Biometrics最新文献_第7页

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease. 相互作用树和重复测量混合模型在阿尔茨海默病诊断中的应用。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag104

Zhichen Xu, Jimin Ding, Xiaogang Su, Guoqiao Wang, Ke Xie, Yining Zhao, Chengjie Xiong, Eric McDade, Lon S Schneider, Lei Liu

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

Pseudo-observation regression for sequentially truncated data. 序贯截断数据的伪观测回归。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag084

Jing Qian, Erik T Parner, Morten Overgaard, Rebecca A Betensky

{"title":"Pseudo-observation regression for sequentially truncated data.","authors":"Jing Qian, Erik T Parner, Morten Overgaard, Rebecca A Betensky","doi":"10.1093/biomtc/ujag084","DOIUrl":"10.1093/biomtc/ujag084","url":null,"abstract":"In observational cohort studies with complex sampling schemes, truncation arises when the time to the event of interest is observed only when it falls below or exceeds another random time, i.e., the truncation time. In more complex settings, observation may require a particular ordering of event times; we refer to this extension of the traditional paradigm as sequential truncation. Nonparametric and semiparametric maximum likelihood estimators have been developed recently to estimate the distribution of the event time of interest in the presence of sequential truncation. In this paper, we develop methods for regression modeling in this complex setting using the tool of pseudo-observations. Pseudo-observations are jackknife-like constructs that estimate an individual's contribution to an estimand. They are convenient as they are based on an estimator for the unconditional distribution of the event time. However, the simple pseudo-observation method may not be valid when the truncation depends on the covariates that also explain the time to event of interest, among other constraints. To address this limitation, we also consider a modified pseudo-observation approach. We develop both simple and modified pseudo-observation methods for Cox and accelerated failure time (AFT) models. We evaluate the proposed methods in simulation studies and apply them to an Alzheimer's disease cohort study.","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/PMC13274734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148269996","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

Two-phase designs for cost-effective evaluation of cancer screening tests. 两阶段设计的成本效益评估癌症筛查试验。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag058

Fangya Mao, Richard J Cook, Thomas Lorey, Nicolas Wentzensen, Li C Cheung

{"title":"Two-phase designs for cost-effective evaluation of cancer screening tests.","authors":"Fangya Mao, Richard J Cook, Thomas Lorey, Nicolas Wentzensen, Li C Cheung","doi":"10.1093/biomtc/ujag058","DOIUrl":"10.1093/biomtc/ujag058","url":null,"abstract":"Screening tests are crucial for detecting diseases at preclinical stages when timely intervention can prevent progression to more severe conditions. Advances in technology have facilitated developing screening tests based on novel markers, but evaluating their performance using biospecimens from large cohorts remains logistically complex and financially demanding. Two-phase designs offer a cost-effective solution by allowing inference when expensive marker measurements are collected on only a carefully selected subsample. While traditional two-phase designs have primarily targeted estimation of marker-outcome associations, they can effectively be extended to evaluate the clinical performance of a test, including estimation of positive predictive value (the risk in test positives) and complementary negative predictive value (the risk in test negatives). We propose a novel two-phase design for efficiently evaluating the risk-stratification utility of screening tests in distinguishing between high- and low-risk individuals for both current and future disease. Designed for use with biospecimens collected from screening studies, our methodology accommodates cohorts that include both pre-existing cases at an initial screening visit and new cases identified during follow-up. We demonstrate the efficiency gains of our proposed design compared to other subsampling schemes through simulation and illustrate its application in a motivating study evaluating the p16/ki-67 dual-stain test for managing human papillomavirus-positive women in cervical cancer screening. Data and stored biospecimen samples from Kaiser Permanente Northern California are used in this analysis.","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/PMC13112528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147760699","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

Personalized treatment design in the context of functional confounding. 功能混淆背景下的个性化治疗设计。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag056

Zhixian Yang, Peijun Sang, Yixin Han, Bei Jiang, Linglong Kong, Xingcai Zhou

引用次数: 0

A novel exact confidence interval for the difference of proportions in paired data using a restricted most probable statistic. 用限制最可能统计量对成对数据的比例差提出了一种新的精确置信区间。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag061

Xingyun Cao, Weizhen Wang, Tianfa Xie

{"title":"A novel exact confidence interval for the difference of proportions in paired data using a restricted most probable statistic.","authors":"Xingyun Cao, Weizhen Wang, Tianfa Xie","doi":"10.1093/biomtc/ujag061","DOIUrl":"10.1093/biomtc/ujag061","url":null,"abstract":"Inference on the difference between two proportions in paired data is a key issue, particularly in biomedical research and clinical trials. Numerous methods exist for constructing confidence intervals for this difference. However, approximate methods that rely on asymptotic normality can be unreliable, underscoring the need for exact confidence intervals to improve reliability. In this paper, we develop a novel interval based on the restricted most probable method, which is further optimized using the h-function method to yield an optimal exact interval, ensuring both reliability and precision. We compare the proposed interval with other exact intervals developed through methodologies such as the score method, two Tang methods, the Wang method, the adjusted Wald method, and the score method with continuity correction. Our comparative analysis, utilizing the infimum coverage probability and total interval length as evaluation metrics, demonstrates the uniformly superior performance of the proposed interval. Additionally, an example illustrates its practical application in real-world scenarios. Supplementary Materials provide another example, numerical results on coverage and non-coverage probabilities, and R code.","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":"147761413","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

Decentralized EM algorithm for Gaussian mixtures under data heterogeneity and partial labeling. 数据异构和部分标注下高斯混合的分散EM算法。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag092

Xuetong Li, Shuyuan Wu, Bin Du, Hansheng Wang

{"title":"Decentralized EM algorithm for Gaussian mixtures under data heterogeneity and partial labeling.","authors":"Xuetong Li, Shuyuan Wu, Bin Du, Hansheng Wang","doi":"10.1093/biomtc/ujag092","DOIUrl":"10.1093/biomtc/ujag092","url":null,"abstract":"We systematically study several network-based Expectation-Maximization (EM) algorithms for the Gaussian mixture model within decentralized federated learning (DFL). Our theoretical investigation reveals that directly extending the classic EM algorithm to DFL leads to a seriously biased estimator if the data are heterogeneously distributed across different sites. To address this issue, we introduce a momentum network EM (MNEM) algorithm, which integrates information from both current and historical estimators from previous DFL iterations. We further develop a semi-supervised MNEM (semi-MNEM) algorithm, which utilizes valuable information provided by partially labeled data. Rigorous theoretical analysis demonstrates that the MNEM estimator can achieve the same asymptotic efficiency as the whole sample estimator under appropriate regularity conditions, even if the data are heterogeneously distributed. Moreover, the semi-MNEM estimator significantly improves the convergence speed of the MNEM algorithm, even if different mixture components are poorly separated. Extensive simulations are conducted, and a widely used chest X-ray dataset is analyzed to demonstrate the finite-sample performance of the proposed methods.","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":"148040683","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

Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis. 从多个中间事件动态预测生存的学习关联：在心血管疾病预后中的应用

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag087

Tonghui Yu, Liming Xiang

{"title":"Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis.","authors":"Tonghui Yu, Liming Xiang","doi":"10.1093/biomtc/ujag087","DOIUrl":"10.1093/biomtc/ujag087","url":null,"abstract":"Cardiovascular diseases are major causes of mortality globally. They often co-occur and are interrelated, leading to partial-order relationships among their onset times. However, these onset times are subject to informative censoring due to the occurrence of death, posing significant challenges for survival prediction. In this paper, we propose a novel copula-based framework that learns dependence among multiple correlated marginal components through a pseudo-likelihood for estimation. We adopt nonparametric marginals, alleviating the reliance on marginal distribution assumptions typically required in conventional copula models, and estimate the association between the onsets of intermediate cardiovascular diseases and death by solving a concordance estimating equation. Under this framework, a renewable risk assessment method is developed for dynamic survival prediction, leveraging information on disease onset times and the maximum follow-up duration. Our proposed method yields estimators with well-established properties, and its flexibility and predictive effectiveness are demonstrated through extensive simulation studies. We apply the method to data from a heart disease study, demonstrating the benefits of incorporating the associations among various cardiovascular diseases and their synergistic effects on mortality for dynamic prediction of overall survival.","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":"148040913","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 adaptive randomization in the I-SPY2 sequential multiple assignment randomized trial. I-SPY2顺序多任务随机试验中的贝叶斯自适应随机化。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag063

Peter Norwood, Christina Yau, Denise Wolf, Philip Beineke, Andrew Chapple, Anastasios Tsiatis, Marie Davidian

{"title":"Bayesian adaptive randomization in the I-SPY2 sequential multiple assignment randomized trial.","authors":"Peter Norwood, Christina Yau, Denise Wolf, Philip Beineke, Andrew Chapple, Anastasios Tsiatis, Marie Davidian","doi":"10.1093/biomtc/ujag063","DOIUrl":"10.1093/biomtc/ujag063","url":null,"abstract":"I-SPY2 is a long-running phase 2 platform trial that evaluates neoadjuvant treatments for locally advanced breast cancer to identify those with high efficacy that are likely to be successful in phase 3 trials, assigning patients to novel agents using response-adaptive randomization (RAR). Recently, I-SPY2 was reconfigured as a sequential multiple assignment randomized trial (SMART), with up to three stages of therapy. At the first stage, a patient is assigned to a tumor-subtype-specific therapy. If the patient fails to show a satisfactory response, the patient is assigned to a second subtype-specific therapy, and receives a third, rescue therapy if response is still not achieved. The I-SPY2 SMART thus supports identification of highly efficacious entire treatment regimes. The transition of I-SPY2 to a SMART required development of a RAR scheme that updates randomization probabilities at each stage, aligned with the goal of maximizing the number of patients who achieve a pathological complete response (pCR). We present our Bayesian RAR approach, which updates randomization probabilities based on the posterior probability that treatments are part of the optimal regime. Empirical studies demonstrate that it results in more patients having treatment experience consistent with highly efficacious regimes, improves overall within-trial pCR rates, and identifies optimal regimes post trial at rates similar to or exceeding those under simple, uniform, nonadaptive randomization.","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/PMC13126647/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147760532","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

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou. 张静茹、崔二佳、李宏哲、寿浩昌对“integrity：一种多源纵向体育活动数据整合方法”的讨论。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag114

Daniel Fuller, Kevin Stanley

{"title":"Discussion on \"INTACT: a method for integration of longitudinal physical activity data from multiple sources\" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.","authors":"Daniel Fuller, Kevin Stanley","doi":"10.1093/biomtc/ujag114","DOIUrl":"10.1093/biomtc/ujag114","url":null,"abstract":"The INTACT (INtegration of Time varying data from weArable sensors for physiCal acTivity) method proposed by Zhang et al. represents a timely and important contribution to the harmonization of accelerometer data across studies. The authors provide a mathematically rigorous presentation and an open source R package. INTACT has clear utility for large scale data integration initiatives. INTACT draws inspiration from techniques used in domains where latent biological mechanisms are poorly defined. We argue that physical activity research is distinct in that the primary latent constructs, time and intensity, are well understood. Accelerometer summary metrics represent proxies for these constructs, raising questions about whether abstraction through an eigenmodel improves interpretation of the physiological meaning. The post harmonization \"activity intensity\" metric produced by INTACT lacks clear units, scale consistency, and it is unclear how the harmonized metric would be applied to activity classification methods. The method's reliance on eigenfunctions is both a strength and a limitation. There are challenges for comparability across studies, and it will be important for applied users to conduct sensitivity analyses to evaluate eigenfunction selection. We also highlight the critical assumption of a shared eigenspace across datasets and populations, which may be violated in contexts with different physical activity patterns. Additional methodological development and clear interpretability will play an important role in the adoption of this method by the research community.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148263191","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