Journal of Biopharmaceutical Statistics最新文献_第3页

Correction.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-02-05 DOI: 10.1080/10543406.2025.2461926

引用次数: 0

Overview of real-world applications of federated learning with NVIDIA FLARE.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-02-02 DOI: 10.1080/10543406.2025.2456174

Holger R Roth, Ziyue Xu, Chester Chen, Daguang Xu, Prerna Dogra, Mona Flores, Yan Cheng, Andrew Feng

引用次数: 0

Bayesian design of clinical trials with multiple time-to-event outcomes subject to functional cure.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-26 DOI: 10.1080/10543406.2025.2451152

Seoyoon Cho, Matthew A Psioda, Joseph G Ibrahim

引用次数: 0

Bayesian method for comparing F1 scores in the absence of a gold standard.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-26 DOI: 10.1080/10543406.2025.2450319

Jun Tamura, Yusuke Saigusa, Junichi Fujita, Kouji Yamamoto

引用次数: 0

Bayesian efficient safety monitoring: a simple and well-performing framework to continuous safety monitoring of adverse events in randomized clinical trials.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-23 DOI: 10.1080/10543406.2025.2456176

Liangcai Zhang, Ming Chen, Vladimir Dragalin, Bin Eddy Jia, Cunyi Wang, Leixin Xia, Chaohui Yuan, Fei Chen

{"title":"Bayesian efficient safety monitoring: a simple and well-performing framework to continuous safety monitoring of adverse events in randomized clinical trials.","authors":"Liangcai Zhang, Ming Chen, Vladimir Dragalin, Bin Eddy Jia, Cunyi Wang, Leixin Xia, Chaohui Yuan, Fei Chen","doi":"10.1080/10543406.2025.2456176","DOIUrl":"https://doi.org/10.1080/10543406.2025.2456176","url":null,"abstract":"During randomized controlled trials, it is critical to remain vigilant in safety monitoring. A common approach is to present information over time, such as frequency tables and graphs, when analyzing adverse events. Nevertheless, there is still a need for developing statistical methods for analyzing safety data of a dynamic nature. The process is typically challenging due to small sample sizes, a lack of observational data sources, difficulties in false-positive control, and the necessity for early detection of serious adverse events. In this article, we propose a simple and effective framework called Bayesian Efficient sAfety Monitoring (BEAM) to analyze evidence aggregation of potentially serious adverse events that may arise during the trial, as well as a timeline for when concrete evidence for safety concerns of unlikely outcomes becomes available. BEAM can be easily tabulated and visualized before the trial starts, making evaluations transparent and easy to use in practice, while maintaining flexibility when the underlying adverse event rate varies. Simulation studies have shown that BEAM supports continuous monitoring, can incorporate external information, and demonstrates good operating characteristics across various scenarios. In most practical situations, it has a reasonable likelihood of detecting elevated risks and identifying safety signals early on when safety concerns arise regarding the investigational drug.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-12"},"PeriodicalIF":1.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","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 joint bent-cable model for longitudinal measurements and survival time with heterogeneous random-effects distributions. 具有非均匀随机效应分布的纵向测量和存活时间的贝叶斯节点弯索模型。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-20 DOI: 10.1080/10543406.2025.2450321

Oludare Ariyo, Kehinde Olobatuyi, Taban Baghfalaki

{"title":"A Bayesian joint bent-cable model for longitudinal measurements and survival time with heterogeneous random-effects distributions.","authors":"Oludare Ariyo, Kehinde Olobatuyi, Taban Baghfalaki","doi":"10.1080/10543406.2025.2450321","DOIUrl":"https://doi.org/10.1080/10543406.2025.2450321","url":null,"abstract":"Biomarkers are measured repeatedly in clinical studies until a pre-defined endpoint, such as death from certain causes, is reached. Such repeated measurements may present a dynamic process for understanding when to expect the study's endpoint. Joint modelling is often employed to handle such a model. Typically, shared random effects are assumed to be common to both the longitudinal component and the study's endpoint. These shared random effects usually assume homogeneous and follow a normal distribution. However, identifying homogeneous subgroups is important when the underlying population is heterogeneous. This issue has received little attention in the literature, particularly for multi-phase longitudinal responses. In this paper, we propose a joint modelling approach for longitudinal and survival models using a bent-cable mixed model for longitudinal measurements and a Weibull distribution for the survival component. We also incorporate a finite mixture of normal distribution assumptions to account for the unobserved heterogeneity in the shared random effects model. A Bayesian MCMC is developed for parameter estimation and inferences. The proposed method is evaluated using simulation studies and the Tehran Lipid and Glucose Study dataset.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-14"},"PeriodicalIF":1.2,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Missing data in the eligibility criteria of synthetic controls from real-world data. 真实世界数据合成控制的合格标准中缺少数据。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-19 DOI: 10.1080/10543406.2025.2450330

Liang Li, Thomas Jemielita, Cong Chen

{"title":"Missing data in the eligibility criteria of synthetic controls from real-world data.","authors":"Liang Li, Thomas Jemielita, Cong Chen","doi":"10.1080/10543406.2025.2450330","DOIUrl":"https://doi.org/10.1080/10543406.2025.2450330","url":null,"abstract":"Randomized clinical trials (RCTs) can benefit from using Real-World Data (RWD) as a supplementary data source to enhance their analysis. An Augmented RCT combines randomized treatment and control groups with synthetic controls derived from RWD. This way, the trial can achieve less prospective enrollment, higher statistical power, and lower costs. However, to ensure scientific validity, the synthetic controls must satisfy the same eligibility criteria as the trial participants. A major challenge is that RWD often have missing data that hinder the eligibility assessment. This problem has been overlooked in the literature and this paper offers statistical solutions to address it. We use multiple imputations to handle missing data in the variables involved in the eligibility criteria. We also propose a generalized propensity score weighting procedure to adjust for the life expectancy requirement, a common eligibility criterion in oncology clinical trials but usually unavailable in RWD. Since the life expectancy is an unmeasured confounder, we discuss the statistical assumptions required to correct its bias. We validate the proposed solutions through simulation studies and the analysis of an Augmented RCT in oncology.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A composite semiparametric homogeneity test for the distributions of multigroup interval-bounded longitudinal data. 多组区间有界纵向数据分布的复合半参数齐性检验。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-02 Epub Date: 2023-11-15 DOI: 10.1080/10543406.2023.2275769

Zhanfeng Wang, Wenmei Li, Hao Ding, Dongsheng Tu

{"title":"A composite semiparametric homogeneity test for the distributions of multigroup interval-bounded longitudinal data.","authors":"Zhanfeng Wang, Wenmei Li, Hao Ding, Dongsheng Tu","doi":"10.1080/10543406.2023.2275769","DOIUrl":"10.1080/10543406.2023.2275769","url":null,"abstract":"Motivated by comparing the distribution of longitudinal quality of life (QoL) data among different treatment groups from a cancer clinical trial, we propose a semiparametric test statistic for the homogeneity of the distributions of multigroup longitudinal measurements, which are bounded in a closed interval with excess observations taking the boundary values. Our procedure is based on a three-component mixed density ratio model and a composite empirical likelihood for the longitudinal data taking values inside the interval. A nonparametric bootstrap method is applied to calculate the p-value of the proposed test. Simulation studies are conducted to evaluate the proposed procedure, which show that the proposed test is effective in controlling type I errors and more powerful than the procedure which ignores the values on the boundaries. It is also robust to the model mispecification than the parametric test. The proposed procedure is also applied to compare the distributions of the scores of Physical Function subscale and Global Heath Status between the patients randomized to two treatment groups in a cancer clinical trial.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"58-69"},"PeriodicalIF":1.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134650477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adjusted win ratio using the inverse probability of treatment weighting. 使用治疗加权的逆概率调整获胜率。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-02 Epub Date: 2023-11-10 DOI: 10.1080/10543406.2023.2275759

Duolao Wang, Sirui Zheng, Ying Cui, Nengjie He, Tao Chen, Bo Huang

{"title":"Adjusted win ratio using the inverse probability of treatment weighting.","authors":"Duolao Wang, Sirui Zheng, Ying Cui, Nengjie He, Tao Chen, Bo Huang","doi":"10.1080/10543406.2023.2275759","DOIUrl":"10.1080/10543406.2023.2275759","url":null,"abstract":"The win ratio method has been increasingly applied in the design and analysis of clinical trials. However, the win ratio method is a univariate approach that does not allow for adjusting for baseline imbalances in covariates, although a stratified win ratio can be calculated when the number of strata is small. This paper proposes an adjusted win ratio to control for such imbalances by inverse probability of treatment weighting (IPTW) method. We derive the adjusted win ratio with its variance and suggest three IPTW adjustments: IPTW-average treatment effect (IPTW-ATE), stabilized IPTW-ATE (SIPTW-ATE) and IPTW-average treatment effect in the treated (IPTW-ATT). The proposed adjusted methods are applied to analyse a composite outcome in the CHARM trial. The statistical properties of the methods are assessed through simulations. Results show that adjusted win ratio methods can correct the win ratio for covariate imbalances at baseline. Simulation results show that the three proposed adjusted win ratios have similar power to detect the treatment difference and have slightly lower power than the corresponding adjusted Cox models when the assumption of proportional hazards holds true but have consistently higher power than adjusted Cox models when the proportional hazard assumption is violated.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"21-36"},"PeriodicalIF":1.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven monitoring for phase II clinical trial designs based on percentile event time test. 基于百分位事件时间测试的数据驱动型 II 期临床试验设计监测。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-02 Epub Date: 2023-12-22 DOI: 10.1080/10543406.2023.2292209

Yeonhee Park, Zhanpeng Xu

引用次数: 0