Biometrics最新文献_第10页

Q-Learning with clustered-SMART (cSMART) data: examining moderators in the construction of clustered adaptive interventions. 基于聚类智能（cSMART）数据的q学习：检验聚类自适应干预构建中的调节因子。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag078

Yao Song, Kelly Speth, Amy Kilbourne, Andrew Quanbeck, Daniel Almirall, Lu Wang

{"title":"Q-Learning with clustered-SMART (cSMART) data: examining moderators in the construction of clustered adaptive interventions.","authors":"Yao Song, Kelly Speth, Amy Kilbourne, Andrew Quanbeck, Daniel Almirall, Lu Wang","doi":"10.1093/biomtc/ujag078","DOIUrl":"10.1093/biomtc/ujag078","url":null,"abstract":"A clustered adaptive intervention (cAI) is a prespecified sequence of decision rules that guides practitioners on how best-and based on which measures-to tailor cluster-level intervention to improve outcomes at the level of individuals within the clusters. A clustered sequential multiple assignment randomized trial (cSMART) is a type of trial that is used to inform the empirical development of a cAI. A common analytic goal in a cSMART focuses on assessing causal effect moderation by candidate tailoring variables. We introduce a clustered Q-learning framework with the M-out-of-N cluster bootstrap using data from a cSMART to evaluate whether a set of candidate tailoring variables may be useful in defining an optimal cAI. This approach could construct confidence intervals (CIs) with near-nominal coverage to assess parameters indexing the causal effect moderation function. Specifically, it allows reliable inferences concerning the utility of candidate tailoring variables in constructing a cAI that maximizes a mean end-of-study outcome even when \"non-regularity,\" a well-known challenge, exists. Simulations demonstrate the numerical performance of the proposed method across varying non-regularity conditions and investigate the impact of varying numbers of clusters and intra-cluster correlation coefficients on CI coverage. Methods are applied on the ADEPT dataset to inform the construction of a clinic-level cAI for improving evidence-based practice in treating mood disorders.","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/PMC13192251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147980368","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 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 1.6 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":1.6,"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

Integration of aggregate data in causally interpretable meta-analysis by inverse weighting. 用逆加权法对因果可解释的meta分析中汇总数据的整合。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag107

Tat-Thang Vo, Tran Trong Khoi Le, Sivem Afach, Stijn Vansteelandt

{"title":"Integration of aggregate data in causally interpretable meta-analysis by inverse weighting.","authors":"Tat-Thang Vo, Tran Trong Khoi Le, Sivem Afach, Stijn Vansteelandt","doi":"10.1093/biomtc/ujag107","DOIUrl":"https://doi.org/10.1093/biomtc/ujag107","url":null,"abstract":"Obtaining causally interpretable meta-analysis results is challenging when there are differences in the distribution of effect modifiers between eligible trials. To overcome this, recent work on transportability methods has considered standardizing results of individual studies over the case-mix of a target population, prior to pooling them as in a classical random-effect meta-analysis. One practical challenge, however, is that case-mix standardization often requires individual participant data (IPD) on outcome, treatments and case-mix characteristics to be fully accessible in every eligible study, along with IPD case-mix characteristics for a random sample from the target population. In this paper, we develop novel strategies to incorporate aggregate-level data into a causal meta-analysis when IPD are available for at least one eligible trial. Our approach extends moment-based methods that are commonly used for population-adjusted indirect comparisons in health technology assessment. Since valid inference for these moment-based methods by M-estimation theory requires additional aggregated data that are often unavailable in practice, computational methods to address this concern are also developed. We assess the finite-sample performance of the proposed approaches by simulated data, and then apply these on real-world clinical data to investigate the effectiveness of risankizumab versus ustekinumab among patients with moderate to severe psoriasis.","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":"148269746","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 inference for mean function of partially observed functional time series. 部分观测函数时间序列均值函数的统计推断。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag111

Shuang Sun, Leheng Cai, Qirui Hu

引用次数: 0

Causally-interpretable random-effects meta-analysis. 因果可解释的随机效应荟萃分析。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag108

Justin M Clark, Kollin W Rott, James S Hodges, Jared D Huling

引用次数: 0

Practical considerations when using the covariate-adjusted log-rank test for the analysis of time-to-event endpoints in oncology trials. 在肿瘤试验中使用协变量调整对数秩检验分析事件时间终点时的实际考虑。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag109

Daniel Backenroth, Sanne Roels, Shiva Dibaj, Ting Ye, Fredrik Öhrn, Kelly Van Lancker

{"title":"Practical considerations when using the covariate-adjusted log-rank test for the analysis of time-to-event endpoints in oncology trials.","authors":"Daniel Backenroth, Sanne Roels, Shiva Dibaj, Ting Ye, Fredrik Öhrn, Kelly Van Lancker","doi":"10.1093/biomtc/ujag109","DOIUrl":"https://doi.org/10.1093/biomtc/ujag109","url":null,"abstract":"Adjusting for prognostic baseline covariates in the design and analysis of randomized controlled trials can increase statistical power and improve the precision of treatment effect estimates by reducing outcome variability. In this paper, we describe how the covariate-adjusted log-rank test can be used to enhance the power of late-phase oncology studies with primary time-to-event endpoints. Using a case study of a metastatic castration-resistant prostate cancer trial with overall survival as the primary endpoint, we demonstrate how to estimate variance reductions achievable through different covariate adjustment strategies, including adjustment for a prognostic score, based on historical data. We also compare these strategies with respect to Type I error rate control. We further describe a method that can be used to prevent Type I error rate inflation that can arise when naively embedding covariate adjustment within a group sequential design. A simulation study illustrates the performance of this method across scenarios with different prognostic covariate strength and different numbers of events, randomization strata, and interim analyses. Finally, we present a re-analysis of the overall survival endpoint from a trial in metastatic colorectal cancer using covariate adjustment in a group sequential design.","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":"148269741","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 the discussion on "INTACT: A method for integration of longitudinal physical activity data from multiple sources". 对“integrity：一种整合多来源纵向体育活动数据的方法”讨论的回应。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag117

Jingru Zhang, Erjia Cui, Hongzhe Li, Haochang Shou

引用次数: 0

A three-groups non-local model for combining heterogeneous data sources to identify genes associated with Parkinson's disease. 一个三组非局部模型，用于结合异质数据源来识别与帕金森病相关的基因。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag090

Troy P Wixson, Benjamin A Shaby, Daisy L Philtron, Leandro A Lima, Stacia K Wyman, Julia A Kaye, Steven Finkbeiner

{"title":"A three-groups non-local model for combining heterogeneous data sources to identify genes associated with Parkinson's disease.","authors":"Troy P Wixson, Benjamin A Shaby, Daisy L Philtron, Leandro A Lima, Stacia K Wyman, Julia A Kaye, Steven Finkbeiner","doi":"10.1093/biomtc/ujag090","DOIUrl":"10.1093/biomtc/ujag090","url":null,"abstract":"We seek to identify genes involved in Parkinson's disease (PD) by combining information across different experiment types. Each experiment, taken individually, may contain too little information to distinguish some important genes from incidental ones. However, when experiments are combined using the proposed statistical framework, additional power emerges. The fundamental building block of the family of statistical models that we propose is a hierarchical three-groups mixture of distributions. Each gene is modeled probabilistically as belonging to either a null group that is unassociated with PD, a deleterious group, or a beneficial group. This three-groups formalism has two key features. By apportioning prior probability of group assignments with a Dirichlet distribution, the resultant posterior group probabilities automatically account for the multiplicity inherent in analyzing many genes simultaneously. By building models for experimental outcomes conditionally on the group labels, any number of data modalities may be combined in a single coherent probability model, allowing information sharing across experiment types. These two features result in parsimonious inference with few false positives, while simultaneously enhancing power to detect signals. Simulations show that our three-groups approach performs at least as well as commonly-used tools for GWAS and RNA-seq, and in some cases it performs better. We apply our proposed approach to publicly available genome-wide association studies and RNA-seq datasets, discovering novel genes that are potential therapeutic targets.","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/PMC13221837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148101105","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 "Nonparanormal adjusted marginal inference" by Susanne Dandl and Torsten Hothorn. 论Susanne Dandl和Torsten Hothorn的“非超自然调整边际推理”。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag068

Edward Bein

引用次数: 0

Correcting random effect distributions to account for survivorship bias in individual heterogeneity Cormack-Jolly-Seber models. 校正随机效应分布以解释个体异质性Cormack-Jolly-Seber模型中的生存偏差。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag086

Blanca Sarzo, Ruth King, Rachel McCrea

{"title":"Correcting random effect distributions to account for survivorship bias in individual heterogeneity Cormack-Jolly-Seber models.","authors":"Blanca Sarzo, Ruth King, Rachel McCrea","doi":"10.1093/biomtc/ujag086","DOIUrl":"10.1093/biomtc/ujag086","url":null,"abstract":"Survivorship (or selection) bias arises within statistical analyses where the observed data are subject to some underlying selection process prior to entry into the sampled data. For example, within capture-recapture studies, a primary selection mechanism is the survival until initial capture time. The common Cormack-Jolly-Seber model conditions on the first time an individual is observed, leading to potential survivorship bias. However, while the issue of survivorship bias has been well studied in many fields, there has been little exploration within the capture-recapture framework. In particular, we focus on individual (continuous) random effect Cormack-Jolly-Seber models, where it is assumed that individuals have different survival probabilities, specified to be from some common underlying distribution. We discuss the implications of the survivorship bias within the data collection process, and describe a novel modeling approach that accounts for the survivorship bias within an ecologically sensible manner. Using simulated data, we demonstrate the significant impact of ignoring the survivorship bias present in the data. We fit the corrected model to a guillemot data set and demonstrate that even with relatively mild selection bias, the individual heterogeneity variability is substantially underestimated when ignoring this survivorship bias.","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":"147980426","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