Biometrics最新文献_第3页

Spatial confounding in multivariate areal data analysis. 多变量区域数据分析中的空间混淆。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag143

Kyle Lin Wu, Sudipto Banerjee

{"title":"Spatial confounding in multivariate areal data analysis.","authors":"Kyle Lin Wu, Sudipto Banerjee","doi":"10.1093/biomtc/ujag143","DOIUrl":"https://doi.org/10.1093/biomtc/ujag143","url":null,"abstract":"We investigate spatial confounding in the presence of multivariate disease dependence. Although multiple methods have been proposed for adjusting statistical models to mitigate spatial confounding in estimating regression coefficients, the results on interactions between spatial confounding and multivariate dependence are very limited. We contribute to this domain by investigating spatial confounding from the \"analysis\" and \"data generation\" perspectives in a Bayesian coregionalized areal regression model. We derive novel results that show that under spatial confounding, posterior variance inflation is limited in multivariate areal models and spatial point estimators of fixed effects retain higher precision compared to their non-spatial counterparts. We demonstrate the favorable performance of spatial analysis compared to a non-spatial model in our simulation experiments, even in the presence of spatial confounding and a misspecified spatial structure. In this regard, we align with recommendations from several authors in preferring hierarchical spatial models. We analyze county-level data from the US on obesity/diabetes prevalence and diabetes-related cancer mortality, comparing the results with and without spatial random effects.","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":"148705255","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 tree-based kernel for densities and its applications in clustering DNase-seq profiles. 基于树的密度核及其在dna序列聚类中的应用。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag154

Yuliang Xu, Kaixuan Luo, Li Ma

{"title":"A tree-based kernel for densities and its applications in clustering DNase-seq profiles.","authors":"Yuliang Xu, Kaixuan Luo, Li Ma","doi":"10.1093/biomtc/ujag154","DOIUrl":"https://doi.org/10.1093/biomtc/ujag154","url":null,"abstract":"Modeling multiple sampling densities within a hierarchical framework enables borrowing of information across samples. These \"density random effects\" can act as kernels in latent variable models to represent exchangeable subgroups or clusters. A key feature of these kernels is the (functional) covariance they induce, which determines how densities are grouped in mixture models. Our motivating problem is clustering chromatin accessibility profiles from high-throughput DNase-seq experiments to detect transcription factor (TF) binding. TF binding typically produces footprint profiles with spatial patterns, creating long-range dependency across genomic locations. Existing nonparametric hierarchical models impose restrictive covariance assumptions and cannot accommodate such dependencies, often leading to biologically uninformative clusters. We propose a nonparametric density kernel that is flexible enough to capture diverse covariance structures and adapts to various spatial patterns of TF footprints. The kernel specifies dyadic tree splitting probabilities via a multivariate logit-normal model with a sparse precision matrix. Bayesian inference for latent variable models using this kernel is implemented through Gibbs sampling with Pólya-Gamma augmentation. Extensive simulations show that our kernel substantially improves clustering accuracy. We apply the proposed mixture model to DNase-seq data from the Encyclopedia of DNA Elements project, which results in biologically meaningful clusters corresponding to binding events of two common TFs.","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":"148987769","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

Estimating effects on cumulative incidence probabilities by direct polytomous regression and polytomous log-odds product. 用直接多元回归和多元对数-赔率积估计对累积发生率的影响。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag160

Shiro Tanaka, Thomas H Scheike

{"title":"Estimating effects on cumulative incidence probabilities by direct polytomous regression and polytomous log-odds product.","authors":"Shiro Tanaka, Thomas H Scheike","doi":"10.1093/biomtc/ujag160","DOIUrl":"https://doi.org/10.1093/biomtc/ujag160","url":null,"abstract":"In competing-risks analysis, modeling each cause separately may yield specification of cumulative incidence functions that are not jointly coherent, because the resulting cause-specific probabilities need not satisfy the natural sum-to-one constraint. We address this problem by introducing a direct polytomous regression approach that models all causes jointly and enforces coherence through a reparameterization based on polytomous log-odds products. Our approach is applicable to semiparametric models with common multiplicative effects over time as well as models focused on a specific time point. For estimation under right-censoring, we develop stratified inverse probability of censoring weighted (IPCW) estimators for the effect parameters. Within a specified class of augmented IPCW estimators, the proposed estimators attain the minimum asymptotic variance under the stated regularity conditions, without the computational burden of deriving augmentation terms for each cause. The utility of our coherent modeling is demonstrated through simulation studies and its applications to a cohort study of type 2 diabetes and a randomized trial of prostate cancer.","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":"148987792","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

Unsupervised optimal deep transfer learning for classification under general conditional shift. 一般条件移位下分类的无监督最优深度迁移学习。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag123

Junjun Lang, Yukun Liu

{"title":"Unsupervised optimal deep transfer learning for classification under general conditional shift.","authors":"Junjun Lang, Yukun Liu","doi":"10.1093/biomtc/ujag123","DOIUrl":"https://doi.org/10.1093/biomtc/ujag123","url":null,"abstract":"Classifiers trained on labeled source data may yield misleading results when applied to unlabeled target data drawn from a different distribution. Transfer learning can rectify this by transferring knowledge from source to target data, but its effectiveness frequently relies on stringent assumptions, such as label shift or strong separation conditions. We introduce a novel general conditional shift assumption, which encompasses label shift as a special case and facilitates the identifiability of both the target distribution and the shift function without requiring a separation condition. Our classifier is constructed by integrating deep neural networks (DNNs) and a pseudo-maximum likelihood approach. We establish asymptotic error bounds for our DNN-based classifier and estimators of the conditional probabilities ${ boldsymbol{eta }_{P}}$ for source data and the target label distribution $boldsymbol{pi }_{Q}$, in terms of the intrinsic dimension of ${ boldsymbol{eta }_{P}}$. Notably, the excess risk of the proposed classifier achieves the optimal minimax rate, up to a logarithmic factor. Our method not only eliminates the need to estimate the shift function, but also alleviates the curse of dimensionality when ${ boldsymbol{eta }_{P}}$ exhibits a low-dimensional structure. Numerical simulations, along with an analysis of an Alzheimer's disease dataset, underscore its exceptional performance.","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":"148410294","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

Interim analysis in sequential multiple assignment randomized trials for survival outcomes. 生存结局的连续多任务随机试验的中期分析。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag120

Zi Wang, Yu Cheng, Abdus S Wahed

{"title":"Interim analysis in sequential multiple assignment randomized trials for survival outcomes.","authors":"Zi Wang, Yu Cheng, Abdus S Wahed","doi":"10.1093/biomtc/ujag120","DOIUrl":"10.1093/biomtc/ujag120","url":null,"abstract":"Sequential multiple assignment randomized trials (SMARTs) mimic the actual treatment processes experienced by physicians and patients in clinical settings and inform the comparative effectiveness of dynamic treatment regimes. In such trials, patients undergo multiple stages of treatment, and the treatment assignment is adjusted over time based on individual patient characteristics, such as disease status and treatment history. In this work, we develop and evaluate statistically valid global interim monitoring (IM) approaches to allow for early termination of SMARTs for efficacy targeting survival outcomes. We propose a weighted log-rank Chi-square statistic to account for overlapping treatment paths and quantify how the log-rank statistics at two different analysis points are correlated. Efficacy boundaries at multiple interim analyses can then be established using the Pocock, O'Brien Fleming, and Lan-DeMets boundaries. We conduct extensive simulations to comparatively evaluate the operating characteristics (type I error and power) of our IM procedure based on the proposed statistic and an existing alternative statistic. The methods are demonstrated via an analysis of a neuroblastoma dataset.","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":"148395745","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

Symmetric vaccine efficacy. 对称疫苗效力。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag141

Lucy D'Agostino McGowan, Sarah C Lotspeich, Michael G Hudgens

引用次数: 0

A Bayesian model averaging method for dose ranging studies in oncology. 肿瘤学剂量范围研究的贝叶斯模型平均方法。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag156

Adetayo Kasim, Nathan W Bean, Elena Parkhomenko, Amelia Cottle, Andre Acusta, Helen Zhou, Tai-Tsang Chen, Antony Sabin, Matthew A Psioda

{"title":"A Bayesian model averaging method for dose ranging studies in oncology.","authors":"Adetayo Kasim, Nathan W Bean, Elena Parkhomenko, Amelia Cottle, Andre Acusta, Helen Zhou, Tai-Tsang Chen, Antony Sabin, Matthew A Psioda","doi":"10.1093/biomtc/ujag156","DOIUrl":"https://doi.org/10.1093/biomtc/ujag156","url":null,"abstract":"Dose finding and optimization studies are important in oncology drug development for making new drugs available to patients at pace and for reducing the risk of toxicity. The requirement by Project Optimus to conduct a randomized dose optimization study necessitates a change in the oncology drug development paradigm, and standard dose-response modeling approaches (e.g., Multiple Comparisons Procedure-Modeling, MCP-Mod) are not always applicable due to small sample sizes and a small number of doses that are typical of dose optimization studies. An innovative Bayesian model averaging method for dose ranging studies (BAMADOS) is proposed for the design and analysis of oncology trials with binary endpoints. The method assumes a monotonic relationship between response and doses of an investigational drug. It does not require pre-specification of candidate models but instead evaluates all possible models in a constrained model space. To minimize the potential impact of the Occam's razor property, non-conjugate moderately informative priors from the family of generalized normal priors are implemented. We show via simulation studies that BAMADOS correctly identifies the optimal biological dose in a dose optimization setting. It further estimates response rates with little bias, even in the presence of discordance between the priors and observed data. Compared to MCP-Mod, BAMADOS exhibited higher power for small sample sizes (30 or fewer participants per dose) and comparable power for larger sample sizes in several scenarios considered.","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":"148910693","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

Random-effects meta-analysis via generalized linear mixed models: A Bartlett-corrected approach for few studies. 通过广义线性混合模型的随机效应荟萃分析：少数研究的bartlett校正方法。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag148

Keisuke Hanada, Tomoyuki Sugimoto

{"title":"Random-effects meta-analysis via generalized linear mixed models: A Bartlett-corrected approach for few studies.","authors":"Keisuke Hanada, Tomoyuki Sugimoto","doi":"10.1093/biomtc/ujag148","DOIUrl":"10.1093/biomtc/ujag148","url":null,"abstract":"Random-effects models are central to meta-analysis, yet the between-study variance is often underestimated when the number of studies is small. In such settings, confidence intervals become unduly narrow and fail to attain the nominal coverage probability. Although several small-sample corrections, including the Bartlett correction, have been developed under the normal-normal model, corresponding methodology for generalized linear mixed models (GLMMs) remains limited. This study proposes a unified framework for random-effects meta-analysis within the GLMM framework that relies exclusively on aggregate data and accommodates outcomes following any distribution in the exponential family, including the binomial, Poisson, and gamma distributions. To improve interval estimation with few studies, we develop a profile likelihood method with a simplified Bartlett correction (PLSBC), which refines the chi-squared approximation of the profile likelihood ratio statistic without requiring higher-order derivatives. We show theoretically that the proposed estimators preserve the consistency and asymptotic normality of the maximum likelihood estimators. Simulation studies demonstrate that the PLSBC yields nearly unbiased estimates and maintains nominal coverage across a variety of outcome types. Applications to three published meta-analyses with binomial, Poisson, and gamma outcomes indicate that the proposed approach provides robust and interpretable inference with few studies. The PLSBC therefore offers a practical and broadly applicable framework for random-effects meta-analysis when the number of studies is limited.","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":"148787851","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 semiparametric modeling of biomarker variability in joint models. 联合模型中生物标志物变异的贝叶斯半参数建模。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag146

Sida Chen, Jessica K Barrett, Marco Palma, Jianxin Pan, Brian D M Tom

{"title":"Bayesian semiparametric modeling of biomarker variability in joint models.","authors":"Sida Chen, Jessica K Barrett, Marco Palma, Jianxin Pan, Brian D M Tom","doi":"10.1093/biomtc/ujag146","DOIUrl":"https://doi.org/10.1093/biomtc/ujag146","url":null,"abstract":"There is growing interest in the role of within-individual variability (WIV) in biomarker trajectories for assessing disease risk and progression. A trajectory-based definition that has attracted recent attention characterizes WIV as the curvature-based roughness of the latent biomarker trajectory, and we refer to the resulting measure as trajectory-based biomarker WIV (TB-WIV). To evaluate TB-WIV associations with clinical outcomes and perform dynamic risk prediction, joint models for longitudinal and time-to-event data (JM) are necessary. However, specifying the longitudinal trajectory is critical and poses methodological challenges. In this work, we investigate three Bayesian semiparametric approaches for longitudinal modeling and TB-WIV estimation. We propose two new approaches based on Bayesian penalized splines (P-splines) and functional principal component analysis, and adapt an existing semiparametric approach to the Bayesian JM setting. Through simulation studies, we evaluate parameter estimation, detection of TB-WIV associations, estimation of their raw and standardized magnitudes, and survival prediction, providing insights for method choice and highlighting the need for caution when interpreting raw TB-WIV association magnitudes. We apply the approaches to UK Cystic Fibrosis Registry data, where we identify significant positive associations between lung function TB-WIV and mortality risk in patients with cystic fibrosis, after adjustment for the underlying lung function level, and provide evidence that TB-WIV adds information for survival prediction.","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":"148787751","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

Statistical analysis of disease onset during lifespan with left truncation. 左截短患者生命期发病的统计分析。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag118

Yue Wang, Bin Nan, Daniel L Gillen, Maria M Corrada

引用次数: 0