Biostatistics最新文献_第2页

Functional quantile principal component analysis. 功能量化主成分分析

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae040

Álvaro Méndez-Civieta, Ying Wei, Keith M Diaz, Jeff Goldsmith

{"title":"Functional quantile principal component analysis.","authors":"Álvaro Méndez-Civieta, Ying Wei, Keith M Diaz, Jeff Goldsmith","doi":"10.1093/biostatistics/kxae040","DOIUrl":"10.1093/biostatistics/kxae040","url":null,"abstract":"This paper introduces functional quantile principal component analysis (FQPCA), a dimensionality reduction technique that extends the concept of functional principal components analysis (FPCA) to the examination of participant-specific quantiles curves. Our approach borrows strength across participants to estimate patterns in quantiles, and uses participant-level data to estimate loadings on those patterns. As a result, FQPCA is able to capture shifts in the scale and distribution of data that affect participant-level quantile curves, and is also a robust methodology suitable for dealing with outliers, heteroscedastic data or skewed data. The need for such methodology is exemplified by physical activity data collected using wearable devices. Participants often differ in the timing and intensity of physical activity behaviors, and capturing information beyond the participant-level expected value curves produced by FPCA is necessary for a robust quantification of diurnal patterns of activity. We illustrate our methods using accelerometer data from the National Health and Nutrition Examination Survey, and produce participant-level 10%, 50%, and 90% quantile curves over 24 h of activity. The proposed methodology is supported by simulation results, and is available as an R package.","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/PMC11823270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513407","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

Understanding the opioid syndemic in North Carolina: A novel approach to modeling and identifying factors. 了解北卡罗莱纳州的阿片类药物综合征：一种建模和识别因素的新方法。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae052

Eva Murphy, David Kline, Kathleen L Egan, Kathryn E Lancaster, William C Miller, Lance A Waller, Staci A Hepler

{"title":"Understanding the opioid syndemic in North Carolina: A novel approach to modeling and identifying factors.","authors":"Eva Murphy, David Kline, Kathleen L Egan, Kathryn E Lancaster, William C Miller, Lance A Waller, Staci A Hepler","doi":"10.1093/biostatistics/kxae052","DOIUrl":"10.1093/biostatistics/kxae052","url":null,"abstract":"The opioid epidemic is a significant public health challenge in North Carolina, but limited data restrict our understanding of its complexity. Examining trends and relationships among different outcomes believed to reflect opioid misuse provides an alternative perspective to understand the opioid epidemic. We use a Bayesian dynamic spatial factor model to capture the interrelated dynamics within six different county-level outcomes, such as illicit opioid overdose deaths, emergency department visits related to drug overdose, treatment counts for opioid use disorder, patients receiving prescriptions for buprenorphine, and newly diagnosed cases of acute and chronic hepatitis C virus and human immunodeficiency virus. We design the factor model to yield meaningful interactions among predefined subsets of these outcomes, causing a departure from the conventional lower triangular structure in the loadings matrix and leading to familiar identifiability issues. To address this challenge, we propose a novel approach that involves decomposing the loadings matrix within a Markov chain Monte Carlo algorithm, allowing us to estimate the loadings and factors uniquely. As a result, we gain a better understanding of the spatio-temporal dynamics of the opioid epidemic in North Carolina.","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/PMC11823283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048855","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

Mediation analysis with graph mediator. 使用图中介的中介分析。

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

Distributed lag interaction model with index modification. 具有索引修改的分布式滞后交互模型。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxaf017

Danielle Demateis, Sandra India-Aldana, Robert O Wright, Rosalind J Wright, Andrea Baccarelli, Elena Colicino, Ander Wilson, Kayleigh P Keller

{"title":"Distributed lag interaction model with index modification.","authors":"Danielle Demateis, Sandra India-Aldana, Robert O Wright, Rosalind J Wright, Andrea Baccarelli, Elena Colicino, Ander Wilson, Kayleigh P Keller","doi":"10.1093/biostatistics/kxaf017","DOIUrl":"https://doi.org/10.1093/biostatistics/kxaf017","url":null,"abstract":"Epidemiological evidence supports an association between exposure to air pollution during pregnancy and birth and child health outcomes. Typically, such associations are estimated by regressing an outcome on daily or weekly measures of exposure during pregnancy using a distributed lag model. However, these associations may be modified by multiple factors. We propose a distributed lag interaction model with index modification that allows for effect modification of a functional predictor by a weighted average of multiple modifiers. Our model allows for simultaneous estimation of modifier index weights and the exposure-time-response function via a spline cross-basis in a Bayesian hierarchical framework. Through simulations, we showed that our model out-performs competing methods when there are multiple modifiers of unknown importance. We applied our proposed method to a Colorado birth cohort to estimate the association between birth weight and air pollution modified by a neighborhood-vulnerability index and to a Mexican birth cohort to estimate the association between birthing-parent cardio-metabolic endpoints and air pollution modified by a birthing-parent lifetime stress index.","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":"144369549","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

Addressing the mean-variance relationship in spatially resolved transcriptomics data with spoon. 用spoon处理空间解析转录组学数据中的均方差关系。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxaf012

Kinnary Shah, Boyi Guo, Stephanie C Hicks

引用次数: 0

Scalable randomized kernel methods for multiview data integration and prediction with application to Coronavirus disease. 多视图数据集成与预测的可扩展随机核方法及其在冠状病毒病中的应用。

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

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

Within-trial data borrowing for sequential multiple assignment randomized trials. 序贯多任务随机试验的试验内数据借用。

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":"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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963638/pdf/","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":"OA","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

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