Biometrics最新文献_第4页

Penalized G-estimation for effect modifier selection in a structural nested mean model for repeated outcomes. 在重复结果的结构嵌套平均模型中对效果修饰符选择的惩罚g估计。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae165

Ajmery Jaman, Guanbo Wang, Ashkan Ertefaie, Michèle Bally, Renée Lévesque, Robert W Platt, Mireille E Schnitzer

{"title":"Penalized G-estimation for effect modifier selection in a structural nested mean model for repeated outcomes.","authors":"Ajmery Jaman, Guanbo Wang, Ashkan Ertefaie, Michèle Bally, Renée Lévesque, Robert W Platt, Mireille E Schnitzer","doi":"10.1093/biomtc/ujae165","DOIUrl":"10.1093/biomtc/ujae165","url":null,"abstract":"Effect modification occurs when the impact of the treatment on an outcome varies based on the levels of other covariates known as effect modifiers. Modeling these effect differences is important for etiological goals and for purposes of optimizing treatment. Structural nested mean models (SNMMs) are useful causal models for estimating the potentially heterogeneous effect of a time-varying exposure on the mean of an outcome in the presence of time-varying confounding. A data-adaptive selection approach is necessary if the effect modifiers are unknown a priori and need to be identified. Although variable selection techniques are available for estimating the conditional average treatment effects using marginal structural models or for developing optimal dynamic treatment regimens, all of these methods consider a single end-of-follow-up outcome. In the context of an SNMM for repeated outcomes, we propose a doubly robust penalized G-estimator for the causal effect of a time-varying exposure with a simultaneous selection of effect modifiers and prove the oracle property of our estimator. We conduct a simulation study for the evaluation of its performance in finite samples and verification of its double-robustness property. Our work is motivated by the study of hemodiafiltration for treating patients with end-stage renal disease at the Centre Hospitalier de l'Université de Montréal. We apply the proposed method to investigate the effect heterogeneity of dialysis facility on the repeated session-specific hemodiafiltration outcomes.","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":"142999234","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

Report of the Editors-2024. 编辑报告-2024。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf004

引用次数: 0

Robust Bayesian graphical regression models for assessing tumor heterogeneity in proteomic networks. 评估蛋白质组学网络中肿瘤异质性的稳健贝叶斯图形回归模型。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae160

Tsung-Hung Yao, Yang Ni, Anindya Bhadra, Jian Kang, Veerabhadran Baladandayuthapani

{"title":"Robust Bayesian graphical regression models for assessing tumor heterogeneity in proteomic networks.","authors":"Tsung-Hung Yao, Yang Ni, Anindya Bhadra, Jian Kang, Veerabhadran Baladandayuthapani","doi":"10.1093/biomtc/ujae160","DOIUrl":"10.1093/biomtc/ujae160","url":null,"abstract":"Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of two canonical assumptions: (i) a homogeneous graph with a common network for all subjects or (ii) an assumption of normality, especially in the context of Gaussian graphical models. Both assumptions are restrictive and can fail to hold in certain applications such as proteomic networks in cancer. To this end, we propose an approach termed robust Bayesian graphical regression (rBGR) to estimate heterogeneous graphs for non-normally distributed data. rBGR is a flexible framework that accommodates non-normality through random marginal transformations and constructs covariate-dependent graphs to accommodate heterogeneity through graphical regression techniques. We formulate a new characterization of edge dependencies in such models called conditional sign independence with covariates, along with an efficient posterior sampling algorithm. In simulation studies, we demonstrate that rBGR outperforms existing graphical regression models for data generated under various levels of non-normality in both edge and covariate selection. We use rBGR to assess proteomic networks in lung and ovarian cancers to systematically investigate the effects of immunogenic heterogeneity within tumors. Our analyses reveal several important protein-protein interactions that are differentially associated with the immune cell abundance; some corroborate existing biological knowledge, whereas others are novel findings.","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":"142969463","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

Composite likelihood inference for space-time point processes. 时空点过程的复合似然推理。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf009

Abdollah Jalilian, Francisco Cuevas-Pacheco, Ganggang Xu, Rasmus Waagepetersen

{"title":"Composite likelihood inference for space-time point processes.","authors":"Abdollah Jalilian, Francisco Cuevas-Pacheco, Ganggang Xu, Rasmus Waagepetersen","doi":"10.1093/biomtc/ujaf009","DOIUrl":"10.1093/biomtc/ujaf009","url":null,"abstract":"The dynamics of a rain forest is extremely complex involving births, deaths, and growth of trees with complex interactions between trees, animals, climate, and environment. We consider the patterns of recruits (new trees) and dead trees between rain forest censuses. For a current census, we specify regression models for the conditional intensity of recruits and the conditional probabilities of death given the current trees and spatial covariates. We estimate regression parameters using conditional composite likelihood functions that only involve the conditional first order properties of the data. When constructing assumption lean estimators of covariance matrices of parameter estimates, we only need mild assumptions of decaying conditional correlations in space, while assumptions regarding correlations over time are avoided by exploiting conditional centering of composite likelihood score functions. Time series of point patterns from rain forest censuses are quite short, while each point pattern covers a fairly big spatial region. To obtain asymptotic results, we therefore use a central limit theorem for the fixed timespan-increasing spatial domain asymptotic setting. This also allows us to handle the challenge of using stochastic covariates constructed from past point patterns. Conveniently, it suffices to impose weak dependence assumptions on the innovations of the space-time process. We investigate the proposed methodology by simulation studies and an application to rain forest data.","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":"143405365","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 general, flexible, and harmonious framework to construct interpretable functions in regression analysis. 在回归分析中构造可解释函数的一个通用的、灵活的、和谐的框架。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf014

Tianyu Zhan, Jian Kang

{"title":"A general, flexible, and harmonious framework to construct interpretable functions in regression analysis.","authors":"Tianyu Zhan, Jian Kang","doi":"10.1093/biomtc/ujaf014","DOIUrl":"10.1093/biomtc/ujaf014","url":null,"abstract":"An interpretable model or method has several appealing features, such as reliability to adversarial examples, transparency of decision-making, and communication facilitator. However, interpretability is a subjective concept, and even its definition can be diverse. The same model may be deemed as interpretable by a study team, but regarded as a black-box algorithm by another squad. Simplicity, accuracy and generalizability are some additional important aspects of evaluating interpretability. In this work, we present a general, flexible and harmonious framework to construct interpretable functions in regression analysis with a focus on continuous outcomes. We formulate a functional skeleton in light of users' expectations of interpretability. A new measure based on Mallows's $C_p$-statistic is proposed for model selection to balance approximation, generalizability, and interpretability. We apply this approach to derive a sample size formula in adaptive clinical trial designs to demonstrate the general workflow, and to explain operating characteristics in a Bayesian Go/No-Go paradigm to show the potential advantages of using meaningful intermediate variables. Generalization to categorical outcomes is illustrated in an example of hypothesis testing based on Fisher's exact test. A real data analysis of NHANES (National Health and Nutrition Examination Survey) is conducted to investigate relationships between some important laboratory measurements. We also discuss some extensions of this method.","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":"143555802","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

Optimal treatment regime estimation in practice: challenges and choices in a randomized clinical trial for depression. 实践中的最佳治疗方案评估：抑郁症随机临床试验的挑战和选择。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf026

Florian Stijven, Trung Dung Tran, Ellen Driessen, Ariel Alonso Abad, Geert Molenberghs, Geert Verbeke, Iven Van Mechelen

{"title":"Optimal treatment regime estimation in practice: challenges and choices in a randomized clinical trial for depression.","authors":"Florian Stijven, Trung Dung Tran, Ellen Driessen, Ariel Alonso Abad, Geert Molenberghs, Geert Verbeke, Iven Van Mechelen","doi":"10.1093/biomtc/ujaf026","DOIUrl":"10.1093/biomtc/ujaf026","url":null,"abstract":"An important aspect of precision medicine is the tailoring of treatments to specific patient types. Nowadays, various methods are available to estimate for this purpose so-called optimal treatment regimes, that is, decision rules for treatment assignment that map patterns of pretreatment characteristics to treatment alternatives and that maximize the expected patient benefit. However, the application of these methods to real-life data has been limited and comes with nonstandard statistical issues. In search of best practices, we reanalyzed data from a randomized clinical trial for the treatment of dysthymic disorder. While the original objective of this trial was to detect a marginally best treatment alternative, we wanted to estimate an optimal treatment regime using 2 prominent estimation methods: Q-learning and value search estimation. An important obstacle in the dataset under study was the occurrence of missing values. This was handled with multiple imputation, a thoughtful implementation of which, however, implied several challenges. Other challenges were implied by the concrete implementation of value search estimation. In this paper, all the choices we have made in the analysis to handle the aforementioned issues are detailed together with a motivation and a description of possible alternatives. Accordingly, this paper may serve as a guide to apply optimal treatment regime estimation in data-analytic practice.","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":"143661937","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

Gaussian processes for time series with lead-lag effects with applications to biology data. 超前滞后效应时间序列的高斯过程及其在生物数据中的应用。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae156

Wancen Mu, Jiawen Chen, Eric S Davis, Kathleen Reed, Douglas Phanstiel, Michael I Love, Didong Li

{"title":"Gaussian processes for time series with lead-lag effects with applications to biology data.","authors":"Wancen Mu, Jiawen Chen, Eric S Davis, Kathleen Reed, Douglas Phanstiel, Michael I Love, Didong Li","doi":"10.1093/biomtc/ujae156","DOIUrl":"10.1093/biomtc/ujae156","url":null,"abstract":"Investigating the relationship, particularly the lead-lag effect, between time series is a common question across various disciplines, especially when uncovering biological processes. However, analyzing time series presents several challenges. Firstly, due to technical reasons, the time points at which observations are made are not at uniform intervals. Secondly, some lead-lag effects are transient, necessitating time-lag estimation based on a limited number of time points. Thirdly, external factors also impact these time series, requiring a similarity metric to assess the lead-lag relationship. To counter these issues, we introduce a model grounded in the Gaussian process, affording the flexibility to estimate lead-lag effects for irregular time series. In addition, our method outputs dissimilarity scores, thereby broadening its applications to include tasks such as ranking or clustering multiple pairwise time series when considering their strength of lead-lag effects with external factors. Crucially, we offer a series of theoretical proofs to substantiate the validity of our proposed kernels and the identifiability of kernel parameters. Our model demonstrates advances in various simulations and real-world applications, particularly in the study of dynamic chromatin interactions, compared to other leading methods.","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/PMC11704948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142943771","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. 通过基于网络的矩阵向量回归评估高通量结构神经成像预测因子对全脑功能连接组结果的影响。

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

Feature screening for metric space-valued responses based on Fréchet regression with its applications. 基于frsamet回归的度量空间值响应特征筛选及其应用。

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":"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. 区间剔除竞争风险数据下编译器随机治疗效果的工具变量估计。

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

引用次数: 0