Biometrics最新文献_第5页

A positivity robust strategy to study effects of switching treatment. 一个积极稳健的策略来研究转换治疗的效果。

IF 1.4 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf085

Matias Janvin, Pål C Ryalen, Aaron L Sarvet, Mats J Stensrud

引用次数: 0

The Cox-Pólya-Gamma algorithm for flexible Bayesian inference of multilevel survival models. 多级生存模型的灵活贝叶斯推理Cox-Pólya-Gamma算法。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf121

Benny Ren, Jeffrey S Morris, Ian Barnett

{"title":"The Cox-Pólya-Gamma algorithm for flexible Bayesian inference of multilevel survival models.","authors":"Benny Ren, Jeffrey S Morris, Ian Barnett","doi":"10.1093/biomtc/ujaf121","DOIUrl":"10.1093/biomtc/ujaf121","url":null,"abstract":"Bayesian Cox semiparametric regression is an important problem in many clinical settings. The elliptical information geometry of Cox models is underutilized in Bayesian inference but can effectively bridge survival analysis and hierarchical Gaussian models. Survival models should be able to incorporate multilevel modeling such as case weights, frailties, and smoothing splines, in a straightforward manner similar to Gaussian models. To tackle these challenges, we propose the Cox-Pólya-Gamma algorithm for Bayesian multilevel Cox semiparametric regression and survival functions. Our novel computational procedure succinctly addresses the difficult problem of monotonicity-constrained modeling of the nonparametric baseline cumulative hazard along with multilevel regression. We develop two key strategies based on the elliptical geometry of Cox models that allows computation to be implemented in a few lines of code. First, we exploit an approximation between Cox models and negative binomial processes through the Poisson process to reduce Bayesian computation to iterative Gaussian sampling. Next, we appeal to sufficient dimension reduction to address the difficult computation of nonparametric baseline cumulative hazards, allowing for the collapse of the Markov transition within the Gibbs sampler based on beta sufficient statistics. We explore conditions for uniform ergodicity of the Cox-Pólya-Gamma algorithm. We provide software and demonstrate our multilevel modeling approach using open-source data and simulations.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449235/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145091074","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 Bayesian semiparametric mixture model for clustering zero-inflated microbiome data. 零膨胀微生物组数据聚类的贝叶斯半参数混合模型。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf125

Suppapat Korsurat, Matthew D Koslovsky

{"title":"A Bayesian semiparametric mixture model for clustering zero-inflated microbiome data.","authors":"Suppapat Korsurat, Matthew D Koslovsky","doi":"10.1093/biomtc/ujaf125","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf125","url":null,"abstract":"Microbiome research has immense potential for unlocking insights into human health and disease. A common goal in human microbiome research is identifying subgroups of individuals with similar microbial composition that may be linked to specific health states or environmental exposures. However, existing clustering methods are often not equipped to accommodate the complex structure of microbiome data and typically make limiting assumptions regarding the number of clusters in the data which can bias inference. Designed for zero-inflated multivariate compositional count data collected in microbiome research, we propose a novel Bayesian semiparametric mixture modeling framework that simultaneously learns the number of clusters in the data while performing cluster allocation. In simulation, we demonstrate the clustering performance of our method compared to distance- and model-based alternatives and the importance of accommodating zero-inflation when present in the data. We then apply the model to identify clusters in microbiome data collected in a study designed to investigate the relation between gut microbial composition and enteric diarrheal disease.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145124127","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

Vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard. 没有金标准的诊断准确性研究的meta分析Vine copula混合模型。

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

Probabilistic exponential family inverse regression and its applications. 概率指数族逆回归及其应用。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf065

Daolin Pang, Ruoqing Zhu, Hongyu Zhao, Tao Wang

{"title":"Probabilistic exponential family inverse regression and its applications.","authors":"Daolin Pang, Ruoqing Zhu, Hongyu Zhao, Tao Wang","doi":"10.1093/biomtc/ujaf065","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf065","url":null,"abstract":"Rapid advances in high-throughput sequencing technologies have led to the fast accumulation of high-dimensional data, which is harnessed for understanding the implications of various factors on human disease and health. While dimension reduction plays an essential role in high-dimensional regression and classification, existing methods often require the predictors to be continuous, making them unsuitable for discrete data, such as presence-absence records of species in community ecology and sequencing reads in single-cell studies. To identify and estimate sufficient reductions in regressions with discrete predictors, we introduce probabilistic exponential family inverse regression (PrEFIR), assuming that, given the response and a set of latent factors, the predictors follow one-parameter exponential families. We show that the low-dimensional reductions result not only from the response variable but also from the latent factors. We further extend the latent factor modeling framework to the double exponential family by including an additional parameter to account for the dispersion. This versatile framework encompasses regressions with all categorical or a mixture of categorical and continuous predictors. We propose the method of maximum hierarchical likelihood for estimation, and develop a highly parallelizable algorithm for its computation. The effectiveness of PrEFIR is demonstrated through simulation studies and real data examples.","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":"144126551","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

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