IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf037

Aristidis K Nikoloulopoulos

引用次数: 0

Distance weighted directional regression for Fréchet sufficient dimension reduction. 距离加权方向回归法用于fracei的充分降维。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf051

Chao Ying, Zhou Yu, Xin Zhang

{"title":"Distance weighted directional regression for Fréchet sufficient dimension reduction.","authors":"Chao Ying, Zhou Yu, Xin Zhang","doi":"10.1093/biomtc/ujaf051","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf051","url":null,"abstract":"Analysis of non-Euclidean data accumulated from human longevity studies, brain functional network studies, and many other areas has become an important issue in modern statistics. Fréchet sufficient dimension reduction aims to identify dependencies between non-Euclidean object-valued responses and multivariate predictors while simultaneously reducing the dimensionality of the predictors. We introduce the distance weighted directional regression method for both linear and nonlinear Fréchet sufficient dimension reduction. We propose a new formulation of the classical directional regression method in sufficient dimension reduction. The new formulation is based on distance weighting, thus providing a unified approach for sufficient dimension reduction with Euclidean and non-Euclidean responses, and is further extended to nonlinear Fréchet sufficient dimension reduction. We derive the asymptotic normality of the linear Fréchet directional regression estimator and the convergence rate of the nonlinear estimator. Simulation studies are presented to demonstrate the empirical performance of the proposed methods and to support our theoretical findings. The application to human mortality modeling and diabetes prevalence analysis show that our proposal can improve interpretation and out-of-sample prediction.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143975586","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

PDC-MAKES: a conditional screening method for controlling false discoveries in high-dimensional multi-response setting. PDC-MAKES：一种控制高维多响应环境中错误发现的条件筛选方法。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf042

Wei Xiong, Han Pan, Tong Shen

{"title":"PDC-MAKES: a conditional screening method for controlling false discoveries in high-dimensional multi-response setting.","authors":"Wei Xiong, Han Pan, Tong Shen","doi":"10.1093/biomtc/ujaf042","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf042","url":null,"abstract":"The coexistences of high dimensionality and strong correlation in both responses and predictors pose unprecedented challenges in identifying important predictors. In this paper, we propose a model-free conditional feature screening method with false discovery rate (FDR) control for ultrahigh-dimensional multi-response setting. The proposed method is built upon partial distance correlation, which measures the dependence between two random vectors while controlling effect for a multivariate random vector. This screening approach is robust against heavy-tailed data and can select predictors in instances of high correlation among predictors. Additionally, it can identify predictors that are marginally unrelated but conditionally related with the response. Leveraging the advantageous properties of partial distance correlation, our method allows for high-dimensional variables to be conditioned upon, distinguishing it from current research in this field. To further achieve FDR control, we apply derandomized knockoff-e-values to establish the threshold for feature screening more stably. The proposed FDR control method is shown to enjoy sure screening property while maintaining FDR control as well as achieving higher power under mild conditions. The superior performance of these methods is demonstrated through simulation examples and a real data application.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962459","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

Discrete-time competing-risks regression with or without penalization. 有或没有惩罚的离散时间竞争风险回归。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf040

Tomer Meir, Malka Gorfine

引用次数: 0

Statistical inference on the relative risk following covariate-adaptive randomization. 协变量自适应随机化后相对风险的统计推断。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf036

Fengyu Zhao, Yang Liu, Feifang Hu

引用次数: 0

Optimal dynamic treatment regime estimation in the presence of nonadherence. 存在不依从的最优动态治疗方案估计。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf041

Dylan Spicker, Michael P Wallace, Grace Y Yi

{"title":"Optimal dynamic treatment regime estimation in the presence of nonadherence.","authors":"Dylan Spicker, Michael P Wallace, Grace Y Yi","doi":"10.1093/biomtc/ujaf041","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf041","url":null,"abstract":"Dynamic treatment regimes (DTRs) are sequences of functions that formalize the process of precision medicine. DTRs take as input patient information and output treatment recommendations. A major focus of the DTR literature has been on the estimation of optimal DTRs, the sequences of decision rules that result in the best outcome in expectation, across the complete population if they were to be applied. While there is a rich literature on optimal DTR estimation, to date, there has been minimal consideration of the impacts of nonadherence on these estimation procedures. Nonadherence refers to any process through which an individual's prescribed treatment does not match their true treatment. We explore the impacts of nonadherence and demonstrate that, generally, when nonadherence is ignored, suboptimal regimes will be estimated. In light of these findings, we propose a method for estimating optimal DTRs in the presence of nonadherence. The resulting estimators are consistent and asymptotically normal, with a double robustness property. Using simulations, we demonstrate the reliability of these results, and illustrate comparable performance between the proposed estimation procedure adjusting for the impacts of nonadherence and estimators that are computed on data without nonadherence.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143953088","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

Bayesian covariate-dependent graph learning with a dual group spike-and-slab prior. 具有双群峰-板先验的贝叶斯协变量相关图学习。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf053

Zijian Zeng, Meng Li, Marina Vannucci

{"title":"Bayesian covariate-dependent graph learning with a dual group spike-and-slab prior.","authors":"Zijian Zeng, Meng Li, Marina Vannucci","doi":"10.1093/biomtc/ujaf053","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf053","url":null,"abstract":"Covariate-dependent graph learning has gained increasing interest in the graphical modeling literature for the analysis of heterogeneous data. This task, however, poses challenges to modeling, computational efficiency, and interpretability. The parameter of interest can be naturally represented as a 3-dimensional array with elements that can be grouped according to 2 directions, corresponding to node level and covariate level, respectively. In this article, we propose a novel dual group spike-and-slab prior that enables multi-level selection at covariate-level and node-level, as well as individual (local) level sparsity. We introduce a nested strategy with specific choices to address distinct challenges posed by the various grouping directions. For posterior inference, we develop a full Gibbs sampler for all parameters, which mitigates the difficulties of parameter tuning often encountered in high-dimensional graphical models and facilitates routine implementation. Through simulation studies, we demonstrate that the proposed model outperforms existing methods in its accuracy of graph recovery. We show the practical utility of our model via an application to microbiome data where we seek to better understand the interactions among microbes as well as how these are affected by relevant covariates.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962246","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

Continuous-space occupancy models. 连续空间占用模型。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf055

Wilson J Wright, Mevin B Hooten

{"title":"Continuous-space occupancy models.","authors":"Wilson J Wright, Mevin B Hooten","doi":"10.1093/biomtc/ujaf055","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf055","url":null,"abstract":"Occupancy models are used to infer species distributions over large spatial extents while accounting for imperfect detection. Current approaches, however, are unable to model species occurrence over continuous spatial domains while accounting for the discrete spatial domain of the observed data. We develop a new class of spatial occupancy models that embeds a change of spatial support between the observed data and occurrence process. We use a clipped Gaussian process to represent species occurrence in continuous space, which can provide inferences at a finer resolution than the observed occupancy data. Our approach is beneficial because it allows for more realistic models of species occurrence, can account for species occurring in only a portion of a surveyed site, and can relate detection probabilities to these within-site occurrence proportions. We show how our model can be fit using Bayesian methods and develop a computationally efficient MCMC algorithm. In particular, we rely on a Vecchia approximation to implement the spatial Gaussian process describing species occurrence and develop a surrogate data approach for jointly updating the spatial terms and spatial covariance parameters. We demonstrate our model using simulated data and compare our approach to alternative spatial occupancy models. We also use our model to analyze ovenbird occurrence data collected in New Hampshire, USA.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969710","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 case-cohort studies with a cure fraction. 提高具有治愈率的病例队列研究的估计效率。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf059

Qingning Zhou, Xu Cao

{"title":"Improving estimation efficiency for case-cohort studies with a cure fraction.","authors":"Qingning Zhou, Xu Cao","doi":"10.1093/biomtc/ujaf059","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf059","url":null,"abstract":"In the studies of time-to-event outcomes, it often happens that a fraction of subjects will never experience the event of interest, and these subjects are said to be cured. The studies with a cure fraction often yield a low event rate. To reduce cost and enhance study power, 2-phase sampling designs are often adopted, especially when the covariates of interest are expensive to measure or obtain. In this paper, we consider the generalized case-cohort design for studies with a cure fraction. Under this design, the expensive covariates are measured for a subset of the study cohort, called subcohort, and for all or a subset of the remaining subjects outside the subcohort who have experienced the event during the study, called cases. We propose a 2-step estimation procedure under a class of semiparametric transformation mixture cure models. We first develop a sieve maximum weighted likelihood method based only on the complete data and also devise an Expectation-Maximization (EM) algorithm for implementation. We then update the resulting estimator via a working model between the outcome and cheap covariates or auxiliary variables using the full data. We show that the proposed update estimator is consistent and asymptotically at least as efficient as the complete-data estimator, regardless of whether the working model is correctly specified or not. We also propose a weighted bootstrap procedure for variance estimation. Extensive simulation studies demonstrate the superior performance of the proposed method in finite-sample. An application to the National Wilms' Tumor Study is provided for illustration.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970761","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

Data integration methods for micro-randomized trials. 微随机试验的数据整合方法。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf002

E Huch, I Nahum-Shani, L Potter, C Lam, D W Wetter, W Dempsey

引用次数: 0

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