Biometrics最新文献_第3页

De-biasing the bias: methods for improving disparity assessments with noisy group measurements. 消除偏差：用噪声组测量改进差异评估的方法。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae155

Solvejg Wastvedt, Joshua Snoke, Denis Agniel, Julie Lai, Marc N Elliott, Steven C Martino

{"title":"De-biasing the bias: methods for improving disparity assessments with noisy group measurements.","authors":"Solvejg Wastvedt, Joshua Snoke, Denis Agniel, Julie Lai, Marc N Elliott, Steven C Martino","doi":"10.1093/biomtc/ujae155","DOIUrl":"10.1093/biomtc/ujae155","url":null,"abstract":"Health care decisions are increasingly informed by clinical decision support algorithms, but these algorithms may perpetuate or increase racial and ethnic disparities in access to and quality of health care. Further complicating the problem, clinical data often have missing or poor quality racial and ethnic information, which can lead to misleading assessments of algorithmic bias. We present novel statistical methods that allow for the use of probabilities of racial/ethnic group membership in assessments of algorithm performance and quantify the statistical bias that results from error in these imputed group probabilities. We propose a sensitivity analysis approach to estimating the statistical bias that allows practitioners to assess disparities in algorithm performance under a range of assumed levels of group probability error. We also prove theoretical bounds on the statistical bias for a set of commonly used fairness metrics and describe real-world scenarios where our theoretical results are likely to apply. We present a case study using imputed race and ethnicity from the modified Bayesian Improved First and Surname Geocoding algorithm for estimation of disparities in a clinical decision support algorithm used to inform osteoporosis treatment. Our novel methods allow policymakers to understand the range of potential disparities under a given algorithm even when race and ethnicity information is missing and to make informed decisions regarding the implementation of machine learning for clinical decision support.","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":"142891791","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 Bayesian framework for causal analysis of recurrent events with timing misalignment. 对具有时间错位的重复事件进行因果分析的贝叶斯框架。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae145

Arman Oganisian, Anthony Girard, Jon A Steingrimsson, Patience Moyo

{"title":"A Bayesian framework for causal analysis of recurrent events with timing misalignment.","authors":"Arman Oganisian, Anthony Girard, Jon A Steingrimsson, Patience Moyo","doi":"10.1093/biomtc/ujae145","DOIUrl":"10.1093/biomtc/ujae145","url":null,"abstract":"Observational studies of recurrent event rates are common in biomedical statistics. Broadly, the goal is to estimate differences in event rates under 2 treatments within a defined target population over a specified follow-up window. Estimation with observational data is challenging because, while membership in the target population is defined in terms of eligibility criteria, treatment is rarely observed exactly at the time of eligibility. Ad hoc solutions to this timing misalignment can induce bias by incorrectly attributing prior event counts and person-time to treatment. Even if eligibility and treatment are aligned, a terminal event process (eg, death) often stops the recurrent event process of interest. In practice, both processes can be censored so that events are not observed over the entire follow-up window. Our approach addresses misalignment by casting it as a time-varying treatment problem: some patients are on treatment at eligibility while others are off treatment but may switch to treatment at a specified time-if they survive long enough. We define and identify an average causal effect estimand under right-censoring. Estimation is done using a g-computation procedure with a joint semiparametric Bayesian model for the death and recurrent event processes. We apply the method to contrast hospitalization rates among patients with different opioid treatments using Medicare insurance claims 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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827256","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

Estimation of a genetic Gaussian network using GWAS summary data. 利用 GWAS 摘要数据估算遗传高斯网络。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae148

Yihe Yang, Noah Lorincz-Comi, Xiaofeng Zhu

引用次数: 0

A Bayesian joint model for mediation analysis with matrix-valued mediators. 矩阵值中介分析的贝叶斯联合模型。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae143

Zijin Liu, Zhihui Amy Liu, Ali Hosni, John Kim, Bei Jiang, Olli Saarela

{"title":"A Bayesian joint model for mediation analysis with matrix-valued mediators.","authors":"Zijin Liu, Zhihui Amy Liu, Ali Hosni, John Kim, Bei Jiang, Olli Saarela","doi":"10.1093/biomtc/ujae143","DOIUrl":"https://doi.org/10.1093/biomtc/ujae143","url":null,"abstract":"Unscheduled treatment interruptions may lead to reduced quality of care in radiation therapy (RT). Identifying the RT prescription dose effects on the outcome of treatment interruptions, mediated through doses distributed into different organs at risk (OARs), can inform future treatment planning. The radiation exposure to OARs can be summarized by a matrix of dose-volume histograms (DVH) for each patient. Although various methods for high-dimensional mediation analysis have been proposed recently, few studies investigated how matrix-valued data can be treated as mediators. In this paper, we propose a novel Bayesian joint mediation model for high-dimensional matrix-valued mediators. In this joint model, latent features are extracted from the matrix-valued data through an adaptation of probabilistic multilinear principal components analysis (MPCA), retaining the inherent matrix structure. We derive and implement a Gibbs sampling algorithm to jointly estimate all model parameters, and introduce a Varimax rotation method to identify active indicators of mediation among the matrix-valued data. Our simulation study finds that the proposed joint model has higher efficiency in estimating causal decomposition effects compared to an alternative two-step method, and demonstrates that the mediation effects can be identified and visualized in the matrix form. We apply the method to study the effect of prescription dose on treatment interruptions in anal canal cancer patients.","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":"142821775","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

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

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

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

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

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