Biometrics最新文献_第3页

Feature screening for metric space-valued responses based on Fréchet regression with its applications.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf007

Bing Tian, Jian Kang, Wei Zhong

{"title":"Feature screening for metric space-valued responses based on Fréchet regression with its applications.","authors":"Bing Tian, Jian Kang, Wei Zhong","doi":"10.1093/biomtc/ujaf007","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf007","url":null,"abstract":"In various applications, we need to handle more general types of responses, such as distributional data and matrix-valued data, rather than a scalar variable. When the dimension of predictors is ultrahigh, it is necessarily important to identify the relevant predictors for such complex types of responses. For example, in our Alzheimer's disease neuroimaging study, we need to select the relevant single nucleotide polymorphisms out of 582 591 candidates for the distribution of voxel-level intensities in each of 42 brain regions. To this end, we propose a new sure independence screening (SIS) procedure for general metric space-valued responses based on global Fréchet regression, termed as Fréchet-SIS. The marginal general residual sum of squares is utilized to serve as a marginal utility for evaluating the importance of predictors, where only a distance between data objects is needed. We theoretically show that the proposed Fréchet-SIS procedure enjoys the sure screening property under mild regularity conditions. Monte Carlo simulations are conducted to demonstrate its excellent finite-sample performance. In Alzheimer's disease neuroimaging study, we identify important genes that correlate with brain activity across different stages of the disease and brain regions. In addition, we also include an economic case study to illustrate our proposal.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143397821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Instrumental variable estimation of complier casual treatment effects with interval-censored competing risks data.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf010

Yichen Lou, Yuqing Ma, Jianguo Sun, Peijie Wang, Zhisheng Ye

{"title":"Instrumental variable estimation of complier casual treatment effects with interval-censored competing risks data.","authors":"Yichen Lou, Yuqing Ma, Jianguo Sun, Peijie Wang, Zhisheng Ye","doi":"10.1093/biomtc/ujaf010","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf010","url":null,"abstract":"This paper discusses the assessment of causal treatment effects on a time-to-event outcome, a crucial part of many scientific investigations. Although some methods have been developed for the problem, they are not applicable to situations where there exist both interval censoring and competing risks. We fill in this critical gap under a class of transformation models for cumulative incidence functions by developing an instrumented variable (IV) estimation approach. The IV is a valuable tool commonly used to mitigate the impact of endogenous treatment selection and to determine causal treatment effects in an unbiased manner. The proposed method is flexible as the model includes many commonly used models such as the sub-distributional proportional odds and hazards models (ie, the Fine-Gray model) as special cases. The resulting estimator for the regression parameter is shown to be consistent and asymptotically normal. A simulation study is conducted to evaluate finite sample performance of the proposed approach and suggests that it works well in practice. It is applied to a breast cancer screening study.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143432303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Improving estimation efficiency for survival data analysis by integrating a coarsened time-to-event outcome from an external study. 通过整合来自外部研究的粗化时间到事件结果，提高生存数据分析的估计效率。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae168

Daxuan Deng, Lijun Zhang, Hao Feng, Vernon M Chinchilli, Chixiang Chen, Ming Wang

{"title":"Improving estimation efficiency for survival data analysis by integrating a coarsened time-to-event outcome from an external study.","authors":"Daxuan Deng, Lijun Zhang, Hao Feng, Vernon M Chinchilli, Chixiang Chen, Ming Wang","doi":"10.1093/biomtc/ujae168","DOIUrl":"10.1093/biomtc/ujae168","url":null,"abstract":"In the era of big data, increasing availability of data makes combining different data sources to obtain more accurate estimations a popular topic. However, the development of data integration is often hindered by the heterogeneity in data forms across studies. In this paper, we focus on a case in survival analysis where we have primary study data with a continuous time-to-event outcome and complete covariate measurements, while the data from an external study contain an outcome observed at regular intervals, and only a subset of covariates is measured. To incorporate external information while accounting for the different data forms, we posit working models and obtain informative weights by empirical likelihood, which will be used to construct a weighted estimator in the main analysis. We have established the theory demonstrating that the new estimator has higher estimation efficiency compared to the conventional ones, and this advantage is robust to working model misspecification, as confirmed in our simulation studies. To assess its utility, we apply our method to accommodate data from the National Alzheimer's Coordinating Center to improve the analysis of the Alzheimer's Disease Neuroimaging Initiative Phase 1 study.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11747882/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142999230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain functional connectome outcomes via network-based matrix-on-vector regression.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf027

Tong Lu, Yuan Zhang, Vince Lyzinski, Chuan Bi, Peter Kochunov, Elliot Hong, Shuo Chen

{"title":"Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain functional connectome outcomes via network-based matrix-on-vector regression.","authors":"Tong Lu, Yuan Zhang, Vince Lyzinski, Chuan Bi, Peter Kochunov, Elliot Hong, Shuo Chen","doi":"10.1093/biomtc/ujaf027","DOIUrl":"10.1093/biomtc/ujaf027","url":null,"abstract":"The joint analysis of multimodal neuroimaging data is vital in brain research, revealing complex interactions between brain structures and functions. Our study is motivated by the analysis of a vast dataset of brain functional connectivity (FC) and multimodal structural imaging (SI) features from the UK Biobank. Specifically, we aim to investigate the effects of SI features, such as white matter microstructure integrity (WMMI) and cortical thickness, on the whole-brain functional connectome network. This analysis is inherently challenging due to the extensive structural-functional associations and the intricate network patterns present in multimodal high-dimensional neuroimaging data. To bridge methodological gaps, we developed a novel multi-level sub-graph extraction method (dense bipartite with nested unipartite graph) within a matrix(network)-on-vector regression model. This method identifies subsets of spatially specific SI features that intensely and systematically influence FC sub-networks, while effectively suppressing false positives in large-scale datasets. Applying our method to a multimodal neuroimaging dataset of 4242 participants ffrom the UK Biobank, we evaluated the effects of whole-brain WMMI and cortical thickness on resting-state FC. Our findings indicate that the WMMI in corticospinal tracts and inferior cerebellar peduncle significantly affect functional connections of sensorimotor, salience, and executive sub-networks, with an average correlation of 0.81 ($p < 0.001$).","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 mixed-effects Bayesian regression model for multivariate group testing data.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf028

Christopher S McMahan, Chase N Joyner, Joshua M Tebbs, Christopher R Bilder

{"title":"A mixed-effects Bayesian regression model for multivariate group testing data.","authors":"Christopher S McMahan, Chase N Joyner, Joshua M Tebbs, Christopher R Bilder","doi":"10.1093/biomtc/ujaf028","DOIUrl":"10.1093/biomtc/ujaf028","url":null,"abstract":"Laboratories use group (pooled) testing with multiplex assays to reduce the time and cost associated with screening large populations for infectious diseases. Multiplex assays test for multiple diseases simultaneously, and combining their use with group testing can lead to highly efficient screening protocols. However, these benefits come at the expense of a more complex data structure which can hinder surveillance efforts. To overcome this challenge, we develop a general Bayesian framework to estimate a mixed multivariate probit model with data arising from any group testing protocol that uses multiplex assays. In the formulation of this model, we account for the correlation between true disease statuses and heterogeneity across population subgroups, and we provide for automated variable selection through the adoption of spike and slab priors. To perform model fitting, we develop an attractive posterior sampling algorithm which is straightforward to implement. We illustrate our methodology through numerical studies and analyze chlamydia and gonorrhea group testing data collected by the State Hygienic Laboratory at the University of Iowa.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Distributed model building and recursive integration for big spatial data modeling. 大空间数据建模的分布式模型构建与递归集成。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae159

Emily C Hector, Brian J Reich, Ani Eloyan

引用次数: 0

Potential outcome simulation for efficient head-to-head comparison of adaptive dose-finding designs.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf012

Michael Sweeting, Daniel Slade, Dan Jackson, Kristian Brock

{"title":"Potential outcome simulation for efficient head-to-head comparison of adaptive dose-finding designs.","authors":"Michael Sweeting, Daniel Slade, Dan Jackson, Kristian Brock","doi":"10.1093/biomtc/ujaf012","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf012","url":null,"abstract":"Dose-finding trials are a key component of the drug development process and rely on a statistical design to help inform dosing decisions. Triallists wishing to choose a design require knowledge of operating characteristics of competing methods. This is often assessed using a large-scale simulation study with multiple designs and configurations investigated, which can be time-consuming and therefore limits the scope of the simulation. We introduce a new approach to the design of simulation studies of dose-finding trials. The approach simulates all potential outcomes that individuals could experience at each dose level in the trial. Datasets are simulated in advance and then applied to each of the competing methods to enable a more efficient head-to-head comparison. Furthermore, individual trial datasets can be interrogated to understand when designs deviate in their decision making. In three case-studies, we show sizeable reductions in Monte Carlo error for comparing a performance metric between two competing designs. Efficiency gains depend on the similarity of the designs. Comparing two Phase I/II design variants, with high correlation of recommending the same optimal biologic dose, we show that the new approach requires a simulation study that is approximately 48 times smaller than the conventional approach. Furthermore, advance-simulated trial datasets can be reused to assess the performance of designs across multiple configurations. We recommend researchers consider this more efficient simulation approach in their dose-finding studies and we have updated the R package escalation to help facilitate implementation.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 simple and powerful method for large-scale composite null hypothesis testing with applications in mediation analysis.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf011

Yaowu Liu

引用次数: 0

The subtype-free average causal effect for heterogeneous disease etiology.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf016

A Sasson, M Wang, S Ogino, D Nevo

{"title":"The subtype-free average causal effect for heterogeneous disease etiology.","authors":"A Sasson, M Wang, S Ogino, D Nevo","doi":"10.1093/biomtc/ujaf016","DOIUrl":"10.1093/biomtc/ujaf016","url":null,"abstract":"Studies have shown that the effect an exposure may have on a disease can vary for different subtypes of the same disease. However, existing approaches to estimate and compare these effects largely overlook causality. In this paper, we study the effect smoking may have on having colorectal cancer subtypes defined by a trait known as microsatellite instability (MSI). We use principal stratification to propose an alternative causal estimand, the Subtype-Free Average Causal Effect (SF-ACE). The SF-ACE is the causal effect of the exposure among those who would be free from other disease subtypes under any exposure level. We study non-parametric identification of the SF-ACE and discuss different monotonicity assumptions, which are more nuanced than in the standard setting. As is often the case with principal stratum effects, the assumptions underlying the identification of the SF-ACE from the data are untestable and can be too strong. Therefore, we also develop sensitivity analysis methods that relax these assumptions. We present 3 different estimators, including a doubly robust estimator, for the SF-ACE. We implement our methodology for data from 2 large cohorts to study the heterogeneity in the causal effect of smoking on colorectal cancer with respect to MSI subtypes.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 model-free framework for evaluating the reliability of a new device with multiple imperfect reference standards.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf025

Ying Cui, Qi Yu, Amita Manatunga, Jeong Hoon Jang

{"title":"A model-free framework for evaluating the reliability of a new device with multiple imperfect reference standards.","authors":"Ying Cui, Qi Yu, Amita Manatunga, Jeong Hoon Jang","doi":"10.1093/biomtc/ujaf025","DOIUrl":"10.1093/biomtc/ujaf025","url":null,"abstract":"A common practice for establishing the reliability of a new computer-aided diagnostic (CAD) device is to evaluate how well its clinical measurements agree with those of a gold standard test. However, in many clinical studies, a gold standard is unavailable, and one needs to aggregate information from multiple imperfect reference standards for evaluation. A key challenge here is the heterogeneity in diagnostic accuracy across different reference standards, which may lead to biased evaluation of a device if improperly accounted for during the aggregation process. We propose an intuitive and easy-to-use statistical framework for evaluation of a device by assessing agreement between its measurements and the weighted sum of measurements from multiple imperfect reference standards, where weights representing relative reliability of each reference standard are determined by a model-free, unsupervised inductive procedure. Specifically, the inductive procedure recursively assigns higher weights to reference standards whose assessments are more consistent with each other and form a majority opinion, while assigning lower weights to those with greater discrepancies. Unlike existing methods, our approach does not require any modeling assumptions or external data to quantify heterogeneous accuracy levels of reference standards. It only requires specifying an appropriate agreement index used for weight assignment and device evaluation. The framework is applied to evaluate a CAD device for kidney obstruction by comparing its diagnostic ratings with those of multiple nuclear medicine physicians.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11911720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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