Biostatistics最新文献

Cure rate joint model for time-to-event data and longitudinal tumor burden with potential change points. 基于时间-事件数据和具有潜在变化点的纵向肿瘤负荷的治愈率联合模型。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag025

Yixiang Qu, Ethan M Alt, Weibin Zhong, Jeen Liu, Chenguang Wang, Joseph G Ibrahim

{"title":"Cure rate joint model for time-to-event data and longitudinal tumor burden with potential change points.","authors":"Yixiang Qu, Ethan M Alt, Weibin Zhong, Jeen Liu, Chenguang Wang, Joseph G Ibrahim","doi":"10.1093/biostatistics/kxag025","DOIUrl":"10.1093/biostatistics/kxag025","url":null,"abstract":"In non-small cell lung cancer (NSCLC) clinical trials, tumor burden (TB) is a key longitudinal biomarker for assessing treatment effects. Typically, standard-of-care (SOC) therapies and some novel interventions initially decrease TB; however, many patients subsequently experience an increase-indicating disease progression-while others show a continuous decline. In patients with an eventual TB increase, the change point marks the onset of progression and must occur before the time of the event. To capture these dynamics, we propose a cure-rate joint model that integrates time-to-event and longitudinal TB data and distinguishes change-point and non-change-point trajectory groups. For the change-point group, our approach flexibly estimates an individualized change point by leveraging time-to-event information. We use a Monte Carlo Expectation-Maximization (MCEM) algorithm for efficient parameter estimation. Simulation studies show that our model can recover diverse disease progression patterns and yield robust marginal TB outcome estimates while accommodating censoring complexity. When applied to a Phase 3 NSCLC trial comparing cemiplimab monotherapy to SOC, the treatment group demonstrates delayed tumor regrowth and consistently lower TB over time, highlighting the clinical utility of our approach. The implementation code is publicly available on https://github.com/quyixiang/JoCuR.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148610235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 heritability of longitudinal rate-of-change: genetic insights into PSA velocity in prostate cancer-free individuals. 估计纵向变化率的遗传性：前列腺癌无个体中PSA速度的遗传见解。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag015

Pei Zhang, Xiaoyu Wang, Jianxin Shi, Paul S Albert

{"title":"Estimating the heritability of longitudinal rate-of-change: genetic insights into PSA velocity in prostate cancer-free individuals.","authors":"Pei Zhang, Xiaoyu Wang, Jianxin Shi, Paul S Albert","doi":"10.1093/biostatistics/kxag015","DOIUrl":"10.1093/biostatistics/kxag015","url":null,"abstract":"Serum prostate-specific antigen (PSA) is widely used for prostate cancer screening. While the genetics of PSA levels have been studied to enhance screening accuracy, the genetic basis of PSA velocity, the rate of PSA change over time, remains unclear. The Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, a large, randomized study with longitudinal PSA data (15,260 cancer-free males, averaging 5.34 samples per subject) and genome-wide genotype data, provides a unique opportunity to estimate PSA velocity heritability. We developed a mixed model to jointly estimate the heritability of PSA levels at age 54 and PSA velocity. To accommodate the large dataset, we implemented 2 efficient computational approaches: a partitioning and meta-analysis strategy using average information restricted maximum likelihood (AI-REML) and a fast restricted Haseman-Elston (REHE) regression method. Simulations showed that both methods yield unbiased estimates of both heritability metrics, with AI-REML providing smaller variability in the estimation of velocity heritability than REHE. Applying AI-REML to PLCO data, we estimated heritability at 0.32 (s.e. = 0.07) for baseline PSA and 0.45 (s.e. = 0.18) for PSA velocity. These findings reveal a substantial genetic contribution to PSA velocity, supporting future genome-wide studies to identify variants affecting PSA dynamics and improve PSA-based screening.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13379640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148474535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

IV-learner: learning conditional average treatment effects using instrumental variables. iv型学习者：使用工具变量学习条件平均处理效果。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag009

Stijn Vansteelandt, Stephen O'Neill, Richard Grieve, Karla Diaz-Ordaz

{"title":"IV-learner: learning conditional average treatment effects using instrumental variables.","authors":"Stijn Vansteelandt, Stephen O'Neill, Richard Grieve, Karla Diaz-Ordaz","doi":"10.1093/biostatistics/kxag009","DOIUrl":"10.1093/biostatistics/kxag009","url":null,"abstract":"A live clinical question is: which patients benefit from intensive care unit (ICU) transfer? Motivated by this question we address the problem of estimating conditional average treatment effects (CATE) in the presence of unmeasured confounding, by leveraging instrumental variables (IVs). CATE-learners (eg R-learner) developed for settings without unmeasured confounding can readily incorporate IVs by substituting the observed exposure with first-stage predictions from a regression of treatment on IVs and covariates. Such predictions may be obtained via flexible data-adaptive methods (eg statistical or machine learning procedures) to alleviate concerns about model misspecification. However, the large regularization bias typical of data-adaptive predictions may propagate into the CATE estimates, resulting in poor accuracy. Neyman-orthogonal learners have therefore been developed, which prevent this by \"insulating\" the resulting CATE estimates against bias and estimation errors in these predictions. However, synthetic data simulations reveal that previously proposed Neyman-orthogonal learners for IV regression perform poorly. We remedy this problem using infinite-dimensional targeted learning, which strategically tailors first-stage predictions to perform well in their ultimate task: delivering accurate, precise CATE estimates. The resulting targeted Neyman-orthogonal learner is easy to construct based on arbitrary, off-the-shelf learners. It can handle continuous or discrete exposures, and arbitrary types and numbers of IVs and covariates. Simulation studies and a re-analysis of the benefits of ICU transfer show substantial enhancements in performance, underscoring the importance of the proposed IV-learner.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147977605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Addressing the influence of unmeasured confounding in observational studies with time-to-event outcomes: a semiparametric sensitivity analysis approach. 在具有事件时间结果的观察性研究中处理未测量混杂因素的影响：半参数敏感性分析方法。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag005

Linda Amoafo, Shiyao Xu, Elizabeth Platz, Daniel Scharfstein

引用次数: 0

Multivariate functional linear discriminant analysis with feature selection: an application to inflammatory bowel disease classification. 具有特征选择的多元功能线性判别分析：在炎症性肠病分类中的应用。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag027

Limeng Liu, Guannan Wang, Sandra E Safo

{"title":"Multivariate functional linear discriminant analysis with feature selection: an application to inflammatory bowel disease classification.","authors":"Limeng Liu, Guannan Wang, Sandra E Safo","doi":"10.1093/biostatistics/kxag027","DOIUrl":"10.1093/biostatistics/kxag027","url":null,"abstract":"Inflammatory Bowel Disease (IBD), including Crohn's Disease (CD) and Ulcerative Colitis (UC), presents significant public health challenges due to its complex etiology. Motivated by the IBD study of the Integrative Human Microbiome Project, our objective is to identify microbial pathways that distinguish between CD, UC, and non-IBD over time. Most current research relies on simplistic analyses that examine 1 variable or time point at a time, or address binary classification problems, limiting our understanding of the dynamic interactions within the microbiome over time. To address these limitations, we develop a novel functional data analysis approach for discriminant analysis of multivariate functional data that can effectively handle multiple high-dimensional predictors, sparse time points, and categorical outcomes. Our method seeks linear combinations of functions (ie discriminant functions) that maximize separation between 2 or more classes over time. We impose a sparsity-inducing penalty when estimating the discriminant functions, allowing us to identify relevant discriminating variables over time. Applications of our method to the motivating data identified microbial features related to mucin degradation, amino acid metabolism, and peptidoglycan recognition, which are implicated in the progression and development of IBD. Furthermore, our method highlighted the role of multiple vitamin B deficiencies in the context of IBD. By moving beyond traditional analytical frameworks, our innovative approach holds the potential for uncovering clinically meaningful discoveries in IBD research.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13420537/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148622464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Dynamic case-control sampling for rapid estimation of vaccine effectiveness against an emerging infectious disease variant. 动态病例对照抽样用于快速估计疫苗对新发传染病变体的有效性。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag002

Taylor M Fortnam, Laura C Chambers, Alyssa Bilinski, Ewa King, Richard C Huard, Ellen Amore, Lisa M Gargano, Jim McDonald, Philip A Chan, Joseph W Hogan

{"title":"Dynamic case-control sampling for rapid estimation of vaccine effectiveness against an emerging infectious disease variant.","authors":"Taylor M Fortnam, Laura C Chambers, Alyssa Bilinski, Ewa King, Richard C Huard, Ellen Amore, Lisa M Gargano, Jim McDonald, Philip A Chan, Joseph W Hogan","doi":"10.1093/biostatistics/kxag002","DOIUrl":"10.1093/biostatistics/kxag002","url":null,"abstract":"New SARS-CoV-2 variants arise frequently with different viral properties that can impact the effectiveness of the vaccines. Updating estimates of vaccine effectiveness (VE) in public health surveillance can be limited by the necessity of conducting a distinct study that entails analysis of prospective cohort data or using a test-negative design. We introduce a method for dynamically updating estimates of VE using data that accumulate in real time. Our method uses dynamic case-control sampling to estimate VE against a newly emerging variant relative to a previous variant. Dynamic case-control sampling is a technique that continuously updates VE estimates by comparing individuals infected with a newly emerging variant (defined as \"cases\") to those infected with a previously circulating variant (defined as \"controls\"). We use this estimate in combination with information about VE from the previous variant (these estimates are typically available from larger, traditional studies) to infer VE against the emerging variant. We demonstrate the utility of this method on the BA.1 and BA.2 sub-lineages of the Omicron variant. The method produces estimates of VE comparable to those produced using traditional methods, although with increased SE. The increase in error, however, is reasonable given a much smaller sample size than other studies, and error ranges of the estimates could be significantly improved by sequencing a larger proportion of identified cases. Our method, which assumes only a fraction of the new cases are being sequenced, can be applied by health departments using routinely collected data to produce timely, rigorous VE estimates to rapidly identify potential changes in VE.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147678571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis. NBSR：用于microRNA-seq数据分析的负二项式Softmax回归模型。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag012

Seong-Hwan Jun, Marc K Halushka, Matthew N McCall

{"title":"NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.","authors":"Seong-Hwan Jun, Marc K Halushka, Matthew N McCall","doi":"10.1093/biostatistics/kxag012","DOIUrl":"10.1093/biostatistics/kxag012","url":null,"abstract":"MicroRNAs play a central role in the regulation of gene expression and the modulation of diseases. Despite their importance, statistical methods for analyzing microRNAs have received less attention compared to messenger RNAs. Critically, messenger RNA sequencing methods are often applied to analyze microRNA sequencing data without considering the unique characteristics of microRNAs. This study examines the assumptions of messenger RNA-based methods and shows that they may incur high false discovery rates. We propose a negative binomial softmax regression (NBSR) model for microRNA sequencing data. Our approach has several advantages over existing methods. First, differential expression across experimental conditions is interpreted using the log relative abundance ratio (log-RAR). Second, it achieves greater statistical power and narrower confidence intervals by modeling the relationship between the biological coefficient of variation and relative abundance. Third, NBSR effectively handles highly variable and sparsely expressed microRNAs, resulting in improved sensitivity for detecting differential expression. Additionally, we show that debiasing the log-RAR enables accurate inference of fold changes in absolute abundance, particularly when only a small subset of microRNAs differ in expression between conditions. We demonstrate the efficacy of our approach using both real and simulated data.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13202216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148037916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 class of latent variable models for the analysis of antibody response data. 一类灵活的潜在变量模型用于抗体应答数据的分析。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag008

Emanuele Giorgi, Jonas Wallin

{"title":"A flexible class of latent variable models for the analysis of antibody response data.","authors":"Emanuele Giorgi, Jonas Wallin","doi":"10.1093/biostatistics/kxag008","DOIUrl":"10.1093/biostatistics/kxag008","url":null,"abstract":"Existing approaches to modelling antibody concentration data are mostly based on finite mixture models that rely on the assumption that individuals can be divided into 2 distinct groups: seronegative and seropositive. Here, we challenge this dichotomous modelling assumption and propose a latent variable modelling framework in which the immune status of each individual is represented along a continuum of latent seroreactivity, ranging from minimal to strong immune activation. This formulation provides greater flexibility in capturing age-related changes in antibody distributions while preserving the full information content of quantitative measurements. We show that the proposed class of models can accommodate a large variety of model formulations, both mechanistic and regression-based, and also includes finite mixture models as a special case. We also propose a computationally efficient $ L_{2} $-based estimator as an alternative to maximum likelihood estimation, which substantially reduces computational cost, and we establish its consistency. Through a case study on malaria serology, we demonstrate how the flexibility of the novel framework enables joint analyses across all ages while accounting for changes in transmission patterns. We conclude by outlining extensions of the proposed modelling framework and its relevance to other omics applications.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13273455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 novel decomposition to explain heterogeneity in observational and randomized studies of causality. 一种新的分解来解释因果关系的观察性和随机研究中的异质性。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag030

Brian Gilbert, Iván Díaz, Kara Rudolph, Nicholas Williams, Tat-Thang Vo

引用次数: 0

transfactor: transcription factor activity estimation via probabilistic gene expression deconvolution. 转录因子：通过概率基因表达反褶积来估计转录因子的活性。

IF 2.1 3区数学

Biostatistics Pub Date : 2026-01-20 DOI: 10.1093/biostatistics/kxag029

Koen Van den Berge, Peter Bickel, Sandrine Dudoit

{"title":"transfactor: transcription factor activity estimation via probabilistic gene expression deconvolution.","authors":"Koen Van den Berge, Peter Bickel, Sandrine Dudoit","doi":"10.1093/biostatistics/kxag029","DOIUrl":"10.1093/biostatistics/kxag029","url":null,"abstract":"Gene expression is a primary modality being studied to differentiate between biological cells. Contemporary single-cell studies simultaneously measure genome-wide transcription levels for thousands of individual cells in a single experiment. While the characterization of cell population differences has often occurred through differential gene expression analysis, tiny effect sizes become statistically significant when thousands of cells are available for each population, compromising biological interpretation. Moreover, these large studies have spurred the development of methods to infer gene regulatory networks (GRNs) directly from the data, and GRN databases are becoming more comprehensive. In this work, we propose a statistical model for gene expression measures and an inference method that leverage GRNs to deconvolve transcription factor (TF) activity from gene expression, by probabilistically assigning mRNA molecules to TFs. This shifts the paradigm from investigating gene expression differences to regulatory differences at the level of TF activity, aiding interpretation and allowing prioritization of a limited number of TFs responsible for significant contributions to the observed gene expression differences. The inferred TF activities result in intuitive prioritization of TFs in terms of the (difference in) estimated number of molecules they produce, in contrast to other widely used methods relying on arbitrary enrichment scores. Our model allows the incorporation of prior information on the regulatory potential between each TF and target gene and is able to deal with both repressing and activating interactions. We compare our approach to other TF activity estimation methods using two simulation experiments and two case studies. Single-cell RNA-sequencing; TF activity; bioinformatics; GRN.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148914473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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