Biometrics最新文献_第8页

Efficient interaction analysis in randomized controlled trials. 随机对照试验的有效相互作用分析。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag074

Likun Zhang, Wei Ma

{"title":"Efficient interaction analysis in randomized controlled trials.","authors":"Likun Zhang, Wei Ma","doi":"10.1093/biomtc/ujag074","DOIUrl":"10.1093/biomtc/ujag074","url":null,"abstract":"Identifying treatment-covariate interaction is an initial step toward revealing treatment effect heterogeneity and advancing precision medicine. However, when the covariate of interest is continuous, the definition of treatment-covariate interaction can be ambiguous or rely on questionable model assumptions. To tackle this challenge, we introduce a model-free framework for interaction analysis in randomized controlled trials, advocating the adoption of a clearly defined target parameter for interaction and thereby avoiding assumptions about the functional form of the data-generating mechanism. To ensure feasibility for most randomized controlled trials encountered in practice, we study interaction analysis under covariate-adaptive randomization, including simple and stratified randomization, and minimization methods. We observe that the usual method for interaction analysis can either exaggerate or understate uncertainty, leading to asymptotically conservative or anti-conservative results. We modify the usual method with a consistent variance estimator. Furthermore, by acknowledging the dependence structure of treatment assignment induced by covariate-adaptive randomization, we compute the semiparametric efficiency bound and introduce a novel semiparametric efficient method for analyzing our specified interaction effect. We demonstrate that our semiparametric efficient inference procedure, equipped with nonparametric and machine learning techniques for adjusting baseline covariate information, is both efficient and widely applicable.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147939896","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

Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments. 从部分重叠治疗的多个研究中对个性化治疗规则的综合学习。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag083

Yuan Bian, Donglin Zeng, Hyun-Joon Yang, Leanne M Williams, Yuanjia Wang

{"title":"Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments.","authors":"Yuan Bian, Donglin Zeng, Hyun-Joon Yang, Leanne M Williams, Yuanjia Wang","doi":"10.1093/biomtc/ujag083","DOIUrl":"10.1093/biomtc/ujag083","url":null,"abstract":"An individualized treatment rule (ITR) tailors treatments to a patient's specific characteristics. However, randomized controlled trials (RCTs) are often underpowered to detect the treatment effect heterogeneity needed for reliable ITR estimation. To address this limitation, there is growing interest in leveraging information from multiple studies to improve statistical power and support individualized decision-making. A key challenge in this context is that available RCTs may not evaluate the same set of treatments. In this paper, we propose an integrative learning framework that synthesizes evidence across multiple RCTs that share a common comparator but differ in their alternative treatment arms. Our method integrates information through a regularized weighted misclassification risk function and adaptively determines the contribution of each study to the ITRs of the others. We rigorously study the excess risk of the resulting estimator. Simulation studies demonstrate that the proposed approaches improve the estimation of both value and benefit functions. We illustrate the utility of our methodology using data from two landmark studies of major depressive disorder: the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care study and the International Study to Predict Optimized Treatment in Depression study, both of which include a selective serotonin reuptake inhibitor as a common treatment arm. We find that the separate learning method outperforms one-size-fits-all methods, and our integrative methods further improve performance.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13227520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148142643","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

Heterogeneous causal mediation analysis using Bayesian additive regression trees. 基于贝叶斯加性回归树的异质性因果中介分析。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag079

Chen Liu, Xu Qin, Victor B Talisa, Jiebiao Wang

{"title":"Heterogeneous causal mediation analysis using Bayesian additive regression trees.","authors":"Chen Liu, Xu Qin, Victor B Talisa, Jiebiao Wang","doi":"10.1093/biomtc/ujag079","DOIUrl":"10.1093/biomtc/ujag079","url":null,"abstract":"Causal mediation analysis provides insights into the mechanisms through which treatments affect outcomes. While mediation effects often vary across individuals, most existing methods focus solely on population-average effects, overlooking individual-level heterogeneity. To address this limitation, we propose a Bayesian regression tree ensemble method that flexibly models nonlinear relationships and captures treatment-by-mediator interactions in the mediation process. Using hierarchical posterior sampling, our approach provides credible intervals with nominal coverage rates for inferring heterogeneous mediation effects. Additionally, we leverage regression tree summaries to identify subgroups with distinct mediation effects and employ SHapley Additive exPlanation values to highlight key moderators and their influence on the mediation process. Comprehensive simulations demonstrate the method's accuracy in estimating and inferring heterogeneous mediation effects. Finally, we apply our method to investigate the heterogeneous mediation role of Alzheimer's disease pathology burden in the effect of apolipoprotein E genotype on late-life cognition.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13199065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148013335","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 shrinkage priors for penalized synthetic control estimators in the presence of spillovers. 存在溢出时惩罚综合控制估计器的贝叶斯收缩先验。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag054

Esteban Fernández-Morales, Arman Oganisian, Youjin Lee

{"title":"Bayesian shrinkage priors for penalized synthetic control estimators in the presence of spillovers.","authors":"Esteban Fernández-Morales, Arman Oganisian, Youjin Lee","doi":"10.1093/biomtc/ujag054","DOIUrl":"10.1093/biomtc/ujag054","url":null,"abstract":"Synthetic control (SC) methods are widely used to estimate the effects of policy interventions, especially those targeting specific geographic regions, referred to as units. These methods construct a weighted combination of untreated units, forming a \"synthetic\" control that approximates the counterfactual outcomes of the treated unit had the intervention not occurred. Although neighboring areas are often selected as controls due to their similarity in observed and unobserved characteristics, their proximity can lead to spillover effects, where the intervention indirectly impacts control units, potentially biasing causal estimates. To address this challenge, we introduce a Bayesian SC framework with utility-based shrinkage priors. Our approach extends traditional penalization techniques (i.e., horseshoe, spike-and-slab) by incorporating a utility function that combines covariate similarity and spatial distance. This provides a metric that guides the data-driven selection of control units based on their relevance and spillover risk, which is assumed to increase with spatial proximity. Rather than outright excluding neighboring units, the method balances bias and variance by reducing the importance of potentially contaminated controls by spillovers. We evaluate the proposed method through simulation studies at varying spillover levels and apply it to assess the impact of Philadelphia's 2017 beverage tax on the sales of sugar-sweetened and artificially sweetened beverages in mass merchandise stores.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13097100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147697368","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

Regression methods for cost-effectiveness analysis with different censoring times or terminating events for survival time and costs. 不同审查时间或终止事件的生存时间和成本的成本效益分析的回归方法。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag073

Dingning Liu, Shuai Chen

{"title":"Regression methods for cost-effectiveness analysis with different censoring times or terminating events for survival time and costs.","authors":"Dingning Liu, Shuai Chen","doi":"10.1093/biomtc/ujag073","DOIUrl":"10.1093/biomtc/ujag073","url":null,"abstract":"Cost-effectiveness analysis (CEA) is crucial for evaluating new medical treatments. In many studies, both costs and effectiveness are censored. While standard survival analysis methods suit survival time, they cannot be directly applied to cumulative outcomes (e.g., costs or quality-adjusted lifetime) due to induced informative censoring. Additional challenges arise when costs and effectiveness have different terminating events or censoring times. Our motivating examples are two cardiovascular trials: MADIT-CRT and MADIT-II. In MADIT-CRT, effectiveness was defined as heart failure-free survival, while costs accumulated until death, leading to different terminating events for costs and effectiveness. Furthermore, subgroup identification for patient heterogeneity was also of interest. In MADIT-II, early stopping of cost collection for some patients complicated CEA. These examples highlight the need for methods handling different censoring structures while allowing covariate adjustment to improve efficiency and address imperfect randomization. Although some CEA methods have been proposed for different terminating events or censoring times, none provide covariate adjustment. We propose regression-based methods for CEA with different terminating events or censoring times, focusing on estimating the incremental cost-effectiveness ratio and the incremental net benefit. By incorporating covariates within a regression framework, our methods enable covariate adjustment and subgroup identification. Simulation studies demonstrate good finite-sample performance, and applications to the two motivating examples show that our approach provides practical tools for CEA under complex censoring mechanisms.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13164937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147926046","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 collaborative learning of the average treatment effect. 高效协作学习的平均治疗效果。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag076

Sijia Li, Rui Duan

{"title":"Efficient collaborative learning of the average treatment effect.","authors":"Sijia Li, Rui Duan","doi":"10.1093/biomtc/ujag076","DOIUrl":"10.1093/biomtc/ujag076","url":null,"abstract":"In response to the growing need for generating real-world evidence from multisite collaborative studies, we introduce an efficient collaborative learning approach to evaluate average treatment effect (ECO-ATE) in a multisite setting under data-sharing constraints. Specifically, ECO-ATE operates in a federated manner, using individual-level data from a user-defined target population and summary statistics from other source populations, to construct efficient estimator for the average treatment effect on the target population of interest. Our federated approach does not require iterative communications between sites, making it particularly suitable for research consortia with limited resources for developing automated data-sharing infrastructures. Compared to existing work data integration methods in causal inference, ECO-ATE allows distributional shifts in outcomes, treatments, and baseline covariates distributions, and achieves semiparametric efficiency bound under appropriate conditions. We conduct simulation studies to demonstrate the extent of efficiency gains achieved by incorporating additional data sources, as well as the robustness of our approach against varying levels of distributional shifts and overparameterization, compared to existing benchmarks. We apply ECO-ATE to a case study examining the effect of insulin versus non-insulin treatments on heart failure for patients with type II diabetes using electronic health record data collected from the All of Us program.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13595063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147947911","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

Borrowing strength across exposures and outcomes via index models for multi-pollutant mixtures. 通过多污染物混合物的指数模型借用暴露强度和结果。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag072

Glen McGee, Joseph Antonelli

{"title":"Borrowing strength across exposures and outcomes via index models for multi-pollutant mixtures.","authors":"Glen McGee, Joseph Antonelli","doi":"10.1093/biomtc/ujag072","DOIUrl":"10.1093/biomtc/ujag072","url":null,"abstract":"An important goal of environmental health research is to assess the health risks posed by mixtures of multiple environmental exposures. In these mixtures analyses, flexible models such as Bayesian kernel machine regression and multiple index models are appealing because they allow for arbitrary non-linear exposure-outcome relationships. However, this flexibility comes at the cost of low power, particularly when exposures are highly correlated and the health effects are weak, as is typical in environmental health studies. We propose a multivariate index modeling strategy that borrows strength across exposures and outcomes by exploiting similar mixture component weights and exposure-response relationships. In the special case of distributed lag models, in which exposures are measured repeatedly over time, we jointly encourage co-clustering of lag profiles and exposure-response curves to more efficiently identify critical windows of vulnerability and characterize important exposure effects. We then extend the proposed approach to the multiple index model setting where the true index structure-the number of indices and their composition-is unknown, and introduce variable importance measures to quantify component contributions to mixture effects. Using time series data from the National Morbidity, Mortality and Air Pollution Study, we demonstrate the proposed methods by jointly modeling three mortality outcomes and two cumulative air pollution measurements with a maximum lag of 14 days.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147926010","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

Knowledge-guided Bayesian biclustering model for omics data with noisy graphs. 带有噪声图的组学数据的知识引导贝叶斯双聚类模型。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag070

Qiyiwen Zhang, Wenrui Li, Suprateek Kundu, Qi Long

{"title":"Knowledge-guided Bayesian biclustering model for omics data with noisy graphs.","authors":"Qiyiwen Zhang, Wenrui Li, Suprateek Kundu, Qi Long","doi":"10.1093/biomtc/ujag070","DOIUrl":"10.1093/biomtc/ujag070","url":null,"abstract":"Extracting biologically meaningful information from high-dimensional, heterogeneous omics data is one of the key challenges in many biomedical studies. Among various biomedical applications, disease subtyping is of particular interest due to its critical role in improving diagnosis and designing personalized treatments. To address this, biclustering has become a widely used statistical method for subtyping. Additionally, it has been demonstrated across various statistical learning methods that incorporating biological graph knowledge such as gene regulatory network can significantly enhance variable selection, prediction accuracy, and model interpretability. However, existing graph-guided methods, while often yielding promising results, tend to overlook potential misspecifications, such as false positive (FP) and false negative (FN) edges in the graphs. Ignoring this noise can lead to suboptimal identification of biclusters. Therefore, it is essential to develop biclustering methods that can effectively handle noisy graphs as well as provide biological insight. We propose a Bayesian denoising knowledge-guided biclustering method that enables to integrate multiple graphs simultaneously. The incorporated graphs, viewed as noisy variations of the underlying true graph, are de-noised through modeling of FP and FN errors. A Markov chain Monte Carlo algorithm is developed to estimate the biclusters. Extensive simulation studies and real data analyses, including gene expression and proteomics datasets of Alzheimer's disease, have been conducted to validate the superior performance of the proposed method.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13181264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962013","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

Uncertainty quantification and multi-stage variable selection for personalized treatment regimes. 个性化治疗方案的不确定性量化和多阶段变量选择。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag081

Jiefeng Bi, Matteo Borrotti, Bernardo Nipoti

{"title":"Uncertainty quantification and multi-stage variable selection for personalized treatment regimes.","authors":"Jiefeng Bi, Matteo Borrotti, Bernardo Nipoti","doi":"10.1093/biomtc/ujag081","DOIUrl":"10.1093/biomtc/ujag081","url":null,"abstract":"A dynamic treatment regime is a sequence of medical decisions that adapts to the evolving clinical status of a patient over time. To facilitate personalized care, it is crucial to assess the probability of each available treatment option being optimal for a specific patient, while also identifying the key prognostic factors that determine the optimal sequence of treatments. This task has become increasingly challenging due to the growing number of individual prognostic factors typically available. In response to these challenges, we propose a Bayesian model for optimizing dynamic treatment regimes that addresses the uncertainty in identifying optimal decision sequences and incorporates dimensionality reduction to manage high-dimensional individual covariates. The first task is achieved through a suitable augmentation of the model to handle counterfactual variables. For the second, we introduce a novel class of spike-and-slab priors for the multi-stage selection of significant factors, to favor the sharing of information across stages. The effectiveness of the proposed approach is demonstrated through an extensive simulation study and illustrated using clinical trial data on severe acute arterial hypertension.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147980371","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 decision-theoretic approach to multiple testing in basket trials. 篮子试验中多重检验的贝叶斯决策理论方法。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag093

Amartya Kumar Maulik, Tianjian Zhou

{"title":"A Bayesian decision-theoretic approach to multiple testing in basket trials.","authors":"Amartya Kumar Maulik, Tianjian Zhou","doi":"10.1093/biomtc/ujag093","DOIUrl":"10.1093/biomtc/ujag093","url":null,"abstract":"Basket trials evaluate a single treatment across multiple patient subpopulations, posing important multiple testing problems. Motivated by this setting, we propose a novel Bayesian decision-theoretic approach based on a family of loss functions. These functions adaptively penalize false positives and false negatives based on the numbers of true null and alternative hypotheses, making a basket more likely to be identified as promising when others show promise. Estimation of treatment effects remains independent across baskets, avoiding the computational burden of existing hierarchical modeling approaches and allowing straightforward generalization to trials with different endpoints. The degree of borrowing and the conservativeness of decisions are controlled by tuning parameters, which can be calibrated to achieve desired control of frequentist error rates. The optimal Bayes decision rule is derived by minimizing the posterior expected loss and is computationally efficient. Simulation studies demonstrate that the proposed approach exhibits desirable operating characteristics, performing competitively with widely used alternatives. We illustrate its practical utility through an application to a basket trial of vemurafenib.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148013330","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