Biostatistics最新文献_第7页

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

IF 2.1 3区数学

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

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

IF 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 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 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 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":"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":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12205949/pdf/","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":"OA","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 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 DOI: 10.1093/biostatistics/kxaf012

Kinnary Shah, Boyi Guo, Stephanie C Hicks

引用次数: 0

Model-based dimensionality reduction for single-cell RNA-seq using generalized bilinear models. 利用广义双线性模型对单细胞RNA-seq进行基于模型的降维。

IF 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 DOI: 10.1093/biostatistics/kxaf024

Phillip B Nicol, Jeffrey W Miller

{"title":"Model-based dimensionality reduction for single-cell RNA-seq using generalized bilinear models.","authors":"Phillip B Nicol, Jeffrey W Miller","doi":"10.1093/biostatistics/kxaf024","DOIUrl":"10.1093/biostatistics/kxaf024","url":null,"abstract":"Dimensionality reduction is a critical step in the analysis of single-cell RNA-seq (scRNA-seq) data. The standard approach is to apply a transformation to the count matrix followed by principal components analysis (PCA). However, this approach can induce spurious heterogeneity and mask true biological variability. An alternative approach is to directly model the counts, but existing methods tend to be computationally intractable on large datasets and do not quantify uncertainty in the low-dimensional representation. To address these problems, we develop scGBM, a novel method for model-based dimensionality reduction of scRNA-seq data using a Poisson bilinear model. We introduce a fast estimation algorithm to fit the model using iteratively reweighted singular value decompositions, enabling the method to scale to datasets with millions of cells. Furthermore, scGBM quantifies the uncertainty in each cell's latent position and leverages these uncertainties to assess the confidence associated with a given cell clustering. On real and simulated single-cell data, we find that scGBM produces low-dimensional embeddings that better capture relevant biological information while removing unwanted variation.","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/PMC12342792/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838618","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

Variable-based probabilistic calibration with binary outcome. 二元结果的基于变量的概率校准。

IF 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 DOI: 10.1093/biostatistics/kxaf026

Hiroe Seto, Shuji Kitora, Asuka Oyama, Hiroshi Toki, Ryohei Yamamoto, Michio Yamamoto

{"title":"Variable-based probabilistic calibration with binary outcome.","authors":"Hiroe Seto, Shuji Kitora, Asuka Oyama, Hiroshi Toki, Ryohei Yamamoto, Michio Yamamoto","doi":"10.1093/biostatistics/kxaf026","DOIUrl":"10.1093/biostatistics/kxaf026","url":null,"abstract":"In developing risk prediction models for specific diseases, it is essential to evaluate the calibration performance of the prediction model. Various methods have been proposed to assess the calibration of prediction models, but it has been pointed out that conventional methods based on the predicted probability of the model are insufficient to detect miscalibration. Another problem is that a method for evaluating calibration for continuous variables of interest has not yet been established. We therefore propose two methods to evaluate the calibration of the variable of interest: the variable-based probabilistic calibration plot (VPC-Plot), which is a visual assessment, and the variable-based probabilistic calibration error (VPCE), which is a corresponding evaluation metric. We conducted theoretical and simulation studies to investigate the properties and effectiveness of the proposed method. Theoretical and simulation studies demonstrated that the proposed methods can detect miscalibration by evaluating the calibration based on the variable of interest, even when conventional methods fail to detect miscalibration. To show the usefulness in the real-world data analysis, we evaluated diabetes prediction models developed using the national health insurance database for Osaka, Japan. We show that the proposed method can identify miscalibration of key covariate in a diabetes prediction model.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314132","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

Wastewater-based reproduction rates for epidemic curve reconstruction. 流行病曲线重建中基于废水的繁殖率。

IF 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 DOI: 10.1093/biostatistics/kxaf033

Emily Somerset, Justin J Slater, Patrick E Brown

引用次数: 0

Bayesian model averaging for partial ordering continual reassessment methods. 偏序连续重评价的贝叶斯模型平均方法。

IF 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 DOI: 10.1093/biostatistics/kxaf035

Luka Kovačević, Weishi Chen, Helen Barnett, Thomas Jaki, Pavel Mozgunov

{"title":"Bayesian model averaging for partial ordering continual reassessment methods.","authors":"Luka Kovačević, Weishi Chen, Helen Barnett, Thomas Jaki, Pavel Mozgunov","doi":"10.1093/biostatistics/kxaf035","DOIUrl":"10.1093/biostatistics/kxaf035","url":null,"abstract":"Phase I clinical trials are essential to bringing novel therapies from chemical development to widespread use. Traditional approaches to dose-finding in Phase I trials, such as the '3 + 3' method and the continual reassessment method (CRM), provide a principled approach for escalating across dose levels. However, these methods lack the ability to incorporate uncertainty regarding the dose-toxicity ordering as found in combination drug trials. Under this setting, dose levels vary across multiple drugs simultaneously, leading to multiple possible dose-toxicity orderings. The CRM for partial ordering (POCRM) extends to these settings by allowing for multiple dose-toxicity orderings. In this work, it is shown that the POCRM is vulnerable to 'estimation incoherency' whereby toxicity estimates shift in an illogical way, threatening patient safety and undermining clinician trust in dose-finding models. To this end, the Bayesian model averaged POCRM (BMA-POCRM) is formalized. BMA-POCRM uses Bayesian model averaging to take into account all possible orderings simultaneously, reducing the frequency of estimation incoherencies. We derive novel theoretical guarantees on the estimation coherency of the POCRM and BMA-POCRM. The effectiveness of BMA-POCRM in drug combination settings is demonstrated through a specific instance of estimate incoherency of POCRM and simulation studies. The results highlight the improved safety, accuracy, and reduced occurrence of estimate incoherency in trials applying the BMA-POCRM relative to the POCRM model.","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/PMC12538209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145338280","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

Probabilistic clustering using shared latent variable model for assessing Alzheimer's disease biomarkers. 使用共享潜在变量模型评估阿尔茨海默病生物标志物的概率聚类。

IF 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 DOI: 10.1093/biostatistics/kxaf010

Yizhen Xu, Scott Zeger, Zheyu Wang

{"title":"Probabilistic clustering using shared latent variable model for assessing Alzheimer's disease biomarkers.","authors":"Yizhen Xu, Scott Zeger, Zheyu Wang","doi":"10.1093/biostatistics/kxaf010","DOIUrl":"10.1093/biostatistics/kxaf010","url":null,"abstract":"The preclinical stage of many neurodegenerative diseases can span decades before symptoms become apparent. Understanding the sequence of preclinical biomarker changes provides a critical opportunity for early diagnosis and effective intervention prior to significant loss of patients' brain functions. The main challenge to early detection lies in the absence of direct observation of the disease state and the considerable variability in both biomarkers and disease dynamics among individuals. Recent research hypothesized the existence of subgroups with distinct biomarker patterns due to co-morbidities and degrees of brain resilience. Our ability to diagnose early and intervene during the preclinical stage of neurodegenerative diseases will be enhanced by further insights into heterogeneity in the biomarker-disease relationship. In this article, we focus on Alzheimer's disease (AD) and attempt to identify the systematic patterns within the heterogeneous AD biomarker-disease cascade. Specifically, we quantify the disease progression using a dynamic latent variable whose mixture distribution represents patient subgroups. Model estimation uses Hamiltonian Monte Carlo with the number of clusters determined by the Bayesian Information Criterion. We report simulation studies that investigate the performance of the proposed model in finite sample settings that are similar to our motivating application. We apply the proposed model to the Biomarkers of Cognitive Decline Among Normal Individuals data, a longitudinal study that was conducted over 2 decades among individuals who were initially cognitively normal. Our application yields evidence consistent with the hypothetical model of biomarker dynamics presented in Jack Jr et al. In addition, our analysis identified 2 subgroups with distinct disease-onset patterns. Finally, we develop a dynamic prediction approach to improve the precision of prognoses.","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/PMC12054513/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029768","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

Network generalized estimating equations for complexly correlated data with applications to cluster randomized trials. 复杂相关数据的网络广义估计方程及其在聚类随机试验中的应用。

IF 2.1 3区数学

Biostatistics Pub Date : 2025-12-31 DOI: 10.1093/biostatistics/kxaf039

Tom Chen, Fan Li, Rui Wang

{"title":"Network generalized estimating equations for complexly correlated data with applications to cluster randomized trials.","authors":"Tom Chen, Fan Li, Rui Wang","doi":"10.1093/biostatistics/kxaf039","DOIUrl":"10.1093/biostatistics/kxaf039","url":null,"abstract":"Estimating parameters corresponding to mean outcomes and their intricate association structures in cluster randomized trials (CRTs) can pose significant methodological challenges. This paper introduces a novel framework that leverages network concepts to represent complex dependency structures and estimate these parameters using generalized estimating equations (GEE). We focus on modeling complex correlation structures by partitioning observations into potentially overlapping groups of interrelated data, where observations are assumed locally exchangeable within each group. This network GEE framework is inherently flexible, and we demonstrate its application to multiple exchangeable structures (simple, nested, block), moving average structures, and exponential decay structures. Furthermore, to address computational challenges arising in GEEs with large cluster sizes, we present the networkGEE R package, enabling the fitting of models beyond the capabilities of existing statistical software. The proposed methods are evaluated through extensive simulation studies. To illustrate their practical application, we analyze data from the Washington State Expedited Partners Therapy trial, a stepped-wedge CRT designed to assess the impact of a public health intervention aimed at reducing sexually transmitted infections through free patient-delivered partner therapy.","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/PMC12766921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145764536","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