Biostatistics最新文献_第3页

Correction to: Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes. 修正：在一项全国性的医院盈利状况和心脏病发作结果的观察性研究中，可扩展的核平衡权值。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae050

引用次数: 0

Fast standard error estimation for joint models of longitudinal and time-to-event data based on stochastic EM algorithms. 基于随机 EM 算法的纵向数据和时间到事件数据联合模型的快速标准误差估计。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae043

Tingting Yu, Lang Wu, Ronald J Bosch, Davey M Smith, Rui Wang

{"title":"Fast standard error estimation for joint models of longitudinal and time-to-event data based on stochastic EM algorithms.","authors":"Tingting Yu, Lang Wu, Ronald J Bosch, Davey M Smith, Rui Wang","doi":"10.1093/biostatistics/kxae043","DOIUrl":"10.1093/biostatistics/kxae043","url":null,"abstract":"Maximum likelihood inference can often become computationally intensive when performing joint modeling of longitudinal and time-to-event data, due to the intractable integrals in the joint likelihood function. The computational challenges escalate further when modeling HIV-1 viral load data, owing to the nonlinear trajectories and the presence of left-censored data resulting from the assay's lower limit of quantification. In this paper, for a joint model comprising a nonlinear mixed-effect model and a Cox Proportional Hazards model, we develop a computationally efficient Stochastic EM (StEM) algorithm for parameter estimation. Furthermore, we propose a novel technique for fast standard error estimation, which directly estimates standard errors from the results of StEM iterations and is broadly applicable to various joint modeling settings, such as those containing generalized linear mixed-effect models, parametric survival models, or joint models with more than two submodels. We evaluate the performance of the proposed methods through simulation studies and apply them to HIV-1 viral load data from six AIDS Clinical Trials Group studies to characterize viral rebound trajectories following the interruption of antiretroviral therapy (ART), accounting for the informative duration of off-ART periods.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632694","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

Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting. 通过交叉拟合，加快基于 R2 的高维中介效应的区间估计。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae037

Zhichao Xu, Chunlin Li, Sunyi Chi, Tianzhong Yang, Peng Wei

{"title":"Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting.","authors":"Zhichao Xu, Chunlin Li, Sunyi Chi, Tianzhong Yang, Peng Wei","doi":"10.1093/biostatistics/kxae037","DOIUrl":"10.1093/biostatistics/kxae037","url":null,"abstract":"Mediation analysis is a useful tool in investigating how molecular phenotypes such as gene expression mediate the effect of exposure on health outcomes. However, commonly used mean-based total mediation effect measures may suffer from cancellation of component-wise mediation effects in opposite directions in the presence of high-dimensional omics mediators. To overcome this limitation, we recently proposed a variance-based R-squared total mediation effect measure that relies on the computationally intensive nonparametric bootstrap for confidence interval estimation. In the work described herein, we formulated a more efficient two-stage, cross-fitted estimation procedure for the R2 measure. To avoid potential bias, we performed iterative Sure Independence Screening (iSIS) in two subsamples to exclude the non-mediators, followed by ordinary least squares regressions for the variance estimation. We then constructed confidence intervals based on the newly derived closed-form asymptotic distribution of the R2 measure. Extensive simulation studies demonstrated that this proposed procedure is much more computationally efficient than the resampling-based method, with comparable coverage probability. Furthermore, when applied to the Framingham Heart Study, the proposed method replicated the established finding of gene expression mediating age-related variation in systolic blood pressure and identified the role of gene expression profiles in the relationship between sex and high-density lipoprotein cholesterol level. The proposed estimation procedure is implemented in R package CFR2M.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481495","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

Model-based multifacet clustering with high-dimensional omics applications. 基于模型的多面聚类与高维 omics 应用。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae020

Wei Zong, Danyang Li, Marianne L Seney, Colleen A Mcclung, George C Tseng

引用次数: 0

Testing for a difference in means of a single feature after clustering. 聚类后对单个特征的均值差异进行测试。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae046

Yiqun T Chen, Lucy L Gao

引用次数: 0

Adaptive Gaussian Markov random fields for child mortality estimation. 用于儿童死亡率估算的自适应高斯马尔可夫随机场。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae030

Serge Aleshin-Guendel, Jon Wakefield

引用次数: 0

Stochastic EM algorithm for partially observed stochastic epidemics with individual heterogeneity. 具有个体异质性的部分观测随机流行病的随机 EM 算法。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae018

Fan Bu, Allison E Aiello, Alexander Volfovsky, Jason Xu

引用次数: 0

A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology. 在环境流行病学中考虑暴露测量误差的可扩展两阶段贝叶斯方法。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae038

Changwoo J Lee, Elaine Symanski, Amal Rammah, Dong Hun Kang, Philip K Hopke, Eun Sug Park

{"title":"A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology.","authors":"Changwoo J Lee, Elaine Symanski, Amal Rammah, Dong Hun Kang, Philip K Hopke, Eun Sug Park","doi":"10.1093/biostatistics/kxae038","DOIUrl":"10.1093/biostatistics/kxae038","url":null,"abstract":"Accounting for exposure measurement errors has been recognized as a crucial problem in environmental epidemiology for over two decades. Bayesian hierarchical models offer a coherent probabilistic framework for evaluating associations between environmental exposures and health effects, which take into account exposure measurement errors introduced by uncertainty in the estimated exposure as well as spatial misalignment between the exposure and health outcome data. While two-stage Bayesian analyses are often regarded as a good alternative to fully Bayesian analyses when joint estimation is not feasible, there has been minimal research on how to properly propagate uncertainty from the first-stage exposure model to the second-stage health model, especially in the case of a large number of participant locations along with spatially correlated exposures. We propose a scalable two-stage Bayesian approach, called a sparse multivariate normal (sparse MVN) prior approach, based on the Vecchia approximation for assessing associations between exposure and health outcomes in environmental epidemiology. We compare its performance with existing approaches through simulation. Our sparse MVN prior approach shows comparable performance with the fully Bayesian approach, which is a gold standard but is impossible to implement in some cases. We investigate the association between source-specific exposures and pollutant (nitrogen dioxide [NO2])-specific exposures and birth weight of full-term infants born in 2012 in Harris County, Texas, using several approaches, including the newly developed method.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378644","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 modeling framework for detecting and leveraging node-level information in Bayesian network inference. 在贝叶斯网络推理中检测和利用节点级信息的建模框架。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae021

Xiaoyue Xi, Hélène Ruffieux

{"title":"A modeling framework for detecting and leveraging node-level information in Bayesian network inference.","authors":"Xiaoyue Xi, Hélène Ruffieux","doi":"10.1093/biostatistics/kxae021","DOIUrl":"10.1093/biostatistics/kxae021","url":null,"abstract":"Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in a principled way, the increasing information collected alongside the data of primary interest constitutes an opportunity to mitigate these difficulties by guiding the detection of dependence structures. For instance, gene network inference may be informed by the use of publicly available summary statistics on the regulation of genes by genetic variants. Here we present a novel Gaussian graphical modeling framework to identify and leverage information on the centrality of nodes in conditional independence graphs. Specifically, we consider a fully joint hierarchical model to simultaneously infer (i) sparse precision matrices and (ii) the relevance of node-level information for uncovering the sought-after network structure. We encode such information as candidate auxiliary variables using a spike-and-slab submodel on the propensity of nodes to be hubs, which allows hypothesis-free selection and interpretation of a sparse subset of relevant variables. As efficient exploration of large posterior spaces is needed for real-world applications, we develop a variational expectation conditional maximization algorithm that scales inference to hundreds of samples, nodes and auxiliary variables. We illustrate and exploit the advantages of our approach in simulations and in a gene network study which identifies hub genes involved in biological pathways relevant to immune-mediated diseases.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452158","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

Bayesian subtyping for multi-state brain functional connectome with application on preadolescent brain cognition. 多状态脑功能连接体贝叶斯分型及其在青春期前脑认知中的应用。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae045

Tianqi Chen, Hongyu Zhao, Chichun Tan, Todd Constable, Sarah Yip, Yize Zhao

{"title":"Bayesian subtyping for multi-state brain functional connectome with application on preadolescent brain cognition.","authors":"Tianqi Chen, Hongyu Zhao, Chichun Tan, Todd Constable, Sarah Yip, Yize Zhao","doi":"10.1093/biostatistics/kxae045","DOIUrl":"10.1093/biostatistics/kxae045","url":null,"abstract":"Converging evidence indicates that the heterogeneity of cognitive profiles may arise through detectable alternations in brain functional connectivity. Despite an unprecedented opportunity to uncover neurobiological subtypes through clustering or subtyping analyses on multi-state functional connectivity, few existing approaches are applicable to accommodate the network topology and unique biological architecture. To address this issue, we propose an innovative Bayesian nonparametric network-variate clustering analysis to uncover subgroups of individuals with homogeneous brain functional network patterns under multiple cognitive states. In light of the existing neuroscience literature, we assume there are unknown state-specific modular structures within functional connectivity. Concurrently, we identify informative network features essential for defining subtypes. To further facilitate practical use, we develop a computationally efficient variational inference algorithm to approximate posterior inference with satisfactory estimation accuracy. Extensive simulations show the superiority of our method. We apply the method to the Adolescent Brain Cognitive Development (ABCD) study, and identify neurodevelopmental subtypes and brain sub-network phenotypes under each state to signal neurobiological heterogeneity, suggesting promising directions for further exploration and investigation in neuroscience.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830270","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