Journal of Biopharmaceutical Statistics最新文献_第3页

List of Reviewers for Journal of Biopharmaceutical Statistics, Volume 36. 《生物制药统计杂志》第36卷审稿人名单。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-01 Epub Date: 2026-10-27 DOI: 10.1080/10543406.2026.2707028

引用次数: 0

Correction. 修正。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-01 Epub Date: 2025-09-04 DOI: 10.1080/10543406.2025.2557043

引用次数: 0

Evaluating bias in the anchor method for the minimal clinically important difference: a simulation approach. 评估锚定法中最小临床重要差异的偏差：模拟方法。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-01 Epub Date: 2025-08-19 DOI: 10.1080/10543406.2025.2547586

Greg Hather, Polyna Khudyakov

引用次数: 0

TITE-STEIN: Time-to-event simple toxicity and efficacy interval design to accelerate phase I/II trials. TITE-STEIN：时间到事件的简单毒性和疗效间隔设计，加速I/II期试验。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-01 Epub Date: 2025-12-04 DOI: 10.1080/10543406.2025.2592615

Hao Sun, Jieqi Tu, Revathi Ananthakrishnan, Eunhee Kim

{"title":"TITE-STEIN: Time-to-event simple toxicity and efficacy interval design to accelerate phase I/II trials.","authors":"Hao Sun, Jieqi Tu, Revathi Ananthakrishnan, Eunhee Kim","doi":"10.1080/10543406.2025.2592615","DOIUrl":"10.1080/10543406.2025.2592615","url":null,"abstract":"Oncology dose-finding trials are shifting from identifying the maximum-tolerated dose (MTD) to determining the optimal biological dose (OBD), driven by the need for efficient methods that consider both toxicity and efficacy. This is particularly important for novel therapies, such as immunotherapies and molecularly targeted therapies, which often exhibit non-monotonic dose-efficacy curves. The Simple Toxicity and Efficacy Interval (STEIN) design has demonstrated strong performance in accommodating diverse dose-efficacy patterns and incorporating both toxicity and efficacy outcomes to select the OBD. However, the rapid accrual of patients and the often-delayed onset of toxicity and/or efficacy pose challenges to timely adaptive-dose decisions. To address these challenges, we propose TITE-STEIN, a model-assisted design that incorporates time-to-event (TITE) outcomes for toxicity and/or efficacy, by extending STEIN. In this article, we demonstrate that TITE-STEIN significantly shortens the trial duration compared to STEIN. Furthermore, by integrating an OBD verification procedure during OBD selection, TITE-STEIN effectively mitigates the risk of exposing patients to inadmissible doses when the OBD does not exist. Extensive simulations demonstrate that TITE-STEIN outperforms existing TITE designs, including TITE-BONI12, TITE-BOIN-ET, LO-TC, and Joint TITE-CRM, by selecting the OBD more accurately, allocating more patients to it, and improving overdose control.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1669-1686"},"PeriodicalIF":1.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145670769","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

Simultaneous comparison of the predictive values of two binary diagnostic tests in the presence of categorical covariates. 在存在分类协变量的情况下，同时比较两种二元诊断试验的预测值。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-01 Epub Date: 2025-08-26 DOI: 10.1080/10543406.2025.2547589

José A Roldán-Nofuentes, Saad B Regad

{"title":"Simultaneous comparison of the predictive values of two binary diagnostic tests in the presence of categorical covariates.","authors":"José A Roldán-Nofuentes, Saad B Regad","doi":"10.1080/10543406.2025.2547589","DOIUrl":"https://doi.org/10.1080/10543406.2025.2547589","url":null,"abstract":"Comparison of predictive values of diagnostic tests is a topic of interest in Medical Statistics, and has been the subject of different studies. In clinical practice, it is frequent to observe categorical covariates when comparing diagnostic tests. In this framework, a global hypothesis test is proposed to simultaneously compare the predictive values of two diagnostic tests when in all of the individuals categorical covariates are observed. This hypothesis test is solved through regression models and also by weighted least squares method for the analysis of categorical data. Simulation experiments were carried out to study the asymptotic behavior of these methods when a binary covariate is observed and when a covariate with three categories is observed, and these were compared to the behavior of the global test when the covariate is ignored. In general, the method based on regression models has shown to have better asymptotic behavior than the other methods. Furthermore, we studied the application of the method based on the regression models when no covariate is observed, for which the individuals in the sample are randomly assigned to a binary dummy random variable. Simulation experiments carried out showed that this method has greater power than the method without the covariate. The results were applied to two examples.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":"36 6","pages":"1463-1492"},"PeriodicalIF":1.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149277121","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

Comparing diagnostic tests and biomarkers based on benefit-risk under tree orderings of disease classes. 比较诊断测试和生物标志物在疾病类别树排序下的收益-风险。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-01 Epub Date: 2025-06-09 DOI: 10.1080/10543406.2025.2512990

Jing Kersey, Hani Samawi, Mario Keko, Marwan Alsharman

{"title":"Comparing diagnostic tests and biomarkers based on benefit-risk under tree orderings of disease classes.","authors":"Jing Kersey, Hani Samawi, Mario Keko, Marwan Alsharman","doi":"10.1080/10543406.2025.2512990","DOIUrl":"10.1080/10543406.2025.2512990","url":null,"abstract":"The assessment and comparison of biomarkers and diagnostic tests using a benefit-risk framework are essential for evaluating both the accuracy of tests and the clinical implications of diagnostic errors. Traditional measures, such as sensitivity and specificity, often do not fully capture the complexities involved in evaluating tests for diseases with multiple subtypes. Many diseases, such as Alzheimer's, are characterized by multiple stages or classes, and in some cases, like cancers, these classes do not follow a specific order, necessitating a more nuanced approach.This paper extends the net benefit approach, traditionally applied to binary diagnostic tests, to address clinical conditions with multiple unordered subtypes using a tree or umbrella ordering framework. We introduce a novel methodology that expands the diagnostic yield table to account for multisubtypes, allowing for a more comprehensive evaluation of diagnostic tests. This approach incorporates decision-making processes based on net benefit, offering additional insights into the criteria for ruling in or ruling out clinical conditions and highlighting the potential adverse consequences of unnecessary diagnostic workups.Through numerical examples, simulations, and real-world data applications, we demonstrate the flexibility and potential advantages of our proposed framework in handling complex disease scenarios. By accommodating multiple subtypes and providing a structured approach to evaluating the net benefit of diagnostic tests, this methodology offers valuable insights for clinical decision-making. The framework's ability to incorporate the specific characteristics of disease subtypes makes it particularly useful in settings where traditional binary classification measures may fall short. This approach could significantly enhance the accuracy of diagnostic evaluations and support more tailored interventions in clinical practice, thereby improving patient outcomes.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1365-1386"},"PeriodicalIF":1.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250953","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

Approximate Bayesian estimation of time to clinical benefit using Frequentist approaches: an application to an intensive blood pressure control trial. 近似贝叶斯估计时间的临床效益使用频率方法：应用于强化血压控制试验。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-01 Epub Date: 2025-06-10 DOI: 10.1080/10543406.2025.2512985

Fang Shao, Guoshuai Shi, Zhe Lv, Duolao Wang, Mingyan Gong, Tao Chen, Chao Li

{"title":"Approximate Bayesian estimation of time to clinical benefit using Frequentist approaches: an application to an intensive blood pressure control trial.","authors":"Fang Shao, Guoshuai Shi, Zhe Lv, Duolao Wang, Mingyan Gong, Tao Chen, Chao Li","doi":"10.1080/10543406.2025.2512985","DOIUrl":"10.1080/10543406.2025.2512985","url":null,"abstract":"Background: Time to Benefit (TTB) is a critical metric in clinical practice, reflecting the duration required to achieve therapeutic goals post-treatment. Traditionally, TTB estimation has relied on Bayesian Weibull regression, which, despite its merits, can be computationally intensive. To address this, we propose and evaluate Frequentist methods as efficient alternatives to approximate Bayesian TTB estimation.Methods: We evaluated three Frequentist methods, parametric delta, Monte Carlo, and nonparametric bootstrap, for TTB estimation, comparing their performance with the Bayesian approach.Results: Extensive simulations demonstrated that the proposed Frequentist methods outperformed the Bayesian method in efficiency. Real-world data applications further validated these findings, with the Monte Carlo (MC) method exhibiting significantly faster computational speed compared to the nonparametric bootstrap, while the Bayesian method was the least efficient.Conclusions: The proposed Frequentist methods offer significant advantages to approximate the Bayesian approach for TTB estimation, particularly in efficiency and practicality. The Monte Carlo method, with its median point estimate and percentile confidence intervals, is the recommended choice for its balance of efficacy and expedience.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1336-1346"},"PeriodicalIF":1.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259384","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

Informative event rate in study determination, study design, and interim analysis monitoring with non-proportional hazards. 研究确定、研究设计和非比例危险的中期分析监测中的信息事件率。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-01 Epub Date: 2025-06-09 DOI: 10.1080/10543406.2025.2514632

Shufang Liu, Kentaro Takeda, Alan Rong

{"title":"Informative event rate in study determination, study design, and interim analysis monitoring with non-proportional hazards.","authors":"Shufang Liu, Kentaro Takeda, Alan Rong","doi":"10.1080/10543406.2025.2514632","DOIUrl":"10.1080/10543406.2025.2514632","url":null,"abstract":"A cancer trial with an immunotherapy or antibody drug conjugate often has a certain delay/crossing time before the drug to take effect. In this paper, we propose to call the events that occur during and after the delay/crossing time as non-informative events and informative events, respectively. We propose to call the rate of number of informative events divided by total number of events as informative event rate (<math><mi>γ</mi></math>), though this rate has been used in the literature. We show three innovative usages of <math><mi>γ</mi></math> under non-proportional hazards (NPH) setting: (1) based on <math><mi>γ</mi></math>, the minimum average hazard ratio (<math><mi>aH</mi><mrow><msub><mi>R</mi><mrow><mi>min</mi></mrow></msub></mrow></math>) can be calculated analytically and used to determine whether trials are worth being conducted for a test drug to get a meaningful average hazard ratio (aHR) at the planning stage; (2) based on a series of <math><mi>γ</mi></math>, aHR and power can be calculated and a proper design can be selected for a trial with a targeted aHR at the design stage; (3) based on <math><mi>γ</mi></math>, a better interim analysis timing to ensure a certain probability for early efficacy/futility stopping can be determined during the course of a study. aHR and the probability for early efficacy/futility stopping under different enrollment scenarios in a simulation were verified by calculation. We propose the concepts of the informative event rate (<math><mi>γ</mi></math>), <math><mi>aH</mi><mrow><msub><mi>R</mi><mrow><mi>min</mi></mrow></msub></mrow></math>, and a targeted aHR and use them in study determination, study design, and interim analysis monitoring under an NPH setting with a delay/crossing time.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1387-1400"},"PeriodicalIF":1.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250954","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

M-DODII: Bayesian dose optimization design for randomized phase II study with multiple indications. M-DODII：多适应症随机II期研究的贝叶斯剂量优化设计

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-01 Epub Date: 2025-11-27 DOI: 10.1080/10543406.2025.2589731

Sasha Amdur Kravets, Ziji Yu, Rachael Liu, Jianchang Lin

{"title":"M-DODII: Bayesian dose optimization design for randomized phase II study with multiple indications.","authors":"Sasha Amdur Kravets, Ziji Yu, Rachael Liu, Jianchang Lin","doi":"10.1080/10543406.2025.2589731","DOIUrl":"10.1080/10543406.2025.2589731","url":null,"abstract":"The landscape of oncology drug development is transitioning from traditional cytotoxic chemotherapy drugs to novel agents, such as molecularly targeted therapies (MTA) or immunotherapies. Conventional dose optimization methods based on chemotherapy that assume a monotone dose-response relationship might not be ideal for the development of these novel therapies. Recognizing these limitations, the US FDA has introduced Project Optimus, an initiative aimed to reform the current paradigm of dose optimization. In addition to dose optimization, another critical objective for early phase proof-of-concept clinical trials is indication selection. However, there are limited methodologies that can address dose optimization and indication selection simultaneously. In this paper, we propose a Bayesian Dose Optimization Design for Randomized Phase II trials with Multiple Indications (M-DODII) that integrates Bayesian continuous monitoring and Bayesian pick-the-winner approach, utilizing efficacy and toxicity endpoints to inform dose selection for multiple indications simultaneously. Through simulation studies, we demonstrate that M-DODII has favorable operating characteristics with controlled selection error. Compared to other adaptive designs, M-DODII shows a lower probability of choosing a suboptimal dose, a higher probability of selecting the optimal dose, and reduced total sample size.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1634-1647"},"PeriodicalIF":1.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145642993","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

Bayesian meta-analysis for rare outcomes. 罕见结果的贝叶斯荟萃分析。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-01 Epub Date: 2025-06-09 DOI: 10.1080/10543406.2025.2512205

Ohud Alqasem, Haydar Demirhan, Anil Dolgun

{"title":"Bayesian meta-analysis for rare outcomes.","authors":"Ohud Alqasem, Haydar Demirhan, Anil Dolgun","doi":"10.1080/10543406.2025.2512205","DOIUrl":"10.1080/10543406.2025.2512205","url":null,"abstract":"Binary meta-analysis studies with rare outcomes frequently include zero or a small number of observations in study groups, creating a sparsity issue with the data. The corrections applied to eliminate the impact of the zero cell counts introduce a bias to the meta-analysis results and potentially distort the inferences about the treatment effect and heterogeneity among the studies. The boundaries of interval estimates become highly biased due to the sparsity of the data. This study proposes two Bayesian random-effects meta-analysis models based on the beta-binomial model with an arc-sine-square-root transformation. The performance of the models in estimating the treatment effect and the in-between study variance is assessed with an extensive Monte Carlo simulation study, and a frequently referred meta-analysis dataset is revisited. The models provide accurate estimates of treatment effect and heterogeneity parameters without a continuity correction. They provide well-calibrated, narrow interval estimates with sufficient coverage of true treatment effect and in-between study variance. They are robust against zero cell counts, very low event probabilities, and unbalanced, skewed data distributions. Recommendations are given for the practical use of the proposed models, and the required model scripts are provided to implement the models using R software.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1313-1335"},"PeriodicalIF":1.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250951","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