Biostatistics最新文献_第4页

Regression and alignment for functional data and network topology. 功能数据和网络拓扑的回归和配准。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-08-13 DOI: 10.1093/biostatistics/kxae026

Danni Tu, Julia Wrobel, Theodore D Satterthwaite, Jeff Goldsmith, Ruben C Gur, Raquel E Gur, Jan Gertheiss, Dani S Bassett, Russell T Shinohara

{"title":"Regression and alignment for functional data and network topology.","authors":"Danni Tu, Julia Wrobel, Theodore D Satterthwaite, Jeff Goldsmith, Ruben C Gur, Raquel E Gur, Jan Gertheiss, Dani S Bassett, Russell T Shinohara","doi":"10.1093/biostatistics/kxae026","DOIUrl":"10.1093/biostatistics/kxae026","url":null,"abstract":"In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141977263","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

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 : 2024-08-08 DOI: 10.1093/biostatistics/kxae018

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

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 : 2024-08-05 DOI: 10.1093/biostatistics/kxae030

Serge Aleshin-Guendel, Jon Wakefield

引用次数: 0

Direct estimation and inference of higher-level correlations from lower-level measurements with applications in gene-pathway and proteomics studies. 从较低层次的测量结果直接估计和推断较高层次的相关性，并将其应用于基因通路和蛋白质组学研究。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-31 DOI: 10.1093/biostatistics/kxae027

Yue Wang, Haoran Shi

{"title":"Direct estimation and inference of higher-level correlations from lower-level measurements with applications in gene-pathway and proteomics studies.","authors":"Yue Wang, Haoran Shi","doi":"10.1093/biostatistics/kxae027","DOIUrl":"https://doi.org/10.1093/biostatistics/kxae027","url":null,"abstract":"This paper tackles the challenge of estimating correlations between higher-level biological variables (e.g. proteins and gene pathways) when only lower-level measurements are directly observed (e.g. peptides and individual genes). Existing methods typically aggregate lower-level data into higher-level variables and then estimate correlations based on the aggregated data. However, different data aggregation methods can yield varying correlation estimates as they target different higher-level quantities. Our solution is a latent factor model that directly estimates these higher-level correlations from lower-level data without the need for data aggregation. We further introduce a shrinkage estimator to ensure the positive definiteness and improve the accuracy of the estimated correlation matrix. Furthermore, we establish the asymptotic normality of our estimator, enabling efficient computation of P-values for the identification of significant correlations. The effectiveness of our approach is demonstrated through comprehensive simulations and the analysis of proteomics and gene expression datasets. We develop the R package highcor for implementing our method.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861746","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 causal effects for binary outcomes using per-decision inverse probability weighting. 使用每次决定的反概率加权法估算二元结果的因果效应。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-30 DOI: 10.1093/biostatistics/kxae025

Yihan Bao, Lauren Bell, Elizabeth Williamson, Claire Garnett, Tianchen Qian

{"title":"Estimating causal effects for binary outcomes using per-decision inverse probability weighting.","authors":"Yihan Bao, Lauren Bell, Elizabeth Williamson, Claire Garnett, Tianchen Qian","doi":"10.1093/biostatistics/kxae025","DOIUrl":"https://doi.org/10.1093/biostatistics/kxae025","url":null,"abstract":"Micro-randomized trials are commonly conducted for optimizing mobile health interventions such as push notifications for behavior change. In analyzing such trials, causal excursion effects are often of primary interest, and their estimation typically involves inverse probability weighting (IPW). However, in a micro-randomized trial, additional treatments can often occur during the time window over which an outcome is defined, and this can greatly inflate the variance of the causal effect estimator because IPW would involve a product of numerous weights. To reduce variance and improve estimation efficiency, we propose two new estimators using a modified version of IPW, which we call \"per-decision IPW.\" The second estimator further improves efficiency using the projection idea from the semiparametric efficiency theory. These estimators are applicable when the outcome is binary and can be expressed as the maximum of a series of sub-outcomes defined over sub-intervals of time. We establish the estimators' consistency and asymptotic normality. Through simulation studies and real data applications, we demonstrate substantial efficiency improvement of the proposed estimator over existing estimators. The new estimators can be used to improve the precision of primary and secondary analyses for micro-randomized trials with binary outcomes.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141794123","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

Incorporating prior information in gene expression network-based cancer heterogeneity analysis. 在基于基因表达网络的癌症异质性分析中纳入先验信息。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-29 DOI: 10.1093/biostatistics/kxae028

Rong Li, Shaodong Xu, Yang Li, Zuojian Tang, Di Feng, James Cai, Shuangge Ma

{"title":"Incorporating prior information in gene expression network-based cancer heterogeneity analysis.","authors":"Rong Li, Shaodong Xu, Yang Li, Zuojian Tang, Di Feng, James Cai, Shuangge Ma","doi":"10.1093/biostatistics/kxae028","DOIUrl":"https://doi.org/10.1093/biostatistics/kxae028","url":null,"abstract":"Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informative for cancer heterogeneity analysis than some simpler measures. Gene interconnections can be classified as \"direct\" and \"indirect,\" where the latter can be caused by shared genomic regulators (such as transcription factors, microRNAs, and other regulatory molecules) and other mechanisms. It has been suggested that incorporating the regulators of gene expressions in network analysis and focusing on the direct interconnections can lead to a deeper understanding of the more essential gene interconnections. Such analysis can be seriously challenged by the large number of parameters (jointly caused by network analysis, incorporation of regulators, and heterogeneity) and often weak signals. To effectively tackle this problem, we propose incorporating prior information contained in the published literature. A key challenge is that such prior information can be partial or even wrong. We develop a two-step procedure that can flexibly accommodate different levels of prior information quality. Simulation demonstrates the effectiveness of the proposed approach and its superiority over relevant competitors. In the analysis of a breast cancer dataset, findings different from the alternatives are made, and the identified sample subgroups have important clinical differences.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141794124","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

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 : 2024-07-13 DOI: 10.1093/biostatistics/kxae020

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

引用次数: 0

A marginal structural model for normal tissue complication probability. 正常组织并发症概率的边际结构模型。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-09 DOI: 10.1093/biostatistics/kxae019

Thai-Son Tang, Zhihui Liu, Ali Hosni, John Kim, Olli Saarela

引用次数: 0

Bayesian estimation of covariate assisted principal regression for brain functional connectivity. 针对大脑功能连接性的协变量辅助主回归贝叶斯估计。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-09 DOI: 10.1093/biostatistics/kxae023

Hyung G Park

引用次数: 0

Multivariate spatiotemporal functional principal component analysis for modeling hospitalization and mortality rates in the dialysis population. 透析人群住院率和死亡率建模的多变量时空功能主成分分析

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad013

Qi Qian, Danh V Nguyen, Donatello Telesca, Esra Kurum, Connie M Rhee, Sudipto Banerjee, Yihao Li, Damla Senturk

{"title":"Multivariate spatiotemporal functional principal component analysis for modeling hospitalization and mortality rates in the dialysis population.","authors":"Qi Qian, Danh V Nguyen, Donatello Telesca, Esra Kurum, Connie M Rhee, Sudipto Banerjee, Yihao Li, Damla Senturk","doi":"10.1093/biostatistics/kxad013","DOIUrl":"10.1093/biostatistics/kxad013","url":null,"abstract":"Dialysis patients experience frequent hospitalizations and a higher mortality rate compared to other Medicare populations, in whom hospitalizations are a major contributor to morbidity, mortality, and healthcare costs. Patients also typically remain on dialysis for the duration of their lives or until kidney transplantation. Hence, there is growing interest in studying the spatiotemporal trends in the correlated outcomes of hospitalization and mortality among dialysis patients as a function of time starting from transition to dialysis across the United States Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multivariate spatiotemporal functional principal component analysis model to study the joint spatiotemporal patterns of hospitalization and mortality rates among dialysis patients. The proposal is based on a multivariate Karhunen-Loéve expansion that describes leading directions of variation across time and induces spatial correlations among region-specific scores. An efficient estimation procedure is proposed using only univariate principal components decompositions and a Markov Chain Monte Carlo framework for targeting the spatial correlations. The finite sample performance of the proposed method is studied through simulations. Novel applications to the USRDS data highlight hot spots across the United States with higher hospitalization and/or mortality rates and time periods of elevated risk.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11358256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10019524","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