Biometrics最新文献_第3页

Precision generalized phase I-II designs. 精密广义I-II期设计。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf043

Saijun Zhao, Peter F Thall, Ying Yuan, Juhee Lee, Pavlos Msaouel, Yong Zang

{"title":"Precision generalized phase I-II designs.","authors":"Saijun Zhao, Peter F Thall, Ying Yuan, Juhee Lee, Pavlos Msaouel, Yong Zang","doi":"10.1093/biomtc/ujaf043","DOIUrl":"10.1093/biomtc/ujaf043","url":null,"abstract":"A new family of precision Bayesian dose optimization designs, PGen I-II, based on early efficacy, early toxicity, and long-term time to treatment failure is proposed. A PGen I-II design refines a Gen I-II design by accounting for patient heterogeneity characterized by subgroups that may be defined by prognostic levels, disease subtypes, or biomarker categories. The design makes subgroup-specific decisions, which may be to drop an unacceptably toxic or inefficacious dose, randomize patients among acceptable doses, or identify a best dose in terms of treatment success defined in terms of time to failure over long-term follow-up. A piecewise exponential distribution for failure time is assumed, including subgroup-specific effects of dose, response, and toxicity. Latent variables are used to adaptively cluster subgroups found to have similar dose-outcome distributions, with the model simplified to borrow strength between subgroups in the same cluster. Guidelines and user-friendly computer software for implementing the design are provided. A simulation study is reported that shows the PGen I-II design is superior to similarly structured designs that either assume patient homogeneity or conduct separate trials within subgroups.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12288667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144706184","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

Negative binomial mixed effects location-scale models for intensive longitudinal count-type physical activity data provided by wearable devices. 可穿戴设备提供的密集纵向计数型体力活动数据的负二项混合效应位置尺度模型。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf099

Qianheng Ma, Genevieve F Dunton, Donald Hedeker

{"title":"Negative binomial mixed effects location-scale models for intensive longitudinal count-type physical activity data provided by wearable devices.","authors":"Qianheng Ma, Genevieve F Dunton, Donald Hedeker","doi":"10.1093/biomtc/ujaf099","DOIUrl":"10.1093/biomtc/ujaf099","url":null,"abstract":"In recent years, the use of wearable devices, for example, accelerometers, have become increasingly prevalent. Wearable devices enable more accurate real-time tracking of a subject's physical activity (PA) level, such as steps, number of activity bouts, or time in moderate-to-vigorous intensity PA (MVPA), which are important general health markers and can often be represented as counts. These intensive within-subject count data provided by wearable devices, for example, minutes in MVPA summarized per hour across days and even months, allow the possibility for modeling not only the mean PA level, but also the dispersion level for each subject. Especially in the context of daily PA, subjects' dispersion levels are potentially informative in reflecting their exercise patterns: some subjects might exhibit consistent PA across time and can be considered \"less dispersed\" subjects; while others might have a large amount of PA at a particular time point, while being sedentary for most of the day, and can be considered \"more dispersed\" subjects. Thus, we propose a negative binomial mixed effects location-scale model to model these intensive longitudinal PA counts and to account for the heterogeneity in both the mean and dispersion level across subjects. Further, to handle the issue of inflated numbers of zeros in the PA data, we also propose a hurdle/zero-inflated version which additionally includes the modeling of the probability of having $>$0 PA levels.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12312402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144752244","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 analysis of interval-censored failure time data with change points and a cured subgroup. 具有变化点和修复子群的间隔截尾失效时间数据的回归分析。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf100

Yichen Lou, Mingyue Du, Xinyuan Song

引用次数: 0

Correction to: Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain functional connectome outcomes via network-based matrix-on-vector regression. 修正：通过基于网络的矩阵向量回归评估高通量结构神经成像预测因子对全脑功能连接组结果的影响。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf111

引用次数: 0

Sparse 2-stage Bayesian meta-analysis for individualized treatments. 个性化治疗的稀疏2阶段贝叶斯荟萃分析。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf082

Junwei Shen, Erica E M Moodie, Shirin Golchi

{"title":"Sparse 2-stage Bayesian meta-analysis for individualized treatments.","authors":"Junwei Shen, Erica E M Moodie, Shirin Golchi","doi":"10.1093/biomtc/ujaf082","DOIUrl":"10.1093/biomtc/ujaf082","url":null,"abstract":"Individualized treatment rules tailor treatments to patients based on clinical, demographic, and other characteristics. Estimation of individualized treatment rules requires the identification of individuals who benefit most from the particular treatments and thus the detection of variability in treatment effects. To develop an effective individualized treatment rule, data from multisite studies may be required due to the low power provided by smaller datasets for detecting the often small treatment-covariate interactions. However, sharing of individual-level data is sometimes constrained. Furthermore, sparsity may arise in 2 senses: different data sites may recruit from different populations, making it infeasible to estimate identical models or all parameters of interest at all sites, and the number of non-zero parameters in the model for the treatment rule may be small. To address these issues, we adopt a 2-stage Bayesian meta-analysis approach to estimate individualized treatment rules which optimize expected patient outcomes using multisite data without disclosing individual-level data beyond the sites. Simulation results demonstrate that our approach can provide consistent estimates of the parameters which fully characterize the optimal individualized treatment rule. We estimate the optimal Warfarin dose strategy using data from the International Warfarin Pharmacogenetics Consortium, where data sparsity and small treatment-covariate interaction effects pose additional statistical challenges.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12288668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144706198","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

Covariance-on-covariance regression. Covariance-on-covariance回归。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf097

Yi Zhao, Yize Zhao

引用次数: 0

Bayesian inference for copy number intra-tumoral heterogeneity from single-cell RNA-sequencing data. 单细胞rna测序数据对拷贝数肿瘤内异质性的贝叶斯推断。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf115

PuXue Qiao, Chun Fung Kwok, Guoqi Qian, Davis J McCarthy

{"title":"Bayesian inference for copy number intra-tumoral heterogeneity from single-cell RNA-sequencing data.","authors":"PuXue Qiao, Chun Fung Kwok, Guoqi Qian, Davis J McCarthy","doi":"10.1093/biomtc/ujaf115","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf115","url":null,"abstract":"Copy number alterations (CNA) are important drivers and markers of clonal structures within tumors. Understanding these structures at single-cell resolution is crucial to advancing cancer treatments. The objective is to cluster single cells into clones and identify CNA events in each clone. Early attempts often sacrifice the intrinsic link between cell clustering and clonal CNA detection for simplicity and rely heavily on human input for critical parameters such as the number of clones. Here, we develop a Bayesian model to utilize single-cell RNA sequencing (scRNA-seq) data for automatic analysis of intra-tumoral clonal structure concerning CNAs, without reliance on prior knowledge. The model clusters cells into sub-tumoral clones, identifies the number of clones, and simultaneously infers the clonal CNA profiles. It synergistically incorporates input from gene expression and germline single-nucleotide polymorphisms. A Gibbs sampling algorithm has been implemented and is available as an R package Chloris. We demonstrate that our new method compares strongly against existing software tools in terms of both cell clustering and CNA profile identification accuracy. Application to human metastatic melanoma and anaplastic thyroid tumor data demonstrates accurate clustering of tumor and non-tumor cells and reveals clonal CNA profiles that highlight functional gene expression differences between clones from the same tumor.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144940997","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

Revisiting optimal allocations for binary responses: insights from considering type-I error rate control. 重新审视二元响应的最优分配：从考虑i型错误率控制的见解。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf114

Lukas Pin, Sofía S Villar, William F Rosenberger

{"title":"Revisiting optimal allocations for binary responses: insights from considering type-I error rate control.","authors":"Lukas Pin, Sofía S Villar, William F Rosenberger","doi":"10.1093/biomtc/ujaf114","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf114","url":null,"abstract":"This work revisits optimal response-adaptive designs from a type-I error rate perspective, highlighting when and how much these allocations exacerbate type-I error rate inflation-an issue previously undocumented. We explore a range of approaches from the literature that can be applied to reduce type-I error rate inflation. However, we found that all of these approaches fail to give a robust solution to the problem. To address this, we derive 2 optimal allocation proportions, incorporating the more robust score test (instead of the Wald test) with finite sample estimators (instead of the unknown true values) in the formulation of the optimization problem. One proportion optimizes statistical power, and the other minimizes the total number of failures in a trial while maintaining a fixed variance level. Through simulations based on an early phase and a confirmatory trial, we provide crucial practical insight into how these new optimal proportion designs can offer substantial patient outcomes advantages while controlling type-I error rate. While we focused on binary outcomes, the framework offers valuable insights that naturally extend to other outcome types, multi-armed trials, and alternative measures of interest.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144941151","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

Multiple tests for restricted mean time lost with competing risks data. 具有竞争风险数据的有限平均损失时间的多个测试。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf086

Merle Munko, Dennis Dobler, Marc Ditzhaus

{"title":"Multiple tests for restricted mean time lost with competing risks data.","authors":"Merle Munko, Dennis Dobler, Marc Ditzhaus","doi":"10.1093/biomtc/ujaf086","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf086","url":null,"abstract":"Easy-to-interpret effect estimands are highly desirable in survival analysis. In the competing risks framework, one good candidate is the restricted mean time lost (RMTL). It is defined as the area under the cumulative incidence function up to a prespecified time point and, thus, it summarizes the cumulative incidence function into a meaningful estimand. While existing RMTL-based tests are limited to 2-sample comparisons and mostly to 2 event types, we aim to develop general contrast tests for factorial designs and an arbitrary number of event types based on a Wald-type test statistic. Furthermore, we avoid the often-made, rather restrictive continuity assumption on the event time distribution. This allows for ties in the data, which often occur in practical applications, for example, when event times are measured in whole days. In addition, we develop more reliable tests for RMTL comparisons that are based on a permutation approach to improve the small sample performance. In a second step, multiple tests for RMTL comparisons are developed to test several null hypotheses simultaneously. Here, we incorporate the asymptotically exact dependence structure between the local test statistics to gain more power. The small sample performance of the proposed testing procedures is analyzed in simulations and finally illustrated by analyzing a real-data example about leukemia patients who underwent bone marrow transplantation.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144741073","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

Semiparametric joint modeling to estimate the treatment effect on a longitudinal surrogate with application to chronic kidney disease trials. 半参数联合建模用于估计慢性肾脏疾病试验中纵向替代物的治疗效果。

IF 1.7 4区数学

Biometrics Pub Date : 2025-07-03 DOI: 10.1093/biomtc/ujaf104

Xuan Wang, Jie Zhou, Layla Parast, Tom Greene

{"title":"Semiparametric joint modeling to estimate the treatment effect on a longitudinal surrogate with application to chronic kidney disease trials.","authors":"Xuan Wang, Jie Zhou, Layla Parast, Tom Greene","doi":"10.1093/biomtc/ujaf104","DOIUrl":"10.1093/biomtc/ujaf104","url":null,"abstract":"In clinical trials where long follow-up is required to measure the primary outcome of interest, there is substantial interest in using an accepted surrogate outcome that can be measured earlier in time or with less cost to estimate a treatment effect. For example, in clinical trials of chronic kidney disease, the effect of a treatment is often demonstrated on a longitudinal surrogate, the change of the longitudinal outcome (glomerular filtration rate, GFR) per year or GFR slope. However, estimating the effect of a treatment on the GFR slope is complicated by the fact that GFR measurement can be terminated by the occurrence of a terminal event, such as death or kidney failure. Thus, to estimate this effect, one must consider both the longitudinal GFR trajectory and the terminal event process. In this paper, we build a semiparametric framework to jointly model the longitudinal outcome and the terminal event, where the model for the longitudinal outcome is semiparametric, the relationship between the longitudinal outcome and the terminal event is nonparametric, and the terminal event is modeled via a semiparametric Cox model. The proposed semiparametric joint model is flexible and can be easily extended to include a nonlinear trajectory of the longitudinal outcome. An estimating equation based method is proposed to estimate the treatment effect on the longitudinal surrogate outcome (eg, GFR slope). Theoretical properties of the proposed estimators are derived, and finite sample performance is evaluated through simulation studies. We illustrate the proposed method using data from the Reduction of Endpoints in NIDDM with the Angiotensin II Antagonist Losartan (RENAAL) trial to examine the effect of Losartan on GFR slope.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12320702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144783416","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