Biometrics最新文献_第9页

Transfer learning estimation of the accelerated failure time model based on high-dimensional data. 基于高维数据的加速失效时间模型迁移学习估计。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag103

Yichen Lou, Mingyue Du, Hui Zhao, Jianguo Sun

{"title":"Transfer learning estimation of the accelerated failure time model based on high-dimensional data.","authors":"Yichen Lou, Mingyue Du, Hui Zhao, Jianguo Sun","doi":"10.1093/biomtc/ujag103","DOIUrl":"10.1093/biomtc/ujag103","url":null,"abstract":"Motivated by a study on seriously ill hospitalized adults to improve their end-of-life care, we consider estimation of the accelerated failure time model, one of the most commonly used models for regression analysis of failure time data. Although many methods have been developed for the problem, standard approaches may fail or underperform when available information is limited. To address this issue, we propose two transfer learning estimation procedures that leverage auxiliary information from multiple source datasets. The first is a data-driven source detection procedure that classifies the source datasets into positively and negatively transferable groups and performs estimation using only the positively transferable or informative source datasets. The other is an ensemble-based approach that adaptively assigns weights to source datasets based on their relevance to the target dataset. Theoretical justifications are provided for the proposed methods, and an extensive simulation study is performed, indicating that the proposed methods work well in practice. Finally, they are applied to the study above and identify some prognostic factors that would not be possible by using the existing methods.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148203952","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

Discussion on "INTACT: A method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou. 张静茹、崔二佳、李宏哲、寿浩昌对“integrity：一种多源纵向体育活动数据整合方法”的讨论。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag113

Haotian Lin, Matthew Reimherr

引用次数: 0

Competing-triggering effect models for multitype recurrent event data. 多类型重复事件数据的竞争触发效应模型。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag094

Tianhao Song, Jason Fine, Payal Khincha, Anastasia Ivanova, Paul Albert

{"title":"Competing-triggering effect models for multitype recurrent event data.","authors":"Tianhao Song, Jason Fine, Payal Khincha, Anastasia Ivanova, Paul Albert","doi":"10.1093/biomtc/ujag094","DOIUrl":"https://doi.org/10.1093/biomtc/ujag094","url":null,"abstract":"Multitype recurrent events frequently arise in lifetime data analysis. It is reasonable to assume that previously occurred events can trigger more events to come, either of homogeneous or of heterogeneous categories. Previous work has focused on nonlinear triggering models for the effects of previous events when there is only a single event type. The standard approach is a proportional intensity model, which captures both the magnitude of the triggering effect and its decay over time via a complicated function of time. We propose a general Cox-type structure for modeling the triggering effects among multiple types of events, with separate triggering effects for each event type on events of interest. The nonlinear model formulation permits parameters to be common across models for different event types. We derive partial likelihood estimators and establish the consistency and asymptotic normality of the estimators, as well as provide plug-in variance estimators. We demonstrate a good performance of the methods through a series of simulation experiments. The models are applied to a cohort of patients with Li-Fraumeni syndrome to analyze the triggering effects between breast cancers and non-breast cancers.","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":"148270119","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

Joint modeling of multiple longitudinal biomarkers and survival outcomes via threshold regression: variability as a predictor. 通过阈值回归对多个纵向生物标志物和生存结果的联合建模：可变性作为预测因子。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag080

Mingyan Yu, Zhenke Wu, Michelle M Hood, Carrie Karvonen-Gutierrez, Siobán D Harlow, Michael R Elliott

{"title":"Joint modeling of multiple longitudinal biomarkers and survival outcomes via threshold regression: variability as a predictor.","authors":"Mingyan Yu, Zhenke Wu, Michelle M Hood, Carrie Karvonen-Gutierrez, Siobán D Harlow, Michael R Elliott","doi":"10.1093/biomtc/ujag080","DOIUrl":"10.1093/biomtc/ujag080","url":null,"abstract":"Longitudinal biomarker data and health outcomes are routinely collected in many studies to assess how biomarker trajectories predict health outcomes. Existing methods primarily focus on mean biomarker profiles, treating variability as a nuisance. However, excess variability may indicate system dysregulations that may be associated with poor outcomes. In this paper, we address the long-standing problem of using variability information of multiple longitudinal biomarkers in time-to-event analyses by formulating and studying a Bayesian joint model. We first model multiple longitudinal biomarkers, some of which are subject to limit-of-detection censoring. We then model the survival times by incorporating random effects and variances from the longitudinal component as predictors through threshold regression that admits nonproportional hazards. We demonstrate the operating characteristics of the proposed joint model through simulations and apply it to data from the Study of Women's Health Across the Nation to investigate the impact of the mean and variability of follicle-stimulating hormone and anti-Müllerian hormone on age at the final menstrual period.","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/PMC13192250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147980385","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

Nonparanormal adjusted marginal inference. 非超自然调整边际推断。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag065

Susanne Dandl, Torsten Hothorn

{"title":"Nonparanormal adjusted marginal inference.","authors":"Susanne Dandl, Torsten Hothorn","doi":"10.1093/biomtc/ujag065","DOIUrl":"10.1093/biomtc/ujag065","url":null,"abstract":"Although treatment effects can be estimated from observed outcome distributions obtained from proper randomization in clinical trials, covariate adjustment is recommended to increase precision. For important treatment effects, such as odds or hazard ratios, conditioning on covariates in binary logistic or proportional hazards models changes the interpretation of the treatment effect, and conditioning on different sets of covariates renders the resulting effect estimates incomparable. We propose a novel nonparanormal model formulation for adjusted marginal inference. This model for the joint distribution of outcome and covariates directly features a marginally defined treatment effect parameter, such as a marginal odds or hazard ratio. Not only the marginal treatment effect of interest can be estimated based on this model, it also provides an overall coefficient of determination and covariate-specific measures of prognostic strength. For the special case of Cohen's standardized mean difference d, we theoretically show that adjusting for an informative prognostic variable improves the precision of the marginal, noncollapsible effect. Empirical results confirm this not only for Cohen's d but also for odds and hazard ratios in simulations and four applications. A reference implementation is available in the R add-on package tram.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147761408","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

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou. 张静茹、崔二佳、李宏哲、寿浩昌对“integrity：一种多源纵向体育活动数据整合方法”的讨论。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag115

Lily Koffman, Ciprian M Crainiceanu

引用次数: 0

Heterogeneity learning in distributed networks with large-scale survival data. 基于大规模生存数据的分布式网络异构学习。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag091

Tingting Cai, Tao Hu, Jianguo Sun, Mengqi Xie

引用次数: 0

Minimum noninferiority dose for phase I clinical trials with immunotherapy. 免疫治疗I期临床试验的最小非劣效性剂量。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag098

Ninghao Zhang, Guosheng Yin

{"title":"Minimum noninferiority dose for phase I clinical trials with immunotherapy.","authors":"Ninghao Zhang, Guosheng Yin","doi":"10.1093/biomtc/ujag098","DOIUrl":"10.1093/biomtc/ujag098","url":null,"abstract":"Recent advancements in immuno-oncology have significantly improved cancer treatments. Compared with traditional clinical trials, the toxicity of these novel therapies is generally low and tolerable, shifting the focus from solely managing toxicity to improving efficacy. Furthermore, such treatments can be costly, and thus it is crucial to identify a low-dose regimen with a good therapeutic effect for broader drug accessibility. Instead of solely identifying the optimal biological dose (OBD) in a phase I/II trial, we emphasize finding a more economical but effective dose. Current methods typically aim to determine the minimum effective dose (MED) based on a predefined efficacy target, which may not reflect the best balance between efficacy and dosage. This paper introduces the minimum noninferiority dose (MND), derived from the OBD, which eliminates the need for artificially setting an efficacy target. The MND ensures the dose maintains efficacy within a reasonable range below the OBD while keeping the dosage as low as possible. Through leveraging the calibration-free odds (CFO) design to monitor toxicity, we further propose a novel Bayesian two-stage design, called CFO-MND, by incorporating a trade-off between dose and efficacy as well as adaptive randomization. Our model-free approach is versatile and applicable to a wide range of scenarios. Furthermore, we incorporate causal inference into the CFO-MND design by introducing the placebo equivalent dose. This allows for preliminary estimation of the drug's average treatment effect at the MND, which provides valuable information for subsequent trials.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148154876","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 inference targeting a concentration index for studies of health inequalities. 针对健康不平等研究的集中指数的因果推理。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag082

Mohammad Ghasempour, Xavier de Luna, Per E Gustafsson

{"title":"Causal inference targeting a concentration index for studies of health inequalities.","authors":"Mohammad Ghasempour, Xavier de Luna, Per E Gustafsson","doi":"10.1093/biomtc/ujag082","DOIUrl":"10.1093/biomtc/ujag082","url":null,"abstract":"A concentration index, a standardised covariance between a health variable and relative income ranks, is often used to quantify income-related health inequalities. There is a lack of formal approach to study the effect of an exposure, e.g., education, on such measures of inequality. In this paper we contribute by filling this gap and developing the necessary theory and method. Thus, we define a counterfactual concentration index for different levels of an exposure. We give conditions for the identification of this complex estimand, and then deduce its efficient influence function. This allows us to propose estimators, which are regular asymptotic linear under certain conditions. In particular, we show that these estimators are $sqrt{n}$-consistent and asymptotically normal, as well as locally efficient. The implementation of the estimators is based on the fit of several nuisance functions. The estimators proposed have rate robustness properties allowing for convergence rates slower than $sqrt{n}$-rate for some of the nuisance function fits. The relevance of the asymptotic results for finite samples is studied with simulation experiments. We also present a case study of the effect of education on income-related health inequalities for a Swedish cohort.","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":"147961979","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

Nonparametric estimation of the total treatment effect with multiple outcomes in the presence of terminal events. 在存在终末期事件的情况下，多结局的总治疗效果的非参数估计。

IF 1.6 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag053

Jessica Gronsbell, Zachary R McCaw, Isabelle-Emmanuella Nogues, Xiangshan Kong, Tianxi Cai, Lu Tian, L J Wei

{"title":"Nonparametric estimation of the total treatment effect with multiple outcomes in the presence of terminal events.","authors":"Jessica Gronsbell, Zachary R McCaw, Isabelle-Emmanuella Nogues, Xiangshan Kong, Tianxi Cai, Lu Tian, L J Wei","doi":"10.1093/biomtc/ujag053","DOIUrl":"10.1093/biomtc/ujag053","url":null,"abstract":"As standards of care advance, patients are living longer and once-fatal diseases are becoming manageable. Clinical trials increasingly focus on reducing disease burden, which can be quantified by the timing and occurrence of multiple non-fatal clinical events. Most existing methods for the analysis of multiple event-time data require stringent modeling assumptions that can be difficult to verify empirically, leading to treatment efficacy estimates that forego interpretability when the underlying assumptions are not met. Moreover, many methods do not appropriately account for informative terminal events, such as premature treatment discontinuation or death, which prevent the occurrence of subsequent events. To address these limitations, we derive and validate estimation and inference procedures for the area under the mean cumulative function (AUMCF), an extension of the restricted mean survival time to the multiple event-time setting. The AUMCF is clinically interpretable, properly accounts for terminal competing risks, and can be estimated nonparametrically. To enable covariate adjustment, we also develop an augmentation estimator that provides efficiency at least equaling, and often exceeding, the unadjusted estimator. The utility and interpretability of the AUMCF are illustrated with extensive simulation studies and through an analysis of multiple heart-failure-related endpoints using data from the Beta-Blocker Evaluation of Survival Trial. Our open-source R package MCC makes conducting AUMCF analyses straightforward and accessible.","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":"147980365","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