Biostatistics最新文献_第7页

Simultaneous clustering and estimation of networks in multiple graphical models. 在多个图形模型中同时对网络进行聚类和估算。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-06-05 DOI: 10.1093/biostatistics/kxae015

Gen Li, Miaoyan Wang

{"title":"Simultaneous clustering and estimation of networks in multiple graphical models.","authors":"Gen Li, Miaoyan Wang","doi":"10.1093/biostatistics/kxae015","DOIUrl":"10.1093/biostatistics/kxae015","url":null,"abstract":"Gaussian graphical models are widely used to study the dependence structure among variables. When samples are obtained from multiple conditions or populations, joint analysis of multiple graphical models are desired due to their capacity to borrow strength across populations. Nonetheless, existing methods often overlook the varying levels of similarity between populations, leading to unsatisfactory results. Moreover, in many applications, learning the population-level clustering structure itself is of particular interest. In this article, we develop a novel method, called Simultaneous Clustering and Estimation of Networks via Tensor decomposition (SCENT), that simultaneously clusters and estimates graphical models from multiple populations. Precision matrices from different populations are uniquely organized as a three-way tensor array, and a low-rank sparse model is proposed for joint population clustering and network estimation. We develop a penalized likelihood method and an augmented Lagrangian algorithm for model fitting. We also establish the clustering accuracy and norm consistency of the estimated precision matrices. We demonstrate the efficacy of the proposed method with comprehensive simulation studies. The application to the Genotype-Tissue Expression multi-tissue gene expression data provides important insights into tissue clustering and gene coexpression patterns in multiple brain tissues.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141263584","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

Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas 利用高斯协方差对多变量纵向结果和生存结果进行贝叶斯联合建模

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-26 DOI: 10.1093/biostatistics/kxae009

Seoyoon Cho, Matthew A Psioda, Joseph G Ibrahim

{"title":"Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas","authors":"Seoyoon Cho, Matthew A Psioda, Joseph G Ibrahim","doi":"10.1093/biostatistics/kxae009","DOIUrl":"https://doi.org/10.1093/biostatistics/kxae009","url":null,"abstract":"There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method’s value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800296","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

Exponential family measurement error models for single-cell CRISPR screens 单细胞 CRISPR 筛选的指数族测量误差模型

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-23 DOI: 10.1093/biostatistics/kxae010

Timothy Barry, Kathryn Roeder, Eugene Katsevich

{"title":"Exponential family measurement error models for single-cell CRISPR screens","authors":"Timothy Barry, Kathryn Roeder, Eugene Katsevich","doi":"10.1093/biostatistics/kxae010","DOIUrl":"https://doi.org/10.1093/biostatistics/kxae010","url":null,"abstract":"Summary CRISPR genome engineering and single-cell RNA sequencing have accelerated biological discovery. Single-cell CRISPR screens unite these two technologies, linking genetic perturbations in individual cells to changes in gene expression and illuminating regulatory networks underlying diseases. Despite their promise, single-cell CRISPR screens present considerable statistical challenges. We demonstrate through theoretical and real data analyses that a standard method for estimation and inference in single-cell CRISPR screens—“thresholded regression”—exhibits attenuation bias and a bias-variance tradeoff as a function of an intrinsic, challenging-to-select tuning parameter. To overcome these difficulties, we introduce GLM-EIV (“GLM-based errors-in-variables”), a new method for single-cell CRISPR screen analysis. GLM-EIV extends the classical errors-in-variables model to responses and noisy predictors that are exponential family-distributed and potentially impacted by the same set of confounding variables. We develop a computational infrastructure to deploy GLM-EIV across hundreds of processors on clouds (e.g. Microsoft Azure) and high-performance clusters. Leveraging this infrastructure, we apply GLM-EIV to analyze two recent, large-scale, single-cell CRISPR screen datasets, yielding several new insights.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800356","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

A semiparametric Gaussian mixture model for chest CT-based 3D blood vessel reconstruction. 基于胸部 CT 的三维血管重建半参数高斯混合物模型

IF 1.8 3区数学

Biostatistics Pub Date : 2024-04-19 DOI: 10.1093/biostatistics/kxae013

Qianhan Zeng, Jing Zhou, Ying Ji, Hansheng Wang

{"title":"A semiparametric Gaussian mixture model for chest CT-based 3D blood vessel reconstruction.","authors":"Qianhan Zeng, Jing Zhou, Ying Ji, Hansheng Wang","doi":"10.1093/biostatistics/kxae013","DOIUrl":"10.1093/biostatistics/kxae013","url":null,"abstract":"Computed tomography (CT) has been a powerful diagnostic tool since its emergence in the 1970s. Using CT data, 3D structures of human internal organs and tissues, such as blood vessels, can be reconstructed using professional software. This 3D reconstruction is crucial for surgical operations and can serve as a vivid medical teaching example. However, traditional 3D reconstruction heavily relies on manual operations, which are time-consuming, subjective, and require substantial experience. To address this problem, we develop a novel semiparametric Gaussian mixture model tailored for the 3D reconstruction of blood vessels. This model extends the classical Gaussian mixture model by enabling nonparametric variations in the component-wise parameters of interest according to voxel positions. We develop a kernel-based expectation-maximization algorithm for estimating the model parameters, accompanied by a supporting asymptotic theory. Furthermore, we propose a novel regression method for optimal bandwidth selection. Compared to the conventional cross-validation-based (CV) method, the regression method outperforms the CV method in terms of computational and statistical efficiency. In application, this methodology facilitates the fully automated reconstruction of 3D blood vessel structures with remarkable accuracy.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140869271","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

Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies. 利用电子健康记录数据进行基于树状结构的亚组发现：含 DTG 疗法治疗效果的异质性。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxad014

Jiabei Yang, Ann W Mwangi, Rami Kantor, Issa J Dahabreh, Monicah Nyambura, Allison Delong, Joseph W Hogan, Jon A Steingrimsson

{"title":"Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies.","authors":"Jiabei Yang, Ann W Mwangi, Rami Kantor, Issa J Dahabreh, Monicah Nyambura, Allison Delong, Joseph W Hogan, Jon A Steingrimsson","doi":"10.1093/biostatistics/kxad014","DOIUrl":"10.1093/biostatistics/kxad014","url":null,"abstract":"The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the subgroup discovery for longitudinal data algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus who are at higher risk of weight gain when receiving dolutegravir (DTG)-containing antiretroviral therapies (ARTs) versus when receiving non-DTG-containing ARTs.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11017113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10204527","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

Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error. 估算表型变异归因于误差测量的高维预测因子的总体比例。

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxad001

Soutrik Mandal, Do Hyun Kim, Xing Hua, Shilan Li, Jianxin Shi

{"title":"Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error.","authors":"Soutrik Mandal, Do Hyun Kim, Xing Hua, Shilan Li, Jianxin Shi","doi":"10.1093/biostatistics/kxad001","DOIUrl":"10.1093/biostatistics/kxad001","url":null,"abstract":"In prospective genomic studies (e.g., DNA methylation, metagenomics, and transcriptomics), it is crucial to estimate the overall fraction of phenotypic variance (OFPV) attributed to the high-dimensional genomic variables, a concept similar to heritability analyses in genome-wide association studies (GWAS). Unlike genetic variants in GWAS, these genomic variables are typically measured with error due to technical limitation and temporal instability. While the existing methods developed for GWAS can be used, ignoring measurement error may severely underestimate OFPV and mislead the design of future studies. Assuming that measurement error variances are distributed similarly between causal and noncausal variables, we show that the asymptotic attenuation factor equals to the average intraclass correlation coefficients of all genomic variables, which can be estimated based on a pilot study with repeated measurements. We illustrate the method by estimating the contribution of microbiome taxa to body mass index and multiple allergy traits in the American Gut Project. Finally, we show that measurement error does not cause meaningful bias when estimating the correlation of effect sizes for two traits.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11017132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10728987","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

Longitudinal regression of covariance matrix outcomes. 协方差矩阵结果的纵向回归。

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxac045

Yi Zhao, Brian S Caffo, Xi Luo

{"title":"Longitudinal regression of covariance matrix outcomes.","authors":"Yi Zhao, Brian S Caffo, Xi Luo","doi":"10.1093/biostatistics/kxac045","DOIUrl":"10.1093/biostatistics/kxac045","url":null,"abstract":"In this study, a longitudinal regression model for covariance matrix outcomes is introduced. The proposal considers a multilevel generalized linear model for regressing covariance matrices on (time-varying) predictors. This model simultaneously identifies covariate-associated components from covariance matrices, estimates regression coefficients, and captures the within-subject variation in the covariance matrices. Optimal estimators are proposed for both low-dimensional and high-dimensional cases by maximizing the (approximated) hierarchical-likelihood function. These estimators are proved to be asymptotically consistent, where the proposed covariance matrix estimator is the most efficient under the low-dimensional case and achieves the uniformly minimum quadratic loss among all linear combinations of the identity matrix and the sample covariance matrix under the high-dimensional case. Through extensive simulation studies, the proposed approach achieves good performance in identifying the covariate-related components and estimating the model parameters. Applying to a longitudinal resting-state functional magnetic resonance imaging data set from the Alzheimer's Disease (AD) Neuroimaging Initiative, the proposed approach identifies brain networks that demonstrate the difference between males and females at different disease stages. The findings are in line with existing knowledge of AD and the method improves the statistical power over the analysis of cross-sectional data.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40712488","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

A joint Bayesian hierarchical model for estimating SARS-CoV-2 genomic and subgenomic RNA viral dynamics and seroconversion. 用于估计 SARS-CoV-2 基因组和亚基因组 RNA 病毒动态和血清转换的贝叶斯分层联合模型。

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxad016

Tracy Q Dong, Elizabeth R Brown

引用次数: 0

Multi-trait analysis of gene-by-environment interactions in large-scale genetic studies. 大规模遗传研究中基因与环境相互作用的多属性分析。

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxad004

Lan Luo, Devan V Mehrotra, Judong Shen, Zheng-Zheng Tang

{"title":"Multi-trait analysis of gene-by-environment interactions in large-scale genetic studies.","authors":"Lan Luo, Devan V Mehrotra, Judong Shen, Zheng-Zheng Tang","doi":"10.1093/biostatistics/kxad004","DOIUrl":"10.1093/biostatistics/kxad004","url":null,"abstract":"Identifying genotype-by-environment interaction (GEI) is challenging because the GEI analysis generally has low power. Large-scale consortium-based studies are ultimately needed to achieve adequate power for identifying GEI. We introduce Multi-Trait Analysis of Gene-Environment Interactions (MTAGEI), a powerful, robust, and computationally efficient framework to test gene-environment interactions on multiple traits in large data sets, such as the UK Biobank (UKB). To facilitate the meta-analysis of GEI studies in a consortium, MTAGEI efficiently generates summary statistics of genetic associations for multiple traits under different environmental conditions and integrates the summary statistics for GEI analysis. MTAGEI enhances the power of GEI analysis by aggregating GEI signals across multiple traits and variants that would otherwise be difficult to detect individually. MTAGEI achieves robustness by combining complementary tests under a wide spectrum of genetic architectures. We demonstrate the advantages of MTAGEI over existing single-trait-based GEI tests through extensive simulation studies and the analysis of the whole exome sequencing data from the UKB.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9090518","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

Modeling biomarker variability in joint analysis of longitudinal and time-to-event data. 在纵向数据和时间到事件数据的联合分析中建立生物标记变异性模型。

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxad009

Chunyu Wang, Jiaming Shen, Christiana Charalambous, Jianxin Pan

{"title":"Modeling biomarker variability in joint analysis of longitudinal and time-to-event data.","authors":"Chunyu Wang, Jiaming Shen, Christiana Charalambous, Jianxin Pan","doi":"10.1093/biostatistics/kxad009","DOIUrl":"10.1093/biostatistics/kxad009","url":null,"abstract":"The role of visit-to-visit variability of a biomarker in predicting related disease has been recognized in medical science. Existing measures of biological variability are criticized for being entangled with random variability resulted from measurement error or being unreliable due to limited measurements per individual. In this article, we propose a new measure to quantify the biological variability of a biomarker by evaluating the fluctuation of each individual-specific trajectory behind longitudinal measurements. Given a mixed-effects model for longitudinal data with the mean function over time specified by cubic splines, our proposed variability measure can be mathematically expressed as a quadratic form of random effects. A Cox model is assumed for time-to-event data by incorporating the defined variability as well as the current level of the underlying longitudinal trajectory as covariates, which, together with the longitudinal model, constitutes the joint modeling framework in this article. Asymptotic properties of maximum likelihood estimators are established for the present joint model. Estimation is implemented via an Expectation-Maximization (EM) algorithm with fully exponential Laplace approximation used in E-step to reduce the computation burden due to the increase of the random effects dimension. Simulation studies are conducted to reveal the advantage of the proposed method over the two-stage method, as well as a simpler joint modeling approach which does not take into account biomarker variability. Finally, we apply our model to investigate the effect of systolic blood pressure variability on cardiovascular events in the Medical Research Council elderly trial, which is also the motivating example for this article.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11017116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9522826","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