen Li, Kun Chen

{"title":"Dissecting microbial community structure and heterogeneity via multivariate covariate-adjusted clustering.","authors":"Zhongmao Liu, Xiaohui Yin, Yanjiao Zhou, Gen Li, Kun Chen","doi":"10.1093/biomtc/ujag186","DOIUrl":"10.1093/biomtc/ujag186","url":null,"abstract":"In microbiome studies, it is often of great interest to identify clusters or partitions of microbiome profiles within a study population and to characterize the distinctive attributes of each resulting microbial community. While raw counts or relative compositions are commonly used for such analysis, variations between clusters may be driven or distorted by subject-level covariates, reflecting underlying biological and clinical heterogeneity across individuals. Simultaneously detecting latent communities and identifying covariates that differentiate them can enhance our understanding of the microbiome and its association with health outcomes. To this end, we propose a Dirichlet-multinomial mixture regression (DMMR) model that enables joint clustering of microbiome profiles while accounting for covariates with either homogeneous or heterogeneous effects across clusters. A novel symmetric link function is introduced to facilitate covariate modeling through the compositional parameters. We develop efficient algorithms with convergence guarantees for parameter estimation and establish theoretical properties of the proposed estimators. Extensive simulation studies demonstrate the effectiveness of the method in clustering, feature selection, and heterogeneity detection. We illustrate the utility of DMMR through a comprehensive application to upper-airway microbiota data from a pediatric asthma study, uncovering distinct microbial subtypes and their associations with clinical characteristics.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 4","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13732015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149317579","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

Dimension reduction for characterizing sexual dimorphism in biomechanics of the temporomandibular joint. 表征颞下颌关节生物力学中两性异形的降维方法。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag181

Sung Hee Park, Xin Zhang, Elizabeth H Slate, Shuchun Sun, Hai Yao

{"title":"Dimension reduction for characterizing sexual dimorphism in biomechanics of the temporomandibular joint.","authors":"Sung Hee Park, Xin Zhang, Elizabeth H Slate, Shuchun Sun, Hai Yao","doi":"10.1093/biomtc/ujag181","DOIUrl":"10.1093/biomtc/ujag181","url":null,"abstract":"Sexual dimorphism is a critical factor in many biological and medical research fields. In biomechanics and bioengineering, understanding sex differences is crucial for studying musculoskeletal conditions such as temporomandibular disorder. This paper focuses on the association between the craniofacial skeletal morphology and temporomandibular joint related masticatory muscle attachments to discern sex differences. Data were collected from 11 female and 10 male cadaver heads to investigate sex-specific relationships between the skull and muscles. We propose a conditional cross-covariance reduction (CCR) model, designed to examine the dynamic association between two sets of random variables conditioned on a third binary variable (e.g., sex), highlighting the most distinctive sex-related relationships between skull and muscle attachments in the human cadaver data. Under the CCR model, we employ a sparse singular value decomposition algorithm. We further introduce a sequential permutation for selecting sparsity method to identify important variables and determine the optimal number of selected variables.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 4","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13720952/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149294231","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

An efficient Bayesian data augmentation algorithm for continuous-time stochastic epidemic models fit to partially observed incidence data. 连续时间随机流行病模型的有效贝叶斯数据增强算法拟合部分观测到的发病率数据。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag187

Haoyu Wu, David A Stephens, Erica E M Moodie

{"title":"An efficient Bayesian data augmentation algorithm for continuous-time stochastic epidemic models fit to partially observed incidence data.","authors":"Haoyu Wu, David A Stephens, Erica E M Moodie","doi":"10.1093/biomtc/ujag187","DOIUrl":"https://doi.org/10.1093/biomtc/ujag187","url":null,"abstract":"Statistical modeling of infectious disease outbreaks with incidence data is crucial to understanding epidemic dynamics and informing public health interventions. Stochastic epidemic models (SEMs) account for the inherent stochasticity in epidemic evolution and are particularly useful in small outbreaks or low-prevalence settings. In SEMs, the epidemic is naturally represented as a continuous-time Markov jump process (MJP), while surveillance data are collected at discrete times and subject to underdetection. Fitting SEMs to partially observed epidemic trajectories requires integrating over all possible transition paths, which is often intractable. Individual-level data augmentation (DA) methods have been developed to address these challenges. However, existing approaches either rely on per-subject updates with limited scalability or impose typically unrealistic assumptions to simplify the complex dimensional change problem for state-space SEMs. We present an efficient DA Markov chain Monte Carlo (MCMC) framework for fitting continuous-time state-space SEMs to incidence data. Our method uses a block sampler to jointly update individual trajectories and proposes transition times using a discrete-time approximation to the latent MJP. Through simulation studies, we demonstrate that our method significantly improves MCMC mixing and scalability compared to the previous DA approach and remains computationally robust in scenarios where particle MCMC algorithms struggle. We apply the method to the 2014 Ebola outbreak in a rural village in Sierra Leone and the 2009 H1N1 outbreak in a remote community in Canada.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 4","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149317578","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

Nonparametric sensitivity analysis for unobserved confounding with survival outcomes. 未观察到的混杂与生存结果的非参数敏感性分析。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag184

Rui Hu, Ted Westling

{"title":"Nonparametric sensitivity analysis for unobserved confounding with survival outcomes.","authors":"Rui Hu, Ted Westling","doi":"10.1093/biomtc/ujag184","DOIUrl":"https://doi.org/10.1093/biomtc/ujag184","url":null,"abstract":"In observational studies, the observed association between an exposure and outcome of interest may be distorted by unobserved confounding. Causal sensitivity analysis can be used to assess the robustness of observed associations to potential unobserved confounding. For time-to-event outcomes, existing sensitivity analysis methods rely on parametric assumptions on the structure of the unobserved confounders and Cox proportional hazards models for the outcome regression. If these assumptions fail to hold, it is unclear whether the conclusions of the sensitivity analysis remain valid. Additionally, causal interpretation of the hazard ratio is challenging. To address these limitations, in this paper, we develop a nonparametric sensitivity analysis framework for time-to-event data. Specifically, we derive nonparametric bounds for the difference between the observed and counterfactual survival curves and propose estimators and inference for these bounds using semiparametric efficiency theory. We also provide nonparametric bounds and inference for the difference between the observed and counterfactual restricted mean survival times. We demonstrate the performance of our proposed methods using numerical studies and an analysis of the causal effect of elective neck dissection on mortality in patients with high-grade parotid carcinoma.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 4","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149340606","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