Biometrics最新文献_第10页

Spatially adaptive variable screening in presurgical functional magnetic resonance imaging data analysis. 术前功能磁共振成像数据分析中的空间自适应变量筛选。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae157

Yifei Hu, Xinge Jessie Jeng

{"title":"Spatially adaptive variable screening in presurgical functional magnetic resonance imaging data analysis.","authors":"Yifei Hu, Xinge Jessie Jeng","doi":"10.1093/biomtc/ujae157","DOIUrl":"https://doi.org/10.1093/biomtc/ujae157","url":null,"abstract":"Accurate delineation of functional brain regions adjacent to tumors is imperative for planning neurosurgery that preserves critical functions. Functional magnetic resonance imaging (fMRI) plays an increasingly pivotal role in presurgical counseling and planning. In the analysis of presurgical fMRI data, the impact of false negatives on patients surpasses that of false positives because failure to identify functional regions and unintentionally resecting critical tissues can result in severe harm to patients. This paper introduces a novel metric, the Bayesian missed discovery rate (BMDR), designed for controlling false negatives within the voxel-specific mixture model. Building on the BMDR metric, we propose a new variable screening procedure that not only ensures effective control of false negatives but also capitalizes on the spatial structure of fMRI data. In comparison to existing statistical methods in fMRI data analysis, our new procedure directly regulates false negatives at a desirable level and is entirely data-driven. Moreover, it significantly differs from current false-negative control procedures by incorporating spatial information. Numerical examples demonstrate that the new method outperforms several state-of-the-art methods in retaining signal voxels, particularly the subtle ones at the boundaries of functional regions, while achieving a cleaner separation of functional regions from background noise. These findings hold promising implications for planning function-preserving neurosurgery.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920669","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

Robust and flexible learning of a high-dimensional classification rule using auxiliary outcomes. 利用辅助结果稳健灵活地学习高维分类规则。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae144

Muxuan Liang, Jaeyoung Park, Qing Lu, Xiang Zhong

引用次数: 0

An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables. 采用贝叶斯平均模型对预测变量进行选择和阈值识别的自适应富集设计。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae141

Lara Maleyeff, Shirin Golchi, Erica E M Moodie, Marie Hudson

{"title":"An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables.","authors":"Lara Maleyeff, Shirin Golchi, Erica E M Moodie, Marie Hudson","doi":"10.1093/biomtc/ujae141","DOIUrl":"10.1093/biomtc/ujae141","url":null,"abstract":"Precision medicine is transforming healthcare by offering tailored treatments that enhance patient outcomes and reduce costs. As our understanding of complex diseases improves, clinical trials increasingly aim to detect subgroups of patients with enhanced treatment effects. Biomarker-driven adaptive enrichment designs, which initially enroll a broad population and later restrict to treatment-sensitive patients, are gaining popularity. However, current practice often assumes either pre-trial knowledge of biomarkers or a simple, linear relationship between continuous markers and treatment effectiveness. Motivated by a trial studying rheumatoid arthritis treatment, we propose a Bayesian adaptive enrichment design to identify predictive variables from a larger set of candidate biomarkers. Our approach uses a flexible modeling framework where the effects of continuous biomarkers are represented using free knot B-splines. We then estimate key parameters by marginalizing over all possible variable combinations using Bayesian model averaging. At interim analyses, we assess whether a biomarker-defined subgroup has enhanced or reduced treatment effects, allowing for early termination for efficacy or futility and restricting future enrollment to treatment-sensitive patients. We consider both pre-categorized and continuous biomarkers, the latter potentially having complex, nonlinear relationships to the outcome and treatment effect. Through simulations, we derive the operating characteristics of our design and compare its performance to existing methods.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827260","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

Causal effect estimation in survival analysis with high dimensional confounders. 具有高维度混杂因素的生存分析中的因果效应估计。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae110

Fei Jiang, Ge Zhao, Rosa Rodriguez-Monguio, Yanyuan Ma

{"title":"Causal effect estimation in survival analysis with high dimensional confounders.","authors":"Fei Jiang, Ge Zhao, Rosa Rodriguez-Monguio, Yanyuan Ma","doi":"10.1093/biomtc/ujae110","DOIUrl":"10.1093/biomtc/ujae110","url":null,"abstract":"With the ever advancing of modern technologies, it has become increasingly common that the number of collected confounders exceeds the number of subjects in a data set. However, matching based methods for estimating causal treatment effect in their original forms are not capable of handling high-dimensional confounders, and their various modified versions lack statistical support and valid inference tools. In this article, we propose a new approach for estimating causal treatment effect, defined as the difference of the restricted mean survival time (RMST) under different treatments in high-dimensional setting for survival data. We combine the factor model and the sufficient dimension reduction techniques to construct propensity score and prognostic score. Based on these scores, we develop a kernel based doubly robust estimator of the RMST difference. We demonstrate its link to matching and establish the consistency and asymptotic normality of the estimator. We illustrate our method by analyzing a dataset from a study aimed at comparing the effects of two alternative treatments on the RMST of patients with diffuse large B cell lymphoma.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11472547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457172","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

Wasserstein regression with empirical measures and density estimation for sparse data. 稀疏数据的瓦瑟斯坦回归与经验度量和密度估计。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae127

Yidong Zhou, Hans-Georg Müller

{"title":"Wasserstein regression with empirical measures and density estimation for sparse data.","authors":"Yidong Zhou, Hans-Georg Müller","doi":"10.1093/biomtc/ujae127","DOIUrl":"https://doi.org/10.1093/biomtc/ujae127","url":null,"abstract":"The problem of modeling the relationship between univariate distributions and one or more explanatory variables lately has found increasing interest. Existing approaches proceed by substituting proxy estimated distributions for the typically unknown response distributions. These estimates are obtained from available data but are problematic when for some of the distributions only few data are available. Such situations are common in practice and cannot be addressed with currently available approaches, especially when one aims at density estimates. We show how this and other problems associated with density estimation such as tuning parameter selection and bias issues can be side-stepped when covariates are available. We also introduce a novel version of distribution-response regression that is based on empirical measures. By avoiding the preprocessing step of recovering complete individual response distributions, the proposed approach is applicable when the sample size available for each distribution varies and especially when it is small for some of the distributions but large for others. In this case, one can still obtain consistent distribution estimates even for distributions with only few data by gaining strength across the entire sample of distributions, while traditional approaches where distributions or densities are estimated individually fail, since sparsely sampled densities cannot be consistently estimated. The proposed model is demonstrated to outperform existing approaches through simulations and Environmental Influences on Child Health Outcomes data.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142581081","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 formal goodness-of-fit test for spatial binary Markov random field models. 空间二元马尔可夫随机场模型的正式拟合优度检验。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae119

Eva Biswas, Andee Kaplan, Mark S Kaiser, Daniel J Nordman

引用次数: 0

Derivation of outcome-dependent dietary patterns for low-income women obtained from survey data using a supervised weighted overfitted latent class analysis. 利用监督加权过度拟合潜类分析法，从调查数据中得出低收入妇女依赖结果的饮食模式。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae122

Stephanie M Wu, Matthew R Williams, Terrance D Savitsky, Briana J K Stephenson

{"title":"Derivation of outcome-dependent dietary patterns for low-income women obtained from survey data using a supervised weighted overfitted latent class analysis.","authors":"Stephanie M Wu, Matthew R Williams, Terrance D Savitsky, Briana J K Stephenson","doi":"10.1093/biomtc/ujae122","DOIUrl":"10.1093/biomtc/ujae122","url":null,"abstract":"Poor diet quality is a key modifiable risk factor for hypertension and disproportionately impacts low-income women. Analyzing diet-driven hypertensive outcomes in this demographic is challenging due to the complexity of dietary data and selection bias when the data come from surveys, a main data source for understanding diet-disease relationships in understudied populations. Supervised Bayesian model-based clustering methods summarize dietary data into latent patterns that holistically capture relationships among foods and a known health outcome but do not sufficiently account for complex survey design. This leads to biased estimation and inference and lack of generalizability of the patterns. To address this, we propose a supervised weighted overfitted latent class analysis (SWOLCA) based on a Bayesian pseudo-likelihood approach that integrates sampling weights into an exposure-outcome model for discrete data. Our model adjusts for stratification, clustering, and informative sampling, and handles modifying effects via interaction terms within a Markov chain Monte Carlo Gibbs sampling algorithm. Simulation studies confirm that the SWOLCA model exhibits good performance in terms of bias, precision, and coverage. Using data from the National Health and Nutrition Examination Survey (2015-2018), we demonstrate the utility of our model by characterizing dietary patterns associated with hypertensive outcomes among low-income women in the United States.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11518851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142520912","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

Adaptive randomization methods for sequential multiple assignment randomized trials (smarts) via thompson sampling. 基于汤普森抽样的顺序多任务随机试验的自适应随机化方法。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae152

Peter Norwood, Marie Davidian, Eric Laber

{"title":"Adaptive randomization methods for sequential multiple assignment randomized trials (smarts) via thompson sampling.","authors":"Peter Norwood, Marie Davidian, Eric Laber","doi":"10.1093/biomtc/ujae152","DOIUrl":"10.1093/biomtc/ujae152","url":null,"abstract":"Response-adaptive randomization (RAR) has been studied extensively in conventional, single-stage clinical trials, where it has been shown to yield ethical and statistical benefits, especially in trials with many treatment arms. However, RAR and its potential benefits are understudied in sequential multiple assignment randomized trials (SMARTs), which are the gold-standard trial design for evaluation of multi-stage treatment regimes. We propose a suite of RAR algorithms for SMARTs based on Thompson Sampling (TS), a widely used RAR method in single-stage trials in which treatment randomization probabilities are aligned with the estimated probability that the treatment is optimal. We focus on two common objectives in SMARTs: (1) comparison of the regimes embedded in the trial and (2) estimation of an optimal embedded regime. We develop valid post-study inferential procedures for treatment regimes under the proposed algorithms. This is nontrivial, as even in single-stage settings standard estimators of an average treatment effect can have nonnormal asymptotic behavior under RAR. Our algorithms are the first for RAR in multi-stage trials that account for non-standard limiting behavior due to RAR. Empirical studies based on real-world SMARTs show that TS can improve in-trial subject outcomes without sacrificing efficiency for post-trial comparisons.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11647911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827259","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

An efficient joint model for high dimensional longitudinal and survival data via generic association features. 基于通用关联特征的高维纵向数据和生存数据的高效联合模型。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae149

Van Tuan Nguyen, Adeline Fermanian, Antoine Barbieri, Sarah Zohar, Anne-Sophie Jannot, Simon Bussy, Agathe Guilloux

{"title":"An efficient joint model for high dimensional longitudinal and survival data via generic association features.","authors":"Van Tuan Nguyen, Adeline Fermanian, Antoine Barbieri, Sarah Zohar, Anne-Sophie Jannot, Simon Bussy, Agathe Guilloux","doi":"10.1093/biomtc/ujae149","DOIUrl":"https://doi.org/10.1093/biomtc/ujae149","url":null,"abstract":"This paper introduces a prognostic method called FLASH that addresses the problem of joint modeling of longitudinal data and censored durations when a large number of both longitudinal and time-independent features are available. In the literature, standard joint models are either of the shared random effect or joint latent class type. Combining ideas from both worlds and using appropriate regularization techniques, we define a new model with the ability to automatically identify significant prognostic longitudinal features in a high-dimensional context, which is of increasing importance in many areas such as personalized medicine or churn prediction. We develop an estimation methodology based on the expectation-maximization algorithm and provide an efficient implementation. The statistical performance of the method is demonstrated both in extensive Monte Carlo simulation studies and on publicly available medical datasets. Our method significantly outperforms the state-of-the-art joint models in terms of C-index in a so-called \"real-time\" prediction setting, with a computational speed that is orders of magnitude faster than competing methods. In addition, our model automatically identifies significant features that are relevant from a practical point of view, making it interpretable, which is of the greatest importance for a prognostic algorithm in healthcare.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827261","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

Debiased high-dimensional regression calibration for errors-in-variables log-contrast models. 变量误差对数对比模型的去偏高维回归校正。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae153

Huali Zhao, Tianying Wang

引用次数: 0