Biometrics最新文献_第4页

Density and treatment effect estimation under covariate-adaptive randomization with heavy-tailed outcomes. 重尾结果协变量自适应随机化下的密度和治疗效果估计。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag159

Hongzi Li, Wei Ma, Yingying Ma, Hanzhong Liu

{"title":"Density and treatment effect estimation under covariate-adaptive randomization with heavy-tailed outcomes.","authors":"Hongzi Li, Wei Ma, Yingying Ma, Hanzhong Liu","doi":"10.1093/biomtc/ujag159","DOIUrl":"https://doi.org/10.1093/biomtc/ujag159","url":null,"abstract":"Randomized experiments are the gold standard for investigating causal relationships, with comparisons of potential outcomes under different treatment groups used to estimate treatment effects. However, outcomes with heavy-tailed distributions pose significant challenges to traditional causal inference approaches. While recent studies have explored these issues under simple randomization, their application in more complex randomization designs, such as stratified randomization or covariate-adaptive randomization, has not been adequately addressed. To fill the gap, we first investigate the performance of nonparametric kernel density estimation methods under covariate-adaptive randomization, thereby establishing theoretical guarantees for treatment effect estimators based on the estimated densities. Second, we demonstrate the application of our density estimation framework to estimate the density treatment effect and overall quantile treatment effect, deriving the consistency and asymptotic normality of the estimators. For the overall quantile treatment effect, we show that the existing variance estimator for the influence function-based M-estimator tends to overestimate the asymptotic variance, especially under more balanced designs, and lacks universal applicability across randomization methods. To remedy this, we introduce a novel stratified transformed difference-in-means estimator to enhance efficiency and propose a universally applicable variance estimator to facilitate valid inferences. Numerical results demonstrate the effectiveness of the proposed methods in finite samples.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011847","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

Monitoring adverse events through Bayesian nonparametric clustering across studies. 通过跨研究的贝叶斯非参数聚类监测不良事件。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag127

Shijie Yuan, Kevin Roberts, Noirrit Kiran Chandra, Yuan Ji, Peter Müller

引用次数: 0

Subgroup identification and membership prediction. 子组识别和成员预测。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag122

Lu Chen, Xuerong Chen, Xinzhou Guo, Yi Li

{"title":"Subgroup identification and membership prediction.","authors":"Lu Chen, Xuerong Chen, Xinzhou Guo, Yi Li","doi":"10.1093/biomtc/ujag122","DOIUrl":"https://doi.org/10.1093/biomtc/ujag122","url":null,"abstract":"In clinical trials, a treatment rarely benefits every patient, underscoring the need to identify subgroups that are more likely to respond. Traditional subgroup analysis approaches, including finite mixture and threshold models, often rely on stringent distributional assumptions and prespecified subgroup structures that may be unrealistic in practice. Moreover, the resulting subgroups can be difficult to interpret and may not generalize well to new patients. To address these challenges, we propose a new least-squares regression framework that accommodates flexible subgroup structure in heterogeneous data. Our model is distribution-free and allows subgroup membership to depend on covariates, while permitting both the number and the organization of coefficient groups to vary across covariates. Building on regularization, we develop a computationally efficient procedure to detect subgroup structure in linear regression coefficients and then use a support vector machine to recover the corresponding partitions, enabling subgroup membership prediction for future individuals. Relative to pairwise fused regularization, our approach substantially reduces computational complexity. We also establish theoretical guarantees for estimation of group-specific parameters and recovery of the underlying partitions. Simulation studies and a real-data application illustrate the practical effectiveness of the proposed method.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148629614","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 contamination model for serial dilution assays. 连续稀释试验的贝叶斯污染模型。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag134

Siquan Wang, Qixuan Chen, Matthew S Perzanowski, Adnan Divjan, Andrew Gelman

{"title":"A Bayesian contamination model for serial dilution assays.","authors":"Siquan Wang, Qixuan Chen, Matthew S Perzanowski, Adnan Divjan, Andrew Gelman","doi":"10.1093/biomtc/ujag134","DOIUrl":"10.1093/biomtc/ujag134","url":null,"abstract":"Serial dilution assays are essential tools in biomedical research. Current analytical methods work well when measurements are in the steep part of the calibration curve but are not so efficient when measurements are near the upper and lower bounds. Moreover, sample contamination is common in serial dilution assays and can result in problematic concentration estimation. To address these challenges, we propose a Bayesian contamination model that simultaneously flags contaminated samples and estimates the concentrations of unknown samples, while quantifying estimation uncertainty. In extensive simulation studies under various contamination scenarios, our method consistently outperforms commonly used approaches. We further demonstrate a step-by-step application of a Bayesian workflow for modeling contamination using raw immunoassay data from the New York City Neighborhood Asthma and Allergy Study. This work advances previous research by introducing a practical method for detecting contaminated samples and estimating unknown concentrations in immunoassays and by presenting a general framework for incorporating unknown contamination processes into the Bayesian modeling workflow.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13397503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148577014","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

Efficient estimation for deep generalized accelerated hazards models with interval-censored data. 区间截尾数据下深度广义加速危险模型的有效估计。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag140

Qiang Wu, Mingyue Du, Shuangge Ma, Xingqiu Zhao

引用次数: 0

Individual causal effect estimation accounting for latent disease state modification among bipolar participants in mobile health studies. 流动健康研究中双相障碍参与者潜在疾病状态改变的个体因果效应估计。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag131

Charlotte R Fowler, Xiaoxuan Cai, Habiballah Rahimi-Eichi, Lisa Dixon, Justin T Baker, Jukka-Pekka Onnela, Linda Valeri

{"title":"Individual causal effect estimation accounting for latent disease state modification among bipolar participants in mobile health studies.","authors":"Charlotte R Fowler, Xiaoxuan Cai, Habiballah Rahimi-Eichi, Lisa Dixon, Justin T Baker, Jukka-Pekka Onnela, Linda Valeri","doi":"10.1093/biomtc/ujag131","DOIUrl":"10.1093/biomtc/ujag131","url":null,"abstract":"Individuals with bipolar disorder tend to cycle through disease states such as depression and mania. The heterogeneous nature of disease across states complicates the evaluation of interventions for bipolar disorder patients, as varied interventional success is observed within and across individuals. We hypothesize that disease state acts as an effect modifier for the causal effect of a given intervention on health outcomes. To address this dilemma, we propose an N-of-1 approach using an adapted auto-regressive hidden Markov model, applied to longitudinal mobile health data collected from individuals with bipolar disorder. This method allows us to identify a latent variable from mobile health data to be treated as an effect modifier between the exposure and outcome of interest while allowing for missing data. A counterfactual approach is employed for causal inference and to obtain a g-formula estimator to recover said effect. The performance of the proposed method is compared with naive approaches across extensive simulations and application to a multi-year smartphone study of bipolar patients, evaluating the individual effect of social activity on sleep duration across different latent disease states.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13377531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148468414","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 transfer learning model for mixed-effects longitudinal data analysis. 混合效应纵向数据分析的贝叶斯迁移学习模型。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag130

Jialing Liu, Souradipto Ghosh Dastidar, Steffen Ventz

{"title":"A Bayesian transfer learning model for mixed-effects longitudinal data analysis.","authors":"Jialing Liu, Souradipto Ghosh Dastidar, Steffen Ventz","doi":"10.1093/biomtc/ujag130","DOIUrl":"10.1093/biomtc/ujag130","url":null,"abstract":"The analysis of contemporary longitudinal data problems often involves high-dimensional measurements of time-course data collected on a small number of observations. The estimation of such models with limited sample sizes can pose substantial challenges and lead to highly variable estimates, unstable predictions, and low power. In such instances, it is natural to borrow information from additional datasets with similar, although not necessarily identical, covariate-outcome relations to improve inference in the target population. We develop a novel Bayesian transfer learning model for longitudinal data (BTLL). BTLL leverages mixture models for the discrepancies between the pivotal parameters of the outcome models of the target and source studies to enhance estimation accuracy and enable data-adaptive information borrowing. BTLL aims to minimize the negative transfer of information from source studies that would otherwise introduce large bias into inference in the target population. Through extensive simulations and real data applications, we show that BTLL improves the precision of parameter estimates in the target study substantially compared to alternative methods. Moreover, our BTLL reduces the estimation bias in heterogeneous settings when the outcome distributions of the target and source datasets deviate from each other. Motivated by the progressive nature of dementia and the importance of early detection, we develop a BTLL prediction model using datasets for individuals with mild cognitive impairment. We show that BTLL can leverage non-invasive biomarkers to identify subjects at high risk for dementia and consistently achieves the lowest prediction error.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13377532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148468344","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

Bayesian optimization for identification of optimal biological dose combinations in personalized dose-finding trials. 个性化剂量寻找试验中最佳生物剂量组合鉴定的贝叶斯优化。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag152

James Willard, Shirin Golchi, Erica E M Moodie

{"title":"Bayesian optimization for identification of optimal biological dose combinations in personalized dose-finding trials.","authors":"James Willard, Shirin Golchi, Erica E M Moodie","doi":"10.1093/biomtc/ujag152","DOIUrl":"https://doi.org/10.1093/biomtc/ujag152","url":null,"abstract":"Early-phase, personalized dose-finding trials for combination therapies seek to identify patient-specific optimal biological dose (OBD) combinations, which are defined as safe dose combinations that maximize therapeutic benefit for a specific covariate pattern. Given the small sample sizes that are typical of these trials, it is challenging for traditional parametric approaches to identify OBD combinations across multiple dosing agents and covariate patterns. To address these challenges, we propose a Bayesian optimization approach to dose-finding that incorporates efficacy and toxicity information into the sequential search strategy. Independent Gaussian processes are used to model the efficacy and toxicity surfaces, and an acquisition function is utilized to define the dose-finding strategy. Furthermore, we define an adaptive stopping rule using the posterior entropy for the location of the OBD. This work is motivated by a personalized dose-finding trial which considers a dual-agent therapy for obstructive sleep apnea (OSA), where OBD combinations are tailored to OSA severity. Via a simulation study, the approach is first investigated across varying degrees of response heterogeneity for both efficacy and toxicity, and then a collection of final designs for the OSA trial are compared. We demonstrate that the proposed approach toward personalized dose-finding yields good performance under the considered scenarios.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148928823","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

Reliable fairness auditing with semi-supervised inference. 具有半监督推理的可靠公平性审计。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag142

Jianhui Gao, Jessica Gronsbell

引用次数: 0

Inferring leakage in imports of frozen seafood allowing for censoring, testing accuracy and a minimum positive prevalence. 推断进口冷冻海产品的泄漏，允许审查，测试准确性和最低阳性流行率。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag155

Sumonkanti Das, Robert G Clark, Mahdi Parsa, Belinda Barnes

{"title":"Inferring leakage in imports of frozen seafood allowing for censoring, testing accuracy and a minimum positive prevalence.","authors":"Sumonkanti Das, Robert G Clark, Mahdi Parsa, Belinda Barnes","doi":"10.1093/biomtc/ujag155","DOIUrl":"https://doi.org/10.1093/biomtc/ujag155","url":null,"abstract":"Many countries screen import consignments to guard against the entry of exotic pests, contaminants, and pathogens. A widely used strategy is to pool individual units into groups and test each group for presence or absence of contamination. Consignments are typically rejected if there are any detections. Screening samples are commonly designed to give a high chance of detection assuming a design prevalence. What is less common, however, is to analyze the history of testing outcomes to infer how many accepted consignments contain contaminated units, and the number of contaminated units-jointly referred to as \"leakage.\" We build on existing censored beta-binomial models to answer these questions for the importation of frozen seafood into Australia, allowing for unknown test sensitivity and specificity. We present new theory and empirical results demonstrating that specificity is identifiable from test data in this context, but sensitivity is not. We also develop a new class of models in which consignment propensity is either zero or above a minimum positive prevalence threshold, motivated by our case study but applicable more widely. Past testing data are modeled under multiple scenarios using both hierarchical Bayes and maximum likelihood methods, revealing new insights into the risk of leakage.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148987813","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