Biostatistics最新文献_第8页

Bayesian scalar-on-tensor regression using the Tucker decomposition for sparse spatial modeling. 利用Tucker分解的贝叶斯张量标量回归进行稀疏空间建模。

IF 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 DOI: 10.1093/biostatistics/kxaf029

Daniel A Spencer, Rene Gutierrez, Rajarshi Guhaniyogi, Russell T Shinohara, Raquel Prado

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 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 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":2.1,"publicationDate":"2025-12-31","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. 序贯多任务随机试验的试验内数据借用。

IF 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 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":2.1,"publicationDate":"2025-12-31","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

Mediation with External Summary Statistic Information. 带有外部汇总统计信息的中介。

IF 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 DOI: 10.1093/biostatistics/kxaf020

Jonathan Boss, Wei Hao, Amber Cathey, Barrett M Welch, Kelly K Ferguson, John D Meeker, Xiang Zhou, Jian Kang, Bhramar Mukherjee

{"title":"Mediation with External Summary Statistic Information.","authors":"Jonathan Boss, Wei Hao, Amber Cathey, Barrett M Welch, Kelly K Ferguson, John D Meeker, Xiang Zhou, Jian Kang, Bhramar Mukherjee","doi":"10.1093/biostatistics/kxaf020","DOIUrl":"10.1093/biostatistics/kxaf020","url":null,"abstract":"Environmental health studies are increasingly measuring endogenous omics data ($ boldsymbol{M} $) to study intermediary biological pathways by which an exogenous exposure ($ boldsymbol{A} $) affects a health outcome ($ boldsymbol{Y} $), given confounders ($ boldsymbol{C} $). Mediation analysis is frequently performed to understand such mechanisms. If intermediary pathways are of interest, then there is likely literature establishing statistical and biological significance of the total effect, defined as the effect of $ boldsymbol{A} $ on $ boldsymbol{Y} $ given $ boldsymbol{C} $. For mediation models with continuous outcomes and mediators, we show that leveraging external summary-level information on the total effect can improve estimation efficiency of the direct and indirect effects. Moreover, the efficiency gain depends on the asymptotic partial $ R^{2} $ between the outcome ($ boldsymbol{Y}midboldsymbol{M},boldsymbol{A},boldsymbol{C} $) and total effect ($ boldsymbol{Y}midboldsymbol{A},boldsymbol{C} $) models, with smaller (larger) values benefiting direct (indirect) effect estimation. We propose a robust data-adaptive estimation procedure, Mediation with External Summary Statistic Information, to improve estimation efficiency in settings with congenial external information, while simultaneously protecting against bias in settings with incongenial external information. In congenial simulation scenarios, we observe relative efficiency gains for mediation effect estimation of up to 40%. We illustrate our methodology using data from the Puerto Rico Testsite for Exploring Contamination Threats, where Cytochrome p450 metabolites are hypothesized to mediate the effect of phthalate exposure on gestational age at delivery. External summary information on the total effect comes from a recently published pooled analysis of 16 studies. The proposed framework blends mediation analysis with emerging data integration techniques.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12302958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735537","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

Identification and estimation of mediational effects of longitudinal modified treatment policies. 纵向修正治疗政策的中介效应的识别和估计。

IF 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 DOI: 10.1093/biostatistics/kxaf031

Brian Gilbert, Katherine Hoffman, Nicholas Williams, Kara Rudolph, Edward J Schenck, Iván Díaz

引用次数: 0

Semiparametric efficient estimation of small genetic effects in large-scale population cohorts. 大规模群体群体中小遗传效应的半参数有效估计。

IF 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 DOI: 10.1093/biostatistics/kxaf030

Olivier Labayle, Breeshey Roskams-Hieter, Joshua Slaughter, Kelsey Tetley-Campbell, Mark J van der Laan, Chris P Ponting, Sjoerd V Beentjes, Ava Khamseh

{"title":"Semiparametric efficient estimation of small genetic effects in large-scale population cohorts.","authors":"Olivier Labayle, Breeshey Roskams-Hieter, Joshua Slaughter, Kelsey Tetley-Campbell, Mark J van der Laan, Chris P Ponting, Sjoerd V Beentjes, Ava Khamseh","doi":"10.1093/biostatistics/kxaf030","DOIUrl":"10.1093/biostatistics/kxaf030","url":null,"abstract":"Population genetics seeks to quantify DNA variant associations with traits or diseases, as well as interactions among variants and with environmental factors. Computing millions of estimates in large cohorts in which small effect sizes and tight confidence intervals are expected, necessitates minimizing model-misspecification bias to increase power and control false discoveries. We present TarGene, a unified statistical workflow for the semi-parametric efficient and double robust estimation of genetic effects including $ k $-point interactions among categorical variables in the presence of confounding and weak population dependence. $ k $-point interactions, or Average Interaction Effects (AIEs), are a direct generalization of the usual average treatment effect (ATE). We estimate genetic effects with cross-validated and/or weighted versions of Targeted Minimum Loss-based Estimators (TMLE) and One-Step Estimators (OSE). The effect of dependence among data units on variance estimates is corrected by using sieve plateau variance estimators based on genetic relatedness across the units. We present extensive realistic simulations to demonstrate power, coverage, and control of type I error. Our motivating application is the targeted estimation of genetic effects on trait, including two-point and higher-order gene-gene and gene-environment interactions, in large-scale genomic databases such as UK Biobank and All of Us. All cross-validated and/or weighted TMLE and OSE for the AIE $ k $-point interaction, as well as ATEs, conditional ATEs and functions thereof, are implemented in the general purpose Julia package TMLE.jl. For high-throughput applications in population genomics, we provide the open-source Nextflow pipeline and software TarGene which integrates seamlessly with modern high-performance and cloud computing platforms.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193867","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 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 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":2.1,"publicationDate":"2025-12-31","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

While-alive regression analysis of composite survival endpoints. 复合生存终点的活时回归分析。

IF 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 DOI: 10.1093/biostatistics/kxaf047

Xi Fang, Hajime Uno, Fan Li

{"title":"While-alive regression analysis of composite survival endpoints.","authors":"Xi Fang, Hajime Uno, Fan Li","doi":"10.1093/biostatistics/kxaf047","DOIUrl":"10.1093/biostatistics/kxaf047","url":null,"abstract":"Composite endpoints are frequently used in clinical trials to enhance the event rate and improve the statistical power. In the presence of a terminal event, the while-alive cumulative frequency measure offers a useful alternative to define composite survival outcomes, by relating the average event rate to the survival time. Although non-parametric methods have been proposed for two-sample comparisons, limited attention has been given to regression methods that directly address time-varying association effects in while-alive measures. We address this gap by developing a regression framework for exposure-weighted while-alive measures for composite survival outcomes that include a terminal component event. Our regression approach uses splines to model time-varying association between covariates and a generalized while-alive loss rate of all component events, and can be applied to both independent and clustered data. We derive the asymptotic properties of the regression estimator under both independent data and cluster-correlated data settings, and study the operating characteristics of our methods through simulations. Finally, we apply our regression method to analyze data two randomized clinical trials. The proposed methods are implemented in the WAreg R package.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12711255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145828750","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 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 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":2.1,"publicationDate":"2025-12-31","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

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 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 DOI: 10.1093/biostatistics/kxae023

Hyung G Park

引用次数: 0