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

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

Pseudo-data augmentation for exact conditional likelihood inference in meta-analysis. 元分析中精确条件似然推断的伪数据增强。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag169

Hisashi Noma

{"title":"Pseudo-data augmentation for exact conditional likelihood inference in meta-analysis.","authors":"Hisashi Noma","doi":"10.1093/biomtc/ujag169","DOIUrl":"10.1093/biomtc/ujag169","url":null,"abstract":"In meta-analyses of binary outcomes, two-stage methods based on the DerSimonian-Laird-type random-effects model have been widely used, but these methods fix the within-study variances to their asymptotic estimates and rely on normality assumptions of the outcome measures. Such strict and often unrealistic assumptions can yield biased or inefficient inference. Likelihood-based approaches such as the binomial-normal generalized linear mixed model and the hypergeometric-normal (HGN) model alleviate some of these issues but are intrinsically restricted to the odds-ratio scale, limiting interpretability and precluding direct estimation of the risk-ratio-a more clinically meaningful measure. To overcome these limitations, we propose a unified likelihood-based framework that enables direct estimation of the risk-ratio under the HGN formulation. The proposed method introduces a pseudo-data augmentation approach that preserves the conditional likelihood structure of the HGN model while yielding an unbiased estimating function for the log-risk-ratio. Higher-order inference is achieved via jackknife bias and variance corrections, ensuring accurate coverage even in small or sparse datasets. Simulation studies confirm nearly unbiased estimation and well-calibrated confidence intervals across a wide range of conditions. Applications to published meta-analyses demonstrate that this approach yields interpretable, coherent, and computationally tractable inference.","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":"149134821","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

Causal decomposition framework for quantifying intervenable cancer health disparities. 量化可干预癌症健康差异的因果分解框架。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag171

Fan Xia, Sang Kyu Lee, Seonjin Kim, Kelly Blum, Hyokyoung G Hong, Alison J Canchola, Mi-Ok Kim

{"title":"Causal decomposition framework for quantifying intervenable cancer health disparities.","authors":"Fan Xia, Sang Kyu Lee, Seonjin Kim, Kelly Blum, Hyokyoung G Hong, Alison J Canchola, Mi-Ok Kim","doi":"10.1093/biomtc/ujag171","DOIUrl":"10.1093/biomtc/ujag171","url":null,"abstract":"This study proposes a nonparametric decomposition framework to quantify and prioritize the impact of multiple intervenable mediators contributing to disparities in health outcomes. Identifying modifiable components of health disparities is essential for informing policy and guiding resource allocation, but several methodological challenges remain. These include formulating hypothetical interventions for immutable characteristics such as race or ethnicity, mitigating bias from potentially incompatible statistical models used to represent causal pathways, and evaluating multiple mediators for their joint and individual effects. To address these challenges, we integrate recent advances in health disparities research with modern machine learning techniques. A key assumption, common to causal disparity frameworks, is the absence of unmeasured confounding in the mediator-outcome relationship. We extend existing methods in mediation analysis and introduce a nonparametric sensitivity analysis that provides sharp bounds on modifiable disparity estimates in the presence of unmeasured confounding. Together, these methods offer a robust and flexible approach to causal disparity analysis, enabling the identification of high-impact, actionable interventions. We demonstrate the utility of the methods in the context of colorectal cancer survival disparities, a setting where persistent differences across racial, ethnic, and underserved populations in the United States underscore the urgent need for analytic strategies that uncover effective intervention points.","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/PMC13647267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149134660","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

Prediction intervals for random-effects meta-analysis based on confidence distributions and Edgington's method. 基于置信分布和Edgington方法的随机效应荟萃分析预测区间。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag167

David Kronthaler, Leonhard Held

引用次数: 0

A Gaussian process model for chemoinformatics with application to the hazard classification of organic solvents. 化学信息学的高斯过程模型及其在有机溶剂危害分类中的应用。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag168

Arron Gosnell, Evangelos Evangelou

{"title":"A Gaussian process model for chemoinformatics with application to the hazard classification of organic solvents.","authors":"Arron Gosnell, Evangelos Evangelou","doi":"10.1093/biomtc/ujag168","DOIUrl":"https://doi.org/10.1093/biomtc/ujag168","url":null,"abstract":"With the proliferation of screening tools for chemical testing, it is now possible to create vast databases of chemicals easily. However, rigorous statistical methodologies employed to analyze these databases are in their infancy, and further development to facilitate chemical discovery is imperative. In this paper, we address the challenge of predicting an organic solvent's water pollution class based on its chemical structure. We hypothesize that solvents with similar chemical structure are more likely to belong in the same hazard class. This chemical similarity is measured by the Tanimoto distance, a non-Euclidean metric on the chemical space. To incorporate the similarity between chemical structures in the model, we propose a Gaussian process model on the chemical space, with the kernel being a function of the Tanimoto distance. A novel feature of the proposed model is the inclusion of a scale parameter in the kernel, which controls the strength of the correlation between compounds and offers additional flexibility. We find that accounting for correlation between chemical compounds substantially improves predictive performance over the uncorrelated model, where compound similarity is unaccounted for. Our model also compares favorably against other established models in the literature. Furthermore, we present a genetic algorithm designed to identify important features to a compound's efficacy, with the goal of facilitating chemical discovery. The algorithm operates based on two criteria derived from the proposed model. Simulation studies are conducted to demonstrate the suitability of the proposed methods.","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":"149134722","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

Transportable inference using target population summary statistics under covariate shift. 协变量移位下目标群体汇总统计的可迁移推理。

IF 1.6 4区数学

Biometrics Pub Date : 2026-10-07 DOI: 10.1093/biomtc/ujag170

Ying Sheng, Yifei Sun, Chiung-Yu Huang

{"title":"Transportable inference using target population summary statistics under covariate shift.","authors":"Ying Sheng, Yifei Sun, Chiung-Yu Huang","doi":"10.1093/biomtc/ujag170","DOIUrl":"10.1093/biomtc/ujag170","url":null,"abstract":"Transporting findings from a study population to a target population is central to evidence-based decision-making in real-world settings. Most existing methods require individual-level data from both populations to account for covariate shift. However, privacy regulations and data-sharing constraints often preclude access to such data from the target population, leaving only covariate summaries available for analysis. In this paper, we develop transportability methods that enable valid inference using source individual-level data and target covariate summaries. First, we apply entropy balancing (EB) to transportability, enabling source individual-level data to be adjusted to match the target covariate moments. We establish asymptotic normality for the EB estimator and propose a variance estimator to account for uncertainty in covariate summaries. Second, we develop a new transportability method that allows more flexible modeling of covariate shift, thereby accounting for covariate shift and uncertainty in covariate summaries simultaneously. Asymptotic normality for the proposed estimator is established, and its asymptotic variance is consistently estimated. The proposed method offers greater flexibility in accounting for covariate shift and thus permits consistent estimation and valid inference when the validity of EB is not guaranteed. The proposed methods are evaluated by simulations and illustrated with an analysis of Surveillance, Epidemiology, and End Results breast cancer data.","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/PMC13650301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149134789","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

Group sequential clinical trial designs for composite endpoints incorporating recurrent and terminal events. 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 : 2026-07-01 DOI: 10.1093/biomtc/ujag135

Qinghua Lian, Kwang Woo Ahn, Soyoung Kim, Brent R Logan, Melhem Solh, Michael J Martens

{"title":"Group sequential clinical trial designs for composite endpoints incorporating recurrent and terminal events.","authors":"Qinghua Lian, Kwang Woo Ahn, Soyoung Kim, Brent R Logan, Melhem Solh, Michael J Martens","doi":"10.1093/biomtc/ujag135","DOIUrl":"10.1093/biomtc/ujag135","url":null,"abstract":"Composite endpoints, which combine multiple events of interest, are commonly used in medical research. Compared to evaluating a single outcome, a composite endpoint analysis captures the full clinical impact of treatment and leverages a greater number of events, potentially reducing the required sample size for the study. While composite endpoints have gained much attention in clinical trials, studying them in group sequential designs remains challenging due to the correlated nature of event data collected from the same individual, as the sequence of test statistics may not possess an independent increments structure. In this paper, we propose both one-sample and two-sample group sequential designs grounded in the mean frequency function, defined as the cumulative count of all recurrent and terminal events over time. Recognizing the lack of independent increments, our proposed method leverages the asymptotic covariance structure of the test statistics to construct group sequential boundaries that control the Type I error rate. Extensive simulation studies show the proposed design controls Type I error well and achieves the desired power. We illustrate the utility of our method through a reanalysis of BMT CTN 1703, a phase III randomized controlled trial that evaluated an experimental therapy for the prevention of adverse outcomes after allogeneic stem cell transplant.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13397504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148576970","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

A general framework for testing clustering significance and variable-level inference in high-dimensional data. 一个测试高维数据聚类显著性和变水平推理的通用框架。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag145

Hui Shen, Dongmei Li, Yufeng Liu

{"title":"A general framework for testing clustering significance and variable-level inference in high-dimensional data.","authors":"Hui Shen, Dongmei Li, Yufeng Liu","doi":"10.1093/biomtc/ujag145","DOIUrl":"https://doi.org/10.1093/biomtc/ujag145","url":null,"abstract":"Clustering is a fundamental tool for uncovering heterogeneity in data, but two challenges remain: determining whether observed clusters reflect genuine structure rather than sampling variability, and identifying the variables that drive any significant clustering pattern. Statistical significance of clustering (SigClust) addresses the first problem by assessing clustering significance using the cluster index under a Gaussian null model, with the null distribution estimated by Monte Carlo simulation in high dimensions. We propose SigClust-DE, a method that improves null covariance estimation in SigClust and extends the framework to variable-level inference for identifying features associated with cluster separation. In this way, SigClust-DE provides a joint framework for clustering significance testing and differential expression analysis, a central task in RNA-seq studies. Through extensive simulations and an application to RNA-seq data, we show that SigClust-DE controls Type I error in clustering significance testing, controls the false discovery rate in variable-level inference, and achieves strong power for detecting differentially expressed features.","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":"148787797","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