Biometrics最新文献

Frequency band analysis of nonstationary multivariate time series. 非平稳多元时间序列的频带分析。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf083

Raanju R Sundararajan, Scott A Bruce

{"title":"Frequency band analysis of nonstationary multivariate time series.","authors":"Raanju R Sundararajan, Scott A Bruce","doi":"10.1093/biomtc/ujaf083","DOIUrl":"10.1093/biomtc/ujaf083","url":null,"abstract":"Information from frequency bands in biomedical time series provides useful summaries of the observed signal. Many existing methods consider summaries of the time series obtained over a few well-known, pre-defined frequency bands of interest. However, there is a dearth of data-driven methods for identifying frequency bands that optimally summarize frequency-domain information in the time series. A new method to identify partition points in the frequency space of a multivariate locally stationary time series is proposed. These partition points signify changes across frequencies in the time-varying behavior of the signal and provide frequency band summary measures that best preserve nonstationary dynamics of the observed series. An $L_2$-norm based discrepancy measure that finds differences in the time-varying spectral density matrix is constructed, and its asymptotic properties are derived. New nonparametric bootstrap tests are also provided to identify significant frequency partition points and to identify components and cross-components of the spectral matrix exhibiting changes over frequencies. Finite-sample performance of the proposed method is illustrated via simulations. The proposed method is used to develop optimal frequency band summary measures for characterizing time-varying behavior in resting-state electroencephalography time series, as well as identifying components and cross-components associated with each frequency partition point.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290460/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144706182","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

High-dimensional multi-study multi-modality covariate-augmented generalized factor model. 高维多研究多模态协变量增广广义因子模型。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf107

Wei Liu, Qingzhi Zhong

{"title":"High-dimensional multi-study multi-modality covariate-augmented generalized factor model.","authors":"Wei Liu, Qingzhi Zhong","doi":"10.1093/biomtc/ujaf107","DOIUrl":"10.1093/biomtc/ujaf107","url":null,"abstract":"Latent factor models that integrate data from multiple sources/studies or modalities have garnered considerable attention across various disciplines. However, existing methods predominantly focus either on multi-study integration or multi-modality integration, rendering them insufficient for analyzing the diverse modalities measured across multiple studies. To address this limitation and cater to practical needs, we introduce a high-dimensional generalized factor model that seamlessly integrates multi-modality data from multiple studies, while also accommodating additional covariates. We conduct a thorough investigation of the identifiability conditions to enhance the model's interpretability. To tackle the complexity of high-dimensional nonlinear integration caused by 4 large latent random matrices, we utilize a variational lower bound to approximate the observed log-likelihood by employing a variational posterior distribution. By profiling the variational parameters, we establish the asymptotical properties of estimators for model parameters using M-estimation theory. Furthermore, we devise a computationally efficient variational expectation maximization (EM) algorithm to execute the estimation process and a criterion to determine the optimal number of both study-shared and study-specific factors. Extensive simulation studies and a real-world application show that the proposed method significantly outperforms existing methods in terms of estimation accuracy and computational efficiency.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871261","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

Model robust designs for dose-response models. 剂量-反应模型的模型稳健设计。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf112

Belmiro P M Duarte, Anthony C Atkinson, Nuno M C Oliveira

{"title":"Model robust designs for dose-response models.","authors":"Belmiro P M Duarte, Anthony C Atkinson, Nuno M C Oliveira","doi":"10.1093/biomtc/ujaf112","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf112","url":null,"abstract":"An optimal experimental design is a structured data collection plan aimed at maximizing the amount of information gathered. Determining an optimal experimental design, however, relies on the assumption that a predetermined model structure, relating the response and covariates, is known a priori. In practical scenarios, such as dose-response modeling, the form of the model representing the \"true\" relationship is frequently unknown, although there exists a finite set or pool of potential alternative models. Designing experiments based on a single model from this set may lead to inefficiency or inadequacy if the \"true\" model differs from that assumed when calculating the design. One approach to minimize the impact of the uncertainty in the model on the experimental plan is known as model robust design. In this context, we systematically address the challenge of finding approximate optimal model robust experimental designs. Our focus is on locally optimal designs, so allowing some of the models in the pool to be nonlinear. We present three Semidefinite Programming-based formulations, each aligned with one of the classes of model robustness criteria introduced by Läuter. These formulations exploit the semidefinite representability of the robustness criteria, leading to the representation of the robust problem as a semidefinite program. To ensure comparability of information measures across various models, we employ standardized designs. To illustrate the application of our approach, we consider a dose-response study where, initially, seven models were postulated as potential candidates to describe the dose-response relationship.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144941118","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

Semi-supervised linear regression: enhancing efficiency and robustness in high dimensions. 半监督线性回归：提高高维的效率和鲁棒性。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf113

Kai Chen, Yuqian Zhang

引用次数: 0

Adjusted predictions for generalized estimating equations. 广义估计方程的调整预测。

IF 1.4 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf090

Francis K C Hui, Samuel Muller, Alan H Welsh

{"title":"Adjusted predictions for generalized estimating equations.","authors":"Francis K C Hui, Samuel Muller, Alan H Welsh","doi":"10.1093/biomtc/ujaf090","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf090","url":null,"abstract":"Generalized estimating equations (GEEs) are a popular statistical method for longitudinal data analysis, requiring specification of the first 2 marginal moments of the response along with a working correlation matrix to capture temporal correlations within a cluster. When it comes to prediction at future/new time points using GEEs, a standard approach adopted by practitioners and software is to base it simply on the marginal mean model. In this article, we propose an alternative approach to prediction for independent cluster GEEs. By viewing the GEE as solving an iterative working linear model, we borrow ideas from universal kriging to construct an adjusted predictor that exploits working cross-correlations between the current and new observations within the same cluster. We establish theoretical conditions for the adjusted GEE predictor to outperform the standard GEE predictor. Simulations and an application to longitudinal data on the growth of sitka spruces demonstrate that, even when we misspecify the working correlation structure, adjusted GEE predictors can achieve better performance relative to standard GEE predictors, the so-called \"oracle\" GEE predictor using all time points, and potentially even cluster-specific predictions from a generalized linear mixed model.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144706180","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 machine learning for heterogeneous treatment effects in the presence of missing outcome data. 在缺少结果数据的情况下，异质性治疗效果的因果机器学习。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf098

Matthew Pryce, Karla Diaz-Ordaz, Ruth H Keogh, Stijn Vansteelandt

{"title":"Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data.","authors":"Matthew Pryce, Karla Diaz-Ordaz, Ruth H Keogh, Stijn Vansteelandt","doi":"10.1093/biomtc/ujaf098","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf098","url":null,"abstract":"When estimating heterogeneous treatment effects, missing outcome data can complicate treatment effect estimation, causing certain subgroups of the population to be poorly represented. In this work, we discuss this commonly overlooked problem and consider the impact that missing at random outcome data has on causal machine learning estimators for the conditional average treatment effect (CATE). We propose 2 de-biased machine learning estimators for the CATE, the mDR-learner, and mEP-learner, which address the issue of under-representation by integrating inverse probability of censoring weights into the DR-learner and EP-learner, respectively. We show that under reasonable conditions, these estimators are oracle efficient and illustrate their favorable performance through simulated data settings, comparing them to existing CATE estimators, including comparison to estimators that use common missing data techniques. We present an example of their application using the GBSG2 trial, exploring treatment effect heterogeneity when comparing hormonal therapies to non-hormonal therapies among breast cancer patients post surgery, and offer guidance on the decisions a practitioner must make when implementing these estimators.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144752242","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 group distributional ICA method for decomposing multi-subject diffusion tensor imaging. 多主体扩散张量成像分解的组分布ICA方法。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf117

Guangming Yang, Ben Wu, Jian Kang, Ying Guo

{"title":"A group distributional ICA method for decomposing multi-subject diffusion tensor imaging.","authors":"Guangming Yang, Ben Wu, Jian Kang, Ying Guo","doi":"10.1093/biomtc/ujaf117","DOIUrl":"10.1093/biomtc/ujaf117","url":null,"abstract":"Diffusion tensor imaging (DTI) is a frequently used imaging modality to investigate white matter fiber connections of human brain. DTI provides an important tool for characterizing human brain structural organization. Common goals in DTI analysis include dimension reduction, denoising, and extraction of underlying structure networks. Blind source separation methods are often used to achieve these goals for other imaging modalities. However, there has been very limited work for multi-subject DTI data. Due to the special characteristics of the 3D diffusion tensor measured in DTI, existing methods such as standard independent component analysis (ICA) cannot be directly applied. We propose a Group Distributional ICA (G-DICA) method to fill this gap. G-DICA represents a fundamentally new blind source separation method that separates the parameters in the distribution function of the observed imaging data as a mixture of independent source signals. Decomposing multi-subject DTI using G-DICA uncovers structural networks corresponding to several major white matter fiber bundles in the brain. Through simulation studies and real data applications, the proposed G-DICA method demonstrates superior performance and improved reproducibility compared to the existing method.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145091036","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

Exploring the heterogeneity in recurrent episode lengths based on quantile regression. 基于分位数回归探讨复发发作长度的异质性。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf122

Yi Liu, Guillermo E Umpierrez, Limin Peng

{"title":"Exploring the heterogeneity in recurrent episode lengths based on quantile regression.","authors":"Yi Liu, Guillermo E Umpierrez, Limin Peng","doi":"10.1093/biomtc/ujaf122","DOIUrl":"10.1093/biomtc/ujaf122","url":null,"abstract":"Recurrent episode data frequently arise in chronic disease studies when an event of interest occurs repeatedly and each occurrence lasts for a random period of time. Understanding the heterogeneity in recurrent episode lengths can help guide dynamic and customized disease management. However, there has been relative sparse attention to methods tailored to this end. Existing approaches either do not confer direct interpretation on episode lengths or involve restrictive or unrealistic distributional assumptions, such as exchangeability of within-individual episode lengths. In this work, we propose a modeling strategy that overcomes these limitations through adopting quantile regression and sensibly incorporating time-dependent covariates. Treating recurrent episodes as clustered data, we develop an estimation procedure that properly handles the special complications, including dependent censoring, dependent truncation, and informative cluster size. Our estimation procedure is computationally simple and yields estimators with desirable asymptotic properties. Our numerical studies demonstrate the advantages of the proposed method over naive adaptations of existing approaches.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145091020","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 flexible framework for N-mixture occupancy models: applications to breeding bird surveys. 氮混合占用模型的灵活框架：在种鸟调查中的应用。

IF 1.4 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf087

Huu-Dinh Huynh, J Andrew Royle, Wen-Han Hwang

{"title":"A flexible framework for N-mixture occupancy models: applications to breeding bird surveys.","authors":"Huu-Dinh Huynh, J Andrew Royle, Wen-Han Hwang","doi":"10.1093/biomtc/ujaf087","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf087","url":null,"abstract":"Estimating species abundance under imperfect detection is a key challenge in biodiversity conservation. The N-mixture model, widely recognized for its ability to distinguish between abundance and individual detection probability without marking individuals, is constrained by its stringent closure assumption, which leads to biased estimates when violated in real-world settings. To address this limitation, we propose an extended framework based on a development of the mixed Gamma-Poisson model, incorporating a community parameter that represents the proportion of individuals consistently present throughout the survey period. This flexible framework generalizes both the zero-inflated type occupancy model and the standard N-mixture model as special cases, corresponding to community parameter values of 0 and 1, respectively. The model's effectiveness is validated through simulations and applications to real-world datasets, specifically with 5 species from the North American Breeding Bird Survey and 46 species from the Swiss Breeding Bird Survey, demonstrating its improved accuracy and adaptability in settings where strict closure may not hold.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144706178","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 "Propensity weighting plus adjustment in proportional hazards model is not doubly robust," by Erin E. Gabriel, Michael C. Sachs, Ingeborg Waernbaum, Els Goetghebeur, Paul F. Blanche, Stijn Vansteelandt, Arvid Sjölander, and Thomas Scheike; Volume 80, Issue 3, September 2024, https://doi.org/10.1093/biomtc/ujae069. 对Erin E. Gabriel、Michael C. Sachs、Ingeborg Waernbaum、Els Goetghebeur、Paul F. Blanche、Stijn Vansteelandt、Arvid Sjölander和Thomas Scheike的“比例风险模型的倾向加权加调整并非双重稳健”的修正；80卷，第3期，2024年9月，https://doi.org/10.1093/biomtc/ujae069。

IF 1.4 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf091

Erin E Gabriel, Michael C Sachs, Ingeborg Waernbaum, Els Goetghebeur, Paul F Blanche, Stijn Vansteelandt, Arvid Sjölander, Thomas Scheike

引用次数: 0