Journal of Biopharmaceutical Statistics最新文献_第9页

A Bayesian framework for safety signal detection from medical device data. 医疗设备数据安全信号检测的贝叶斯框架。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-03-10 DOI: 10.1080/10543406.2025.2464595

Jianjin Xu, Adrijo Chakraborty, Archie Sachdeva, Ram Tiwari

{"title":"A Bayesian framework for safety signal detection from medical device data.","authors":"Jianjin Xu, Adrijo Chakraborty, Archie Sachdeva, Ram Tiwari","doi":"10.1080/10543406.2025.2464595","DOIUrl":"10.1080/10543406.2025.2464595","url":null,"abstract":"Safety evaluation is important during both the pre-market clinical trials and post-market surveillance. In either a pre-market or post-market setting wherein the safety of a device is compared to that of a control device, it is desirable to identify any difference in the safety between two devices as expeditiously as possible. Here, we introduce the Bayesian hierarchical framework for the safety assessment in two-arm clinical trials, with signal detection accomplished by evaluating the effect size of each adverse event (AE) measured by odds ratio or relative risk. The framework starts with a standard hierarchical Bayesian model with a parametric distribution as a common prior for the effect sizes of all AEs. Then, it is extended with a non-parametric prior, Dirichlet Process Prior, to allow for more flexibility. After that, to account for the rare events in some trials, it is further extended with the option of additional zero-inflated parameters and calculation of regularized effect size. Extra incorporation of exposure-time information is available under each framework. The performance of the proposed technique, along with its extensions, is studied by simulation. The application of the proposed Bayesian framework is demonstrated by data from a two-device clinical trial, the newer left ventricular assist device (LVAD) and the existing LVAD. The Bayesian analysis result is then compared to a traditional frequentist technique. Through both simulation and application, the proposed Bayesian technique is shown to be robust to the selection of priors of the variance component, and has comparative and under some scenarios even better performance than the frequentist technique. Overall, the developed Bayesian framework is a feasible alternative to the frequentist method for safety evaluation of medical device clinical trials.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"883-899"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586908","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

Quality principles in Phase I dose escalation design. 一期剂量递增设计的质量原则。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-06-13 DOI: 10.1080/10543406.2025.2512988

Jonathan M Siegel

引用次数: 0

Estimation of treatment effects in early phase randomized clinical trials involving multiple data sources for external control. 涉及多个外部控制数据源的早期随机临床试验的治疗效果评估。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-06-13 DOI: 10.1080/10543406.2025.2512984

Heiko Götte, Marietta Kirchner, Meinhard Kieser

{"title":"Estimation of treatment effects in early phase randomized clinical trials involving multiple data sources for external control.","authors":"Heiko Götte, Marietta Kirchner, Meinhard Kieser","doi":"10.1080/10543406.2025.2512984","DOIUrl":"10.1080/10543406.2025.2512984","url":null,"abstract":"Augmented randomized clinical trials are a valuable design option for early phase clinical trials. The addition of external controls could, on the one hand, increase precision in treatment effect estimates or reduce the number of required control patients for a randomized trial but may, on the other hand, introduce bias. We build on previous work on augmented trials with one external control data source in time-to-event settings and extend it to multiple control data sources. In a comprehensive simulation study, we evaluate existing and novel analysis options mainly based on Bayesian hierarchical models as well as propensity score analysis. Different sources of bias are investigated including population (observable and unobservable confounders), data collection (assessment schedule, real-world vs. clinical trial data), and time trend as well as different types of data like individual patient data (with or without baseline covariates) or summary data. Our simulation study provides recommendations in terms of choice of estimation method as well as choice of data sources. Explicit incorporation of the above-mentioned sources of bias in a simulation study is relevant as the magnitude of deviation from the ideal setting has a significant impact on all investigated estimation methods.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"945-963"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144295387","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 bias correction method for hazard ratio estimation and its inference in a multiple-arm clinical trial. 多组临床试验中风险比估计及其推断的偏倚校正方法。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-09-09 DOI: 10.1080/10543406.2025.2547590

Liji Shen, Ziwen Wei, Xuan Deng

{"title":"A bias correction method for hazard ratio estimation and its inference in a multiple-arm clinical trial.","authors":"Liji Shen, Ziwen Wei, Xuan Deng","doi":"10.1080/10543406.2025.2547590","DOIUrl":"10.1080/10543406.2025.2547590","url":null,"abstract":"A randomized clinical trial with multiple experimental groups and one common control group is often used to speed up development to select the best experimental regimen or to increase the chance of success of clinical trials. Most of the time, multiple dose levels of an experimental drug or multiple combinations of one experimental drug with other drugs comprise multiple experimental groups. Because the experimental drug appears in multiple comparisons with a shared control group, multiple testing adjustments to control the family-wise type I error rate are needed. We extend the stepwise over-correction (SOC) method that is applied to a multi-arm trial with a response rate as its endpoint to a multi-arm trial where time to event is the primary endpoint and confidence interval of the hazard ratio determines the statistical significance. We provide the formula of the bias of the maximum treatment effect estimate toward the true treatment effect between the selected experimental group and the shared control group. We aim to use the bias-corrected estimate for the inference of treatment effects in multi-arm trials on the full alpha level and demonstrate a completely new type of reject region. This approach does not require us to split alpha level among the multiple comparisons or to specify the test order ahead of time. The type I error control and the power enhancement of the proposed approach are both held.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1035-1050"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024748","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

Statistical approaches to evaluate the positive control drug using the hERG assay. 使用hERG测定法评价阳性对照药物的统计方法。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-11-19 DOI: 10.1080/10543406.2025.2575939

Yu-Ting Weng, Dalong Huang

引用次数: 0

A phase II, seamless single-arm to two-arm Bayesian design for a time-to-event endpoint. 第二阶段，无缝的单臂到双臂贝叶斯设计，用于时间到事件端点。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-07-30 DOI: 10.1080/10543406.2026.2703579

Yun Qing, Ruitao Lin

{"title":"A phase II, seamless single-arm to two-arm Bayesian design for a time-to-event endpoint.","authors":"Yun Qing, Ruitao Lin","doi":"10.1080/10543406.2026.2703579","DOIUrl":"https://doi.org/10.1080/10543406.2026.2703579","url":null,"abstract":"Phase II oncology trials often face challenges such as patient recruitment difficulties and limited resources, leading to the use of single-arm designs without concurrent controls. This study introduces a novel two-stage Bayesian design bridging single-arm and randomized two-arm trials for time-to-event endpoints. The design incorporates an initial experimental treatment stage with futility monitoring, followed by a randomized second stage comparing experimental and control arms, contingent on first-stage progression. The methodology enables comprehensive efficacy analysis by combining data from both stages. The design parameters are optimized to minimize the expected sample size under the null hypothesis of no treatment effect. Extensive simulation studies demonstrate consistently lower expected sample sizes than conventional two-arm trials utilizing log-rank tests. Furthermore, the design achieves reduced maximum sample sizes relative to the sequential implementation of separate single-arm and two-arm trials across various scenarios. An illustrative application in extensive-stage small cell lung cancer demonstrates practical implementation considerations. As a resource-efficient approach, this design streamlines phase II evaluations and expedites clinical decision-making for evaluating experimental oncology treatments.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.3,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148632891","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

Balancing sample size and accuracy of dose selection in phase 1 oncology trials - designs tiered by cohort size and maximum number of patients treated per dose. 平衡1期肿瘤试验的样本量和剂量选择的准确性——按队列大小和每次剂量治疗的最大患者人数分层设计。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-07-26 DOI: 10.1080/10543406.2026.2699850

Ping Gao, Xun Zhang, Weidong Zhang, Meng Li, Chunming Zhang

{"title":"Balancing sample size and accuracy of dose selection in phase 1 oncology trials - designs tiered by cohort size and maximum number of patients treated per dose.","authors":"Ping Gao, Xun Zhang, Weidong Zhang, Meng Li, Chunming Zhang","doi":"10.1080/10543406.2026.2699850","DOIUrl":"https://doi.org/10.1080/10543406.2026.2699850","url":null,"abstract":"The objective of phase 1 oncology dose‑escalation trials is to identify the dose with a toxicity rate that matches or is the closest to the target dose‑limiting toxicity (DLT). There are mainly two categories of design methods for phase 1 trials: the rule-based methods and model-based/assisted methods. All literature has focused on the designs, mostly on how the various model-based/assisted methods are better than the 3 + 3 design, which is the most used rule-based design. The accuracy of dose selection is perhaps the most important operating characteristics (OC). Our investigation shows that the accuracy is more consequentially affected by the maximum number of patients treated per dose (MPTPD) than by the type of designs. Simulation examples show that the model-based BOIN and CRM design has lower accuracy for dose selection than the 3 + 3 design with the same cohort size and the same MPTPD. Larger MPTPD is associated with larger sample sizes. Simulations show that selection accuracy improves with increasing MPTPD (hence sample sizes). Our findings suggest that the design of phase 1 trials should include consideration of the balance between MPTPD, sample size and expectation of selection accuracy. Rule-based designs that are tiered by cohort sizes and MPTPD are proposed. These designs perform similarly with the BOIN and CRM designs with identical cohort sizes and MPTPD, but can be used without any software.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-25"},"PeriodicalIF":1.3,"publicationDate":"2026-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148594691","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

Why the minimum effective dose is unidentifiable - and how a DOR→OOD standard delivers label-ready dosing in oncology. 为什么最小有效剂量无法确定-以及DOR→OOD标准如何在肿瘤学中提供标签就绪剂量。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-07-24 DOI: 10.1080/10543406.2026.2699849

Haitao Pan, Naitee Ting

{"title":"Why the minimum effective dose is unidentifiable - and how a DOR→OOD standard delivers label-ready dosing in oncology.","authors":"Haitao Pan, Naitee Ting","doi":"10.1080/10543406.2026.2699849","DOIUrl":"https://doi.org/10.1080/10543406.2026.2699849","url":null,"abstract":"Oncology dose optimization has moved beyond the maximum tolerated dose paradigm, yet many programs still implicitly target a threshold-style minimum effective dose (MED). In serious cancers, deliberately sub-therapeutic comparators are rarely ethical or approvable, so any sharp MED threshold is typically a fragile and only partially identifiable target. We propose a statistical and operational framework that reframes dose-finding as region-based optimization. First, we define a constraint-based Dose Optimization Region (DOR) where clinically meaningful benefit (potentially multi-endpoint and PD-informed) is credible, unacceptable toxicity is bounded, exposure targets are attainable within a prespecified window, and implementation is feasible. Second, within the DOR, we identify an Operational Optimal Dose (OOD) - a label-ready dosing strategy that integrates dose, schedule, exposure targets, and adjustmentrules - using comparative evidence across binary and time-to-eventendpoints, exposure - response (PK/PD) analyses, and time-to-event methods that account for delayed effects where follow-up allows. This approach turns regulatory evidence domains into region-definingconstraints and shifts the target from a single-point estimate to a strategy-level deliverable. It is intended to sit on top of conventional dose-finding designs and PK/PD modeling as a region-defining and reporting standard, rather than to replace existing methods. We illustrate its use with small-samplevisualizations based on pairwise superiority probabilities across in-region arms and with a real-world-inspired example to show how DOR→OOD can summarize the totality of evidence in practice. The framework provides statisticians and clinicians with practical design patterns and reporting checklists, aligning with contemporary regulatory expectations and supporting dose optimization in oncology, with potential adaptation to other therapeutic areas.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-18"},"PeriodicalIF":1.3,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148581264","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

Augmented match weighted estimators: new methods for estimating average treatment effects under extreme propensity scores. 增强匹配加权估计器：在极端倾向得分下估计平均治疗效果的新方法。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-07-22 DOI: 10.1080/10543406.2026.2699851

Tanchumin Xu, Yunshu Zhang, Shu Yang

{"title":"Augmented match weighted estimators: new methods for estimating average treatment effects under extreme propensity scores.","authors":"Tanchumin Xu, Yunshu Zhang, Shu Yang","doi":"10.1080/10543406.2026.2699851","DOIUrl":"https://doi.org/10.1080/10543406.2026.2699851","url":null,"abstract":"Propensity score matching (PSM) and augmented inverse propensity weighting (AIPW) are used in observational studies to estimate causal effects. The AIPW estimator is doubly robust and locally efficient but can be unstable when the propensity scores are close to zero or one. PSM circumvents the instability of propensity score weighting but it hinges on the correctness of the propensity score model and cannot attain the semiparametric efficiency bound. The fixed number of matches, <math><mi>K</mi></math>, renders PSM nonsmooth and thus invalidates standard bootstrap inference. This article presents novel augmented match weighted (AMW) estimators that combine the advantages of matching and weighting estimators. AMW adheres to the form of AIPW for its double robustness and local efficiency but it mitigates the instability. We replace inverse propensity weights with matching weights resulting from PSM with unfixed <math><mi>K</mi></math>. Meanwhile, we propose a new cross-validation procedure to select <math><mi>K</mi></math> that minimizes the mean squared error anchored around an unbiased estimator of the causal estimand. We derive the limiting distribution showing AMW estimators enjoy the double robustness property and can achieve the semiparametric efficiency bound if both nuisance models are correct. As a byproduct of unfixed <math><mi>K</mi></math> which smooths AMW estimators, nonparametric bootstrap can be adopted for variance estimation. Furthermore, simulation and real-data applications support that the AMW estimators are stable and their variances can be obtained by nonparametric bootstrap. Our method is particularly recommended when the propensity score distribution is extreme. In such settings, the AMW estimator is more efficient and robust than PSM, more stable than AIPW, and is accompanied by a valid variance estimation procedure.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-20"},"PeriodicalIF":1.3,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148551405","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

Industrialization of Bayesian decision-making for proof-of-commercial-concept study designs. 商业化概念研究设计验证贝叶斯决策的产业化。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-07-21 DOI: 10.1080/10543406.2026.2697812

Fan Wu, Pascal Minini, Gang Han, Bingzhi Zhang, Meehyung Cho, Xun Chen, Eugene Andre Houseman

引用次数: 0