Biometrics最新文献_第2页

A zero-inflated hierarchical generalized transformation model to address non-normality in spatially-informed cell-type deconvolution. 一个零膨胀的层次广义变换模型来处理空间信息胞型反褶积中的非正态性。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag055

Hunter J Melton, Jonathan R Bradley, Chong Wu

{"title":"A zero-inflated hierarchical generalized transformation model to address non-normality in spatially-informed cell-type deconvolution.","authors":"Hunter J Melton, Jonathan R Bradley, Chong Wu","doi":"10.1093/biomtc/ujag055","DOIUrl":"10.1093/biomtc/ujag055","url":null,"abstract":"Oral squamous cell carcinomas (OSCC), the predominant head and neck cancer, pose significant challenges due to late-stage diagnoses and low five-year survival rates. Spatial transcriptomics offers a promising avenue to decipher the genetic intricacies of OSCC tumor microenvironments. In spatial transcriptomics, Cell-type deconvolution is a crucial inferential goal; however, current methods fail to consider the high zero-inflation present in OSCC data. To address this, we develop a novel zero-inflated version of the hierarchical generalized transformation model (ZI-HGT) and apply it to the Conditional AutoRegressive Deconvolution (CARD) for cell-type deconvolution. The ZI-HGT serves as an auxiliary Bayesian technique for CARD, reconciling the highly zero-inflated OSCC spatial transcriptomics data with CARD's normality assumption. The combined ZI-HGT + CARD framework achieves enhanced cell-type deconvolution accuracy and quantifies uncertainty in the estimated cell-type proportions. We demonstrate the superior performance through simulations and analysis of the OSCC data. Furthermore, our approach enables the determination of the locations of the diverse fibroblast population in the tumor microenvironment, critical for understanding tumor growth and immunosuppression in OSCC.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13087646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147697379","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

Correction to: Surrogate Measures and Consistent Surrogates. 更正：替代措施和一致的替代措施。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag060

引用次数: 0

Borrowing information from an unidentifiable model: Guaranteed efficiency gain with a dichotomized outcome in the external data. 从一个无法识别的模型中借用信息：通过外部数据的二分类结果来保证效率的提高。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag062

Lu Wang, Yanyuan Ma, Jiwei Zhao

{"title":"Borrowing information from an unidentifiable model: Guaranteed efficiency gain with a dichotomized outcome in the external data.","authors":"Lu Wang, Yanyuan Ma, Jiwei Zhao","doi":"10.1093/biomtc/ujag062","DOIUrl":"10.1093/biomtc/ujag062","url":null,"abstract":"In the era of big data, the increasing availability of diverse data sources has driven interest in analytical approaches that integrate information across sources to enhance statistical accuracy, efficiency, and scientific insights. Many existing methods assume exchangeability among data sources and often implicitly require that sources measure identical covariates or outcomes, or that the error distribution is correctly specified-assumptions that may not hold in complex real-world scenarios. This paper explores the integration of data from sources with distinct outcome scales, focusing on leveraging external data to improve statistical efficiency. Specifically, we consider a scenario where the primary dataset includes a continuous outcome, and external data provides a dichotomized version of the same outcome. We propose two novel estimators: the first estimator remains asymptotically consistent even when the error distribution is potentially misspecified, while the second estimator guarantees an efficiency gain over weighted least squares estimation that uses the primary study data alone. Theoretical properties of these estimators are rigorously derived, and extensive simulation studies are conducted to highlight their robustness and efficiency gains across various scenarios. Finally, a real-world application using the NHANES dataset demonstrates the practical utility of the proposed methods.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13126646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147760513","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

Long-term memory effects of an incremental blood pressure intervention in a mortal cohort. 增加血压干预对人类长期记忆的影响。

IF 1.7 4区数学

Biometrics Pub Date : 2026-01-06 DOI: 10.1093/biomtc/ujaf176

Maria Josefsson, Nina Karalija, Michael J Daniels

{"title":"Long-term memory effects of an incremental blood pressure intervention in a mortal cohort.","authors":"Maria Josefsson, Nina Karalija, Michael J Daniels","doi":"10.1093/biomtc/ujaf176","DOIUrl":"10.1093/biomtc/ujaf176","url":null,"abstract":"In the present study, we examine long-term population-level effects on episodic memory of an intervention over 15 years that reduces systolic blood pressure in individuals with hypertension. A limitation with previous research on the potential risk reduction of such interventions is that they do not properly account for the reduction of mortality rates. Hence, one can only speculate whether the effect is due to changes in memory or changes in mortality. Therefore, we extend previous research by providing both an etiological and a prognostic effect estimate. To do this, we propose a Bayesian semi-parametric estimation approach for an incremental threshold intervention, using the extended G-formula. Additionally, we introduce a novel sparsity-inducing Dirichlet prior for longitudinal data, that exploits the longitudinal structure of the data. We demonstrate the usefulness of our approach in simulations, and compare its performance to other Bayesian decision tree ensemble approaches. In our analysis of the data from the Betula cohort, we found no significant prognostic or etiological effects across all ages. This suggests that systolic blood pressure interventions likely do not strongly affect memory, either at the overall population level or among individuals who would remain alive under both the natural course and the intervention (the always survivor stratum).","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12865380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103661","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 inference for Cox regression models using catalytic prior distributions. 使用催化先验分布的Cox回归模型的贝叶斯推断。

IF 1.7 4区数学

Biometrics Pub Date : 2026-01-06 DOI: 10.1093/biomtc/ujag004

Weihao Li, Dongming Huang

{"title":"Bayesian inference for Cox regression models using catalytic prior distributions.","authors":"Weihao Li, Dongming Huang","doi":"10.1093/biomtc/ujag004","DOIUrl":"https://doi.org/10.1093/biomtc/ujag004","url":null,"abstract":"The Cox proportional hazards model (Cox model) is a popular model for survival data analysis. When the sample size is small relative to the dimension of the model, the standard maximum partial likelihood inference is often problematic. In this work, we propose the Cox catalytic prior distribution for Bayesian inference on Cox models, which extends a general class of prior distributions originally designed to stabilize complex parametric models. The Cox catalytic prior is formulated as a weighted likelihood of the regression coefficients derived from synthetic data and a surrogate baseline hazard constant. This surrogate hazard can be either provided by the user or estimated from the data, and the synthetic data are generated from the predictive distribution of a fitted simpler model. For point estimation, we derive an approximation of the marginal posterior mode, which can be computed conveniently as a regularized log partial likelihood estimator. We prove that our prior distribution is proper and the resulting estimator is consistent under mild conditions. In simulation studies, our proposed method outperforms standard maximum partial likelihood inference and is on par with existing shrinkage methods. We further illustrate the application of our method to a real dataset.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103716","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

Rejoinder to reader reaction "Comment on 'Double robust conditional independence test for novel biomarkers given established risk factors with survival data' by Lucas Kook". 对读者反应的回复“Lucas Kook对“基于生存数据的风险因素的新型生物标志物的双重稳健条件独立测试”的评论”。

IF 1.7 4区数学

Biometrics Pub Date : 2026-01-06 DOI: 10.1093/biomtc/ujag038

Baoying Yang, Jing Qin, Jing Ning, Yukun Liu

引用次数: 0

Optimal design of dynamic experiments for scalar-on-function linear models with application to a biopharmaceutical study. 函数上标度线性模型的动态实验优化设计及其在生物制药研究中的应用。

IF 1.7 4区数学

Biometrics Pub Date : 2026-01-06 DOI: 10.1093/biomtc/ujaf169

Damianos Michaelides, Maria Adamou, David C Woods, Antony M Overstall

引用次数: 0

An adaptive design for optimizing treatment assignment in randomized clinical trials. 随机临床试验中优化治疗分配的自适应设计。

IF 1.7 4区数学

Biometrics Pub Date : 2026-01-06 DOI: 10.1093/biomtc/ujaf168

Wei Zhang, Zhiwei Zhang, Aiyi Liu

{"title":"An adaptive design for optimizing treatment assignment in randomized clinical trials.","authors":"Wei Zhang, Zhiwei Zhang, Aiyi Liu","doi":"10.1093/biomtc/ujaf168","DOIUrl":"10.1093/biomtc/ujaf168","url":null,"abstract":"The treatment assignment mechanism in a randomized clinical trial can be optimized for statistical efficiency within a specified class of randomization mechanisms. Optimal designs of this type have been characterized in terms of the variances of potential outcomes conditional on baseline covariates. Approximating these optimal designs requires information about the conditional variance functions, which is often unavailable or unreliable at the design stage. As a practical solution to this dilemma, we propose a multi-stage adaptive design that allows the treatment assignment mechanism to be modified at interim analyses based on accruing information about the conditional variance functions. This adaptation has profound implications on the distribution of trial data, which need to be accounted for in treatment effect estimation. We consider a class of treatment effect estimators that are consistent and asymptotically normal, identify the most efficient estimator within this class, and approximate the most efficient estimator by substituting estimates of unknown quantities. Simulation results indicate that, when there is little or no prior information available, the proposed design can bring substantial efficiency gains over conventional one-stage designs based on the same prior information. The methodology is illustrated with real data from a completed trial in stroke.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145916646","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 the causal effect of redlining on present-day air pollution. 估计红线对当今空气污染的因果影响。

IF 1.7 4区数学

Biometrics Pub Date : 2026-01-06 DOI: 10.1093/biomtc/ujaf173

Xiaodan Zhou, Shu Yang, Brian J Reich

{"title":"Estimating the causal effect of redlining on present-day air pollution.","authors":"Xiaodan Zhou, Shu Yang, Brian J Reich","doi":"10.1093/biomtc/ujaf173","DOIUrl":"10.1093/biomtc/ujaf173","url":null,"abstract":"Recent studies have shown associations between redlining policies (1935-1974) and present-day fine particulate matter (PM$_{2.5}$) and nitrogen dioxide (NO$_2$) air pollution concentrations. In this paper, we move beyond associations and investigate the causal effects of redlining using spatial causal inference. Redlining policies were enacted in the 1930s, so there is very limited documentation of pre-treatment covariates. Consequently, traditional methods failed to sufficiently account for unmeasured confounders, potentially biasing causal interpretations. By integrating historical redlining data with 2010 PM$_{2.5}$ and NO$_2$ concentrations, our study seeks to estimate the long-term causal impact. Our study addresses challenges with a novel spatial and non-spatial latent factor framework, using the unemployment rate, house rent and percentage of Black population in 1940 US Census as proxies to reconstruct pre-treatment latent socio-economic status. We establish identification of a causal effect under broad assumptions, and use Bayesian Markov Chain Monte Carlo to quantify uncertainty. Our causal analysis provides evidence that historically redlined neighborhoods are exposed to notably higher NO$_2$ concentration. In contrast, the disparities in PM$_{2.5}$ between these neighborhoods are less pronounced. Among the cities analyzed, Los Angeles, CA, and Atlanta, GA, demonstrate the most significant effects for both NO$_2$ and PM$_{2.5}$.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12805554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145984363","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

Doubly balanced samples with dynamic sample sizes. 具有动态样本量的双重平衡样本。

IF 1.7 4区数学

Biometrics Pub Date : 2026-01-06 DOI: 10.1093/biomtc/ujag011

Blair Robertson, Chris Price, Marco Reale, Philip Davies

引用次数: 0