Biostatistics最新文献_第9页

A Bayesian multivariate factor analysis model for causal inference using time-series observational data on mixed outcomes. 用时间序列观测数据对混合结果进行因果推理的贝叶斯多因素分析模型。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad030

Pantelis Samartsidis, Shaun R Seaman, Abbie Harrison, Angelos Alexopoulos, Gareth J Hughes, Christopher Rawlinson, Charlotte Anderson, André Charlett, Isabel Oliver, Daniela De Angelis

{"title":"A Bayesian multivariate factor analysis model for causal inference using time-series observational data on mixed outcomes.","authors":"Pantelis Samartsidis, Shaun R Seaman, Abbie Harrison, Angelos Alexopoulos, Gareth J Hughes, Christopher Rawlinson, Charlotte Anderson, André Charlett, Isabel Oliver, Daniela De Angelis","doi":"10.1093/biostatistics/kxad030","DOIUrl":"10.1093/biostatistics/kxad030","url":null,"abstract":"Assessing the impact of an intervention by using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. Here, we propose a novel Bayesian multivariate factor analysis model for estimating intervention effects in such settings and develop an efficient Markov chain Monte Carlo algorithm to sample from the high-dimensional and nontractable posterior of interest. The proposed method is one of the few that can simultaneously deal with outcomes of mixed type (continuous, binomial, count), increase efficiency in the estimates of the causal effects by jointly modeling multiple outcomes affected by the intervention, and easily provide uncertainty quantification for all causal estimands of interest. Using the proposed approach, we evaluate the impact that Local Tracing Partnerships had on the effectiveness of England's Test and Trace programme for COVID-19.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"867-884"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11247182/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138500308","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

Covariate-guided Bayesian mixture of spline experts for the analysis of multivariate high-density longitudinal data. 用于分析多变量高密度纵向数据的协变量指导贝叶斯混合样条专家。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad034

Haoyi Fu, Lu Tang, Ori Rosen, Alison E Hipwell, Theodore J Huppert, Robert T Krafty

{"title":"Covariate-guided Bayesian mixture of spline experts for the analysis of multivariate high-density longitudinal data.","authors":"Haoyi Fu, Lu Tang, Ori Rosen, Alison E Hipwell, Theodore J Huppert, Robert T Krafty","doi":"10.1093/biostatistics/kxad034","DOIUrl":"10.1093/biostatistics/kxad034","url":null,"abstract":"With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging data play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate high-density longitudinal data and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this article, we propose a group-based method to cluster a collection of multivariate high-density longitudinal data via a Bayesian mixture of smoothing splines. Our method assumes each multivariate high-density longitudinal trajectory is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture components and are incorporated via logistic weights of a mixture-of-experts model. We formulate this approach under a fully Bayesian framework using Gibbs sampling where the number of components is selected based on a deviance information criterion. The proposed method is compared to existing methods via simulation studies and is applied to a study on functional near-infrared spectroscopy, which aims to understand infant emotional reactivity and recovery from stress. The results reveal distinct patterns of brain activity, as well as associations between these patterns and selected covariates.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"666-680"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11247181/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139032905","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 semiparametric Markov renewal mixed models for vocalization syntax. 发声句法的贝叶斯半参数马尔可夫更新混合模型。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxac050

Yutong Wu, Erich D Jarvis, Abhra Sarkar

{"title":"Bayesian semiparametric Markov renewal mixed models for vocalization syntax.","authors":"Yutong Wu, Erich D Jarvis, Abhra Sarkar","doi":"10.1093/biostatistics/kxac050","DOIUrl":"10.1093/biostatistics/kxac050","url":null,"abstract":"Speech and language play an important role in human vocal communication. Studies have shown that vocal disorders can result from genetic factors. In the absence of high-quality data on humans, mouse vocalization experiments in laboratory settings have been proven useful in providing valuable insights into mammalian vocal development, including especially the impact of certain genetic mutations. Such data sets usually consist of categorical syllable sequences along with continuous intersyllable interval (ISI) times for mice of different genotypes vocalizing under different contexts. ISIs are of particular importance as increased ISIs can be an indication of possible vocal impairment. Statistical methods for properly analyzing ISIs along with the transition probabilities have however been lacking. In this article, we propose a class of novel Markov renewal mixed models that capture the stochastic dynamics of both state transitions and ISI lengths. Specifically, we model the transition dynamics and the ISIs using Dirichlet and gamma mixtures, respectively, allowing the mixture probabilities in both cases to vary flexibly with fixed covariate effects as well as random individual-specific effects. We apply our model to analyze the impact of a mutation in the Foxp2 gene on mouse vocal behavior. We find that genotypes and social contexts significantly affect the length of ISIs but, compared to previous analyses, the influences of genotype and social context on the syllable transition dynamics are weaker.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"648-665"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9774490","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":"29 1","pages":""},"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":"85 1","pages":""},"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

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":" ","pages":"486-503"},"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

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":" ","pages":"323-335"},"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

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

Correction to: A transformation perspective on marginal and conditional models. Correction to：边际模型和条件模型的转换视角。

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxad017

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":" ","pages":"504-520"},"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