Biostatistics最新文献_第2页

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxaf004

Yixi Xu, Yi Zhao

引用次数: 0

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 : 2024-12-31 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":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11822954/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scalable randomized kernel methods for multiview data integration and prediction with application to Coronavirus disease.

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxaf001

Sandra E Safo, Han Lu

{"title":"Scalable randomized kernel methods for multiview data integration and prediction with application to Coronavirus disease.","authors":"Sandra E Safo, Han Lu","doi":"10.1093/biostatistics/kxaf001","DOIUrl":"10.1093/biostatistics/kxaf001","url":null,"abstract":"There is still more to learn about the pathobiology of coronavirus disease (COVID-19) despite 4 years of the pandemic. A multiomics approach offers a comprehensive view of the disease and has the potential to yield deeper insight into the pathogenesis of the disease. Previous multiomics integrative analysis and prediction studies for COVID-19 severity and status have assumed simple relationships (ie linear relationships) between omics data and between omics and COVID-19 outcomes. However, these linear methods do not account for the inherent underlying nonlinear structure associated with these different types of data. The motivation behind this work is to model nonlinear relationships in multiomics and COVID-19 outcomes, and to determine key multidimensional molecules associated with the disease. Toward this goal, we develop scalable randomized kernel methods for jointly associating data from multiple sources or views and simultaneously predicting an outcome or classifying a unit into one of 2 or more classes. We also determine variables or groups of variables that best contribute to the relationships among the views. We use the idea that random Fourier bases can approximate shift-invariant kernel functions to construct nonlinear mappings of each view and we use these mappings and the outcome variable to learn view-independent low-dimensional representations. We demonstrate the effectiveness of the proposed methods through extensive simulations. When the proposed methods were applied to gene expression, metabolomics, proteomics, and lipidomics data pertaining to COVID-19, we identified several molecular signatures for COVID-19 status and severity. Our results agree with previous findings and suggest potential avenues for future research. Our algorithms are implemented in Pytorch and interfaced in R and available at: https://github.com/lasandrall/RandMVLearn.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460884","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

Within-trial data borrowing for sequential multiple assignment randomized trials.

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxaf003

Ales Kotalik, David M Vock, Nancy E Sherwood, Brian P Hobbs, Joseph S Koopmeiners

{"title":"Within-trial data borrowing for sequential multiple assignment randomized trials.","authors":"Ales Kotalik, David M Vock, Nancy E Sherwood, Brian P Hobbs, Joseph S Koopmeiners","doi":"10.1093/biostatistics/kxaf003","DOIUrl":"https://doi.org/10.1093/biostatistics/kxaf003","url":null,"abstract":"The Sequential Multiple Assignment Randomized Trial (SMART) is a complex trial design that involves randomizing a single participant multiple times in a sequential manner. This results in the branching nature of a SMART, which represents several distinct groups defined by different combinations of treatments, response statuses, etc. A SMART can then answer various scientific questions of interest, eg, the optimal dynamic treatment regime (DTR) for treating a chronic illness, what intervention to offer first, and what intervention to offer to nonresponders (or suboptimal responders). However, the analysis of a SMART can suffer from low precision, as the potentially widely branching structure can lead to reduced sample sizes in some groups of interest. In this paper, we propose a novel analysis method for a SMART in which dynamic borrowing is used to borrow strength across groups with similar expected outcomes, thus providing increased precision for the estimation of the expected outcomes of DTRs. We apply our method to a SMART evaluating various weight loss strategies using a binary endpoint of clinically significant weight loss and show by simulation that our method can improve the precision of the estimated expected outcome of a DTR, aid in the identification of the optimal DTR, and produce a clustering analysis of DTRs embedded in a SMART.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765923","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 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 : 2024-12-31 DOI: 10.1093/biostatistics/kxae023

Hyung G Park

引用次数: 0

Semiparametric mixture regression for asynchronous longitudinal data using multivariate functional principal component analysis.

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxaf008

Ruihan Lu, Yehua Li, Weixin Yao

{"title":"Semiparametric mixture regression for asynchronous longitudinal data using multivariate functional principal component analysis.","authors":"Ruihan Lu, Yehua Li, Weixin Yao","doi":"10.1093/biostatistics/kxaf008","DOIUrl":"10.1093/biostatistics/kxaf008","url":null,"abstract":"The transitional phase of menopause induces significant hormonal fluctuations, exerting a profound influence on the long-term well-being of women. In an extensive longitudinal investigation of women's health during mid-life and beyond, known as the Study of Women's Health Across the Nation (SWAN), hormonal biomarkers are repeatedly assessed, following an asynchronous schedule compared to other error-prone covariates, such as physical and cardiovascular measurements. We conduct a subgroup analysis of the SWAN data employing a semiparametric mixture regression model, which allows us to explore how the relationship between hormonal responses and other time-varying or time-invariant covariates varies across subgroups. To address the challenges posed by asynchronous scheduling and measurement errors, we model the time-varying covariate trajectories as functional data with reduced-rank Karhunen-Loéve expansions, where splines are employed to capture the mean and eigenfunctions. Treating the latent subgroup membership and the functional principal component (FPC) scores as missing data, we propose an Expectation-Maximization algorithm to effectively fit the joint model, combining the mixture regression for the hormonal response and the FPC model for the asynchronous, time-varying covariates. In addition, we explore data-driven methods to determine the optimal number of subgroups within the population. Through our comprehensive analysis of the SWAN data, we unveil a crucial subgroup structure within the aging female population, shedding light on important distinctions and patterns among women undergoing menopause.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694532","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

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 : 2024-12-31 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":"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":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","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

A joint normal-ordinal (probit) model for ordinal and continuous longitudinal data. 用于序数和连续纵向数据的正态-序数（probit）联合模型。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae014

Margaux Delporte, Geert Molenberghs, Steffen Fieuws, Geert Verbeke

{"title":"A joint normal-ordinal (probit) model for ordinal and continuous longitudinal data.","authors":"Margaux Delporte, Geert Molenberghs, Steffen Fieuws, Geert Verbeke","doi":"10.1093/biostatistics/kxae014","DOIUrl":"10.1093/biostatistics/kxae014","url":null,"abstract":"In biomedical studies, continuous and ordinal longitudinal variables are frequently encountered. In many of these studies it is of interest to estimate the effect of one of these longitudinal variables on the other. Time-dependent covariates have, however, several limitations; they can, for example, not be included when the data is not collected at fixed intervals. The issues can be circumvented by implementing joint models, where two or more longitudinal variables are treated as a response and modeled with a correlated random effect. Next, by conditioning on these response(s), we can study the effect of one or more longitudinal variables on another. We propose a normal-ordinal(probit) joint model. First, we derive closed-form formulas to estimate the model-based correlations between the responses on their original scale. In addition, we derive the marginal model, where the interpretation is no longer conditional on the random effects. As a consequence, we can make predictions for a subvector of one response conditional on the other response and potentially a subvector of the history of the response. Next, we extend the approach to a high-dimensional case with more than two ordinal and/or continuous longitudinal variables. The methodology is applied to a case study where, among others, a longitudinal ordinal response is predicted with a longitudinal continuous variable.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141312354","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

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

Recurrent events modeling based on a reflected Brownian motion with application to hypoglycemia.

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae053

Yingfa Xie, Haoda Fu, Yuan Huang, Vladimir Pozdnyakov, Jun Yan

{"title":"Recurrent events modeling based on a reflected Brownian motion with application to hypoglycemia.","authors":"Yingfa Xie, Haoda Fu, Yuan Huang, Vladimir Pozdnyakov, Jun Yan","doi":"10.1093/biostatistics/kxae053","DOIUrl":"https://doi.org/10.1093/biostatistics/kxae053","url":null,"abstract":"Patients with type 2 diabetes need to closely monitor blood sugar levels as their routine diabetes self-management. Although many treatment agents aim to tightly control blood sugar, hypoglycemia often stands as an adverse event. In practice, patients can observe hypoglycemic events more easily than hyperglycemic events due to the perception of neurogenic symptoms. We propose to model each patient's observed hypoglycemic event as a lower boundary crossing event for a reflected Brownian motion with an upper reflection barrier. The lower boundary is set by clinical standards. To capture patient heterogeneity and within-patient dependence, covariates and a patient level frailty are incorporated into the volatility and the upper reflection barrier. This framework provides quantification for the underlying glucose level variability, patients heterogeneity, and risk factors' impact on glucose. We make inferences based on a Bayesian framework using Markov chain Monte Carlo. Two model comparison criteria, the deviance information criterion and the logarithm of the pseudo-marginal likelihood, are used for model selection. The methodology is validated in simulation studies. In analyzing a dataset from the diabetic patients in the DURABLE trial, our model provides adequate fit, generates data similar to the observed data, and offers insights that could be missed by other models.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048852","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