Biometrics最新文献_第6页

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

Conformal predictive intervals in survival analysis: a resampling approach. 生存分析中的适形预测区间：重采样方法。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf063

Jing Qin, Jin Piao, Jing Ning, Yu Shen

{"title":"Conformal predictive intervals in survival analysis: a resampling approach.","authors":"Jing Qin, Jin Piao, Jing Ning, Yu Shen","doi":"10.1093/biomtc/ujaf063","DOIUrl":"10.1093/biomtc/ujaf063","url":null,"abstract":"The distribution-free method of conformal prediction has gained considerable attention in computer science, machine learning, and statistics. Candès et al. extended this method to right-censored survival data, addressing right-censoring complexity by creating a covariate shift setting, extracting a subcohort of subjects with censoring times exceeding a fixed threshold. Their approach only estimates the lower prediction bound for type I censoring, where all subjects have available censoring times regardless of their failure status. In medical applications, we often encounter more general right-censored data, observing only the minimum of failure time and censoring time. Subjects with observed failure times have unavailable censoring times. To address this, we propose a bootstrap method to construct 1- as well as 2-sided conformal predictive intervals for general right-censored survival data under different working regression models. Through simulations, our method demonstrates excellent average coverage for the lower bound and good coverage for the 2-sided predictive interval, regardless of working model is correctly specified or not, particularly under moderate censoring. We further extend the proposed method to several directions in medical applications. We apply this method to predict breast cancer patients' future survival times based on tumor characteristics and treatment.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144141216","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

Estimating optimally tailored active surveillance strategy under interval censoring. 区间审查下的最优主动监控策略估计。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf067

Muxuan Liang, Yingqi Zhao, Daniel W Lin, Matthew Cooperberg, Yingye Zheng

{"title":"Estimating optimally tailored active surveillance strategy under interval censoring.","authors":"Muxuan Liang, Yingqi Zhao, Daniel W Lin, Matthew Cooperberg, Yingye Zheng","doi":"10.1093/biomtc/ujaf067","DOIUrl":"10.1093/biomtc/ujaf067","url":null,"abstract":"Active surveillance (AS) using repeated biopsies to monitor disease progression has been a popular alternative to immediate surgical intervention in cancer care. However, a biopsy procedure is invasive and sometimes leads to severe side effects of infection and bleeding. To reduce the burden of repeated surveillance biopsies, biomarker-assistant decision rules are sought to replace the fix-for-all regimen with tailored biopsy intensity for individual patients. Constructing or evaluating such decision rules is challenging. The key AS outcome is often ascertained subject to interval censoring. Furthermore, patients will discontinue participation in the AS study once they receive a positive surveillance biopsy. Thus, patient dropout is affected by the outcomes of these biopsies. This work proposes a non-parametric kernel-based method to estimate a tailored AS strategy's true positive rates (TPRs) and true negative rates (TNRs), accounting for interval censoring and immediate dropouts. We develop a weighted classification framework based on these estimates to estimate the optimally tailored AS strategy and further incorporate the cost-benefit ratio for cost-effectiveness in medical decision-making. Theoretically, we provide a uniform generalization error bound of the derived AS strategy, accommodating all possible trade-offs between TPRs and TNRs. Simulation and application to a prostate cancer surveillance study show the superiority of the proposed method.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12123698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186381","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

Towards efficient and interpretable assumption-lean generalized linear modeling of continuous exposure effects. 面向连续暴露效应的有效和可解释的假设精益广义线性模型。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf071

Stijn Vansteelandt

{"title":"Towards efficient and interpretable assumption-lean generalized linear modeling of continuous exposure effects.","authors":"Stijn Vansteelandt","doi":"10.1093/biomtc/ujaf071","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf071","url":null,"abstract":"Advances in causal inference have largely ignored continuous exposures, apart from model-based approaches, which face criticism due to potential model misspecification. Model-free approaches based on modified treatment policies, such as uniformly shifting each subject's observed exposure, have emerged as promising alternatives. However, because such interventions are impractical, it is necessary to evaluate a range of possible shifts to generate actionable insights. To address this, we introduce models that parameterize the effects of shift interventions across varying magnitudes, coupled with assumption-lean estimation strategies. To ensure validity and interpretability under model misspecification, we tailor these to minimize (squared) bias in estimating the effects of realistic shifts. We employ debiased machine learning procedures for this but observe them to exhibit erratic behavior under certain data-generating mechanisms, prompting two key innovations. First, we propose a broadly applicable debiasing procedure that yields estimators with significantly improved finite-sample properties and is of independent methodological interest. Second, we develop debiased machine learning estimators for estimands with a more favorable efficiency bound, but more nuanced interpretation when models are misspecified. Unlike existing projection estimators, our methods avoid inverse exposure density weighting and do not demand tailored shift interventions to address positivity violations. Extensive simulations and a re-analysis of the Bangladesh Wash Benefits study demonstrate the effectiveness, stability, and utility of our approach. This work advances assumption-lean methods that balance validity, interpretability, and efficiency.","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":"144324437","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

COCA: a randomized Bayesian design integrating dose optimization and component contribution assessment for combination therapies. COCA：一个随机贝叶斯设计，将剂量优化和成分贡献评估整合到联合治疗中。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf077

Xiaohan Chi, Ruitao Lin, Ying Yuan

{"title":"COCA: a randomized Bayesian design integrating dose optimization and component contribution assessment for combination therapies.","authors":"Xiaohan Chi, Ruitao Lin, Ying Yuan","doi":"10.1093/biomtc/ujaf077","DOIUrl":"10.1093/biomtc/ujaf077","url":null,"abstract":"In cancer treatment, the development of combination therapies requires demonstrating the contribution of each individual drug and optimizing the dose during early-phase trials. This necessitates a large sample size, presenting formidable obstacles for drug developers. To address this issue, we propose a 2-stage randomized phase II design that seamlessly integrates combination dose optimization with component contribution assessment. In stage 1, the optimal combination dose is determined by maximizing the risk-benefit tradeoff across multiple candidate combination doses. In stage 2, a multi-arm randomized phase is initiated to evaluate the contribution of each component within the combination therapy. To increase trial efficiency and reduce the sample size, efficacy data from both stages are adaptively combined using a Bayesian logistic regression model with a spike-and-slab prior. The sample size and decision cutoffs of the proposed design are systematically determined based on a novel calibration procedure to achieve desired operating characteristics. Extensive simulation studies show that the proposed design achieves the dual goals of dose optimization and contribution assessment, while yielding substantial sample size savings compared to competing designs.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12206157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144473981","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

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