Biometrics最新文献_第5页

Correction to: Discussion on 'Causal inference with misspecified network interference structure' by Bar Weinstein and Daniel Nevo. 更正：Bar Weinstein和Daniel Nevo关于“错误指定网络干扰结构的因果推理”的讨论。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag133

引用次数: 0

Correction to: Statistical inference for mean function of partially observed functional time series. 修正：部分观测函数时间序列平均函数的统计推断。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag158

引用次数: 0

Shift-adjusted Neyman-Pearson classifiers via single index modeling (SACSIM). 移位调整内曼-皮尔逊分类器通过单指标建模（SACSIM）。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag138

Jiaming Qiu, Ying-Qi Zhao, Yingye Zheng

引用次数: 0

Seamless dose optimization design accounting for unknown patient heterogeneity in cancer clinical trials. 考虑癌症临床试验中未知患者异质性的无缝剂量优化设计。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag161

Rebecca B Silva, Bin Cheng, Shing M Lee

{"title":"Seamless dose optimization design accounting for unknown patient heterogeneity in cancer clinical trials.","authors":"Rebecca B Silva, Bin Cheng, Shing M Lee","doi":"10.1093/biomtc/ujag161","DOIUrl":"10.1093/biomtc/ujag161","url":null,"abstract":"Project Optimus, an initiative by the FDA's Oncology Center of Excellence, seeks to reform the dose-optimization and dose-selection paradigm in oncology. We propose a dose-optimization design that considers plateau efficacy profiles, integrates pharmacokinetic data to inform the exposure-toxicity curve, and accounts for patient characteristics that may contribute to heterogeneity in response. The dose-optimization design is carried out in two stages. First, a toxicity-driven stage estimates a safe set of doses. Then, a dose-ranging efficacy-driven stage explores the set using response and patient characteristic data, employing Bayesian Sparse Group Selection to understand patient heterogeneity. Between stages, the design integrates pharmacokinetic data and uses futility assessments to identify the target population among the general phase I patient population. An optimal dose is recommended for each identified subpopulation within the target population. The simulation study demonstrates that a model-based approach to identifying the target population can be effective; patient characteristics relating to heterogeneity were identified, and different optimal doses were recommended for each identified target subpopulation. Most designs that account for patient heterogeneity are intended for trials where heterogeneity is known, and pre-defined subpopulations are specified. However, given the limited information at such an early stage, subpopulations should be learned through the design.","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/PMC13584192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148987957","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

Adaptive Bayesian multivariate spline knot inference with prior specifications on model complexity. 基于模型复杂度的自适应贝叶斯多变量样条结推理。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag121

Junhui He, Ying Yang, Jian Kang

引用次数: 0

FLAME: a model for duration-dependent risk accumulation in episodic temporal exposures. 火焰：一个模型的持续依赖的风险积累在偶发性时间暴露。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag126

Xinkai Zhou, Lee Goeddel, Nauder Faraday, Ciprian M Crainiceanu

{"title":"FLAME: a model for duration-dependent risk accumulation in episodic temporal exposures.","authors":"Xinkai Zhou, Lee Goeddel, Nauder Faraday, Ciprian M Crainiceanu","doi":"10.1093/biomtc/ujag126","DOIUrl":"https://doi.org/10.1093/biomtc/ujag126","url":null,"abstract":"Emerging technologies enable continuous monitoring of temporal exposures to disease risk factors, leading to complex exposure processes characterized by subject-specific numbers and durations of exposure episodes. A key scientific question is how the number and duration of such episodes influence disease risk. Existing methods typically rely on scalar summaries or time-indexed representations and are not naturally suited to model duration-dependent risk accumulation at the episode level. We introduce the FLexible Accumulation ModEl (FLAME), a semiparametric model for risk accumulation at the level of individual exposure episodes, with duration as the primary driver of risk. FLAME is motivated by and applied to quantifying the association between the duration of intraoperative hypotension and acute kidney injury (AKI) following cardiac surgery. The estimated risk accumulation function reveals that, although 60 one-minute hypotensive episodes are associated with an AKI probability of 0.24, a single sustained 60-minute episode increases that probability to 0.33, representing a 38% increase despite identical total duration. These findings provide actionable insights for intraoperative hemodynamic management and demonstrate the importance of accounting for episodic exposure patterns. While motivated by cardiac surgery, FLAME is broadly applicable to other settings involving high-resolution temporal exposures. An R package, flameRisk, is provided to facilitate the application of the method in practice.","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":"148429724","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

Manifold-constrained Gaussian processes for inference of mixed-effects ordinary differential equations with application to pharmacokinetics. 混合效应常微分方程的流形约束高斯过程推理及其在药代动力学中的应用。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag125

Yuxuan Zhao, Samuel W K Wong

{"title":"Manifold-constrained Gaussian processes for inference of mixed-effects ordinary differential equations with application to pharmacokinetics.","authors":"Yuxuan Zhao, Samuel W K Wong","doi":"10.1093/biomtc/ujag125","DOIUrl":"https://doi.org/10.1093/biomtc/ujag125","url":null,"abstract":"Dynamic systems involving a population of subjects often feature significant variability among the individual trajectories. Mixed-effects ordinary differential equations (ODEs) provide a natural modeling framework for such systems, where subjects share the same functional form for the ODEs but have subject-specific parameters. However, inference for nonlinear mixed-effects ODE models remains challenging, particularly in settings where the system is only partially observed or the trajectories are highly sensitive to the model parameters. In pharmacokinetic modeling, for example, the observed data also tend to be sparse and noisy. To address these challenges, we propose an extension of manifold-constrained Gaussian processes for inference of general mixed-effects ODE models within a Bayesian statistical framework. We evaluate our method on simulated examples, demonstrating its ability to provide fast and accurate inference for parameters and trajectories using nested optimization. To illustrate the practical efficacy of the proposed method, we provide a real data analysis of a pharmacokinetic model used for an HIV combination therapy study.","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":"148429749","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

Generalized Local Kendall's τ: a novel framework for uncovering nonlinear local dependence. 广义局部肯德尔τ：揭示非线性局部依赖的新框架。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag132

Zaixin Huang, Zhengjun Zhang

{"title":"Generalized Local Kendall's τ: a novel framework for uncovering nonlinear local dependence.","authors":"Zaixin Huang, Zhengjun Zhang","doi":"10.1093/biomtc/ujag132","DOIUrl":"https://doi.org/10.1093/biomtc/ujag132","url":null,"abstract":"Identifying patient subgroups is increasingly vital for advancing personalized and precision medicine, where patient heterogeneity plays a central role. Subgroups can be revealed by examining how predictors depend on each other. However, traditional global dependence measures, such as Kendall's τ, often overlook localized and nonlinear relationships, potentially missing critical patterns that define subpopulations. Existing local dependence measures are typically constrained to linear structures or lack flexibility in capturing complex local behaviors. We propose a novel, intuitive, and highly flexible framework, the Generalized Local Kendall's τ, for assessing nonlinear local dependence. This method allows local regions to take diverse shapes (e.g., rectangles, squares, extreme-value neighborhoods), thereby unifying global, local, and tail dependence within a flexible and interpretable structure. The framework includes U-statistic-based and copula model-based estimators, introducing four new quantile dependence measures and novel tail dependence coefficients. We develop two visualization tools-Type I and Type II local Kendall's τ surfaces-applied to Archimedean copulas to aid interpretation. Comparisons show that global Kendall's τ often differs markedly from its local versions, emphasizing the added value of local analysis. Simulation results indicate that when the model is correctly specified, the copula-based estimator exhibits significantly superior performance. We illustrate the framework's practical utility using Parkinson's disease and COVID-19 datasets, showing its ability to detect subgroup-specific dependence patterns vital for precision medicine.","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":"148787795","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

Bayesian joint additive factor models for multiview learning. 多视图学习的贝叶斯联合加性因子模型。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag021

Niccolo Anceschi, Federico Ferrari, David B Dunson, Himel Mallick

{"title":"Bayesian joint additive factor models for multiview learning.","authors":"Niccolo Anceschi, Federico Ferrari, David B Dunson, Himel Mallick","doi":"10.1093/biomtc/ujag021","DOIUrl":"10.1093/biomtc/ujag021","url":null,"abstract":"It is increasingly common to collect data of multiple different types on the same set of samples. Our focus is on studying relationships between such multiview features and responses. A motivating application arises in the context of precision medicine where multiomics data are collected to correlate with clinical outcomes. It is of interest to infer dependence within and across views while combining multimodal information to improve the prediction of outcomes. The signal-to-noise ratio can vary substantially across views, motivating more nuanced statistical tools beyond standard late and early fusion. This challenge comes with the need to preserve interpretability, select features, and obtain accurate uncertainty quantification. To address these challenges, we introduce two complementary factor regression models. A baseline joint factor regression (jfr) captures combined variation across views via a single factor set, and a more nuanced Joint Additive FActor Regression (jafar) that decomposes variation into shared and view-specific components. For JFR, we use independent cumulative shrinkage process (I-CUSP) priors, while for JAFAR, we develop a dependent version (D-CUSP) designed to ensure identifiability of the components. We develop Gibbs samplers that exploit the model structure and accommodate flexible feature and outcome distributions. Prediction of time-to-labor onset from immunome, metabolome, and proteome data illustrates performance gains against state-of-the-art competitors.","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/PMC13439293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148672888","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

Bayesian tele-connected spatial clustering of multivariate spatial data with applications to disease-mapping. 多维空间数据的贝叶斯远程连接空间聚类及其在疾病制图中的应用。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag128

Srijato Bhattacharyya, Huiyan Sang, Bani Mallick

{"title":"Bayesian tele-connected spatial clustering of multivariate spatial data with applications to disease-mapping.","authors":"Srijato Bhattacharyya, Huiyan Sang, Bani Mallick","doi":"10.1093/biomtc/ujag128","DOIUrl":"10.1093/biomtc/ujag128","url":null,"abstract":"Spatial clustering is crucial in disease mapping by identifying subregions with different patterns of disease incidence or mortality. This study proposes a novel Bayesian spatial clustering method for multivariate spatial disease data, which allows for understanding geographic variations of multivariate disease patterns while accounting for both spatial information and dependence among multiple disease measurements. We develop a new random tele-connected graph partition model with an unknown number of clusters, which is capable of encouraging locally contiguous clusters and allowing for remote subregions to be clustered together. We use this prior in a Bayesian hierarchical model to detect spatial clusters and estimate cluster-specific disease patterns and dependence across the multivariate disease variables. We develop a tailored Markov chain Monte Carlo (MCMC) algorithm for posterior inference, utilizing efficient doubly split-merge samplers taking advantage of graph algorithms. We illustrate our method with simulation studies and apply it to investigate the clustering patterns of county-level prostate cancer mortality rate decline across six southern U.S. states from 1985 to 2014.","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/PMC13377533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148468420","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