Journal of Biopharmaceutical Statistics最新文献

{"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":"https://doi.org/10.1080/10543406.2025.2512990","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-22"},"PeriodicalIF":1.2,"publicationDate":"2025-06-09","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}

{"title":"Bayesian meta-analysis for rare outcomes.","authors":"Ohud Alqasem, Haydar Demirhan, Anil Dolgun","doi":"10.1080/10543406.2025.2512205","DOIUrl":"https://doi.org/10.1080/10543406.2025.2512205","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-23"},"PeriodicalIF":1.2,"publicationDate":"2025-06-09","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}

{"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":"https://doi.org/10.1080/10543406.2025.2514632","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-14"},"PeriodicalIF":1.2,"publicationDate":"2025-06-09","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}

{"title":"Bayesian optimal Phase II survival trial design with event-driven approach.","authors":"Yuntong Li, Jianrong Wu","doi":"10.1080/10543406.2025.2512202","DOIUrl":"https://doi.org/10.1080/10543406.2025.2512202","url":null,"abstract":"<p><p>Bayesian design incorporates prior knowledge and external information, making it an attractive option during the early phase of a clinical trial. A number of Bayesian optimal designs have been proposed to make go/no-go decisions based on posterior probabilities while also having desired frequentist operating characteristics. However, existing Bayesian designs either are not appropriate for time-to-event endpoints or rely on an exponential distribution assumption on the data. In this paper, we propose a Bayesian optimal Phase II event-driven design (BOP2e) that allows for futility and/or superiority stopping for single-arm trials with a time-to-event endpoint. The proposed BOP2e design is optimal in minimizing the expected sample size under null hypothesis while also controlling the frequentist Type I error. Simulation studies are performed to explore the operating characteristics of the proposed BOP2e designs. A user-friendly Shiny application is available to help implement the proposed designs.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-13"},"PeriodicalIF":1.2,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250952","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}

{"title":"BPED: A Bayesian basket design for pediatric trials with external data.","authors":"Yimei Li, Ying Yuan","doi":"10.1080/10543406.2025.2512203","DOIUrl":"10.1080/10543406.2025.2512203","url":null,"abstract":"<p><p>The basket trial is a novel type of trial that evaluates one treatment in multiple indications (such as cancer types) simultaneously. One challenge of applying the basket trial design to pediatric studies is limited accrual, resulting in low statistical power. To address this issue, we propose a Bayesian <u>b</u>asket design for <u>p</u>ediatric trials with <u>e</u>xternal <u>d</u>ata (BPED) that performs dual-information borrowing to improve the design efficiency: borrow information from the external data to the pediatric trial, and borrow information between the cancer types within the pediatric trial. BPED also accommodates potential heterogeneous treatment effects across cancer types by allowing each cancer type belonging to the sensitive or insensitive latent subgroups. The design adaptively updates the members of the subgroups based on the accumulated pediatric and external data to make go/no-go decisions for each cancer type. The simulation study shows that, compared to some existing designs, BPED yields higher power to detect the treatment effect for sensitive cancer types and maintains a desirable type I error rate for insensitive cancer types.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-20"},"PeriodicalIF":1.2,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12354168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of EWOC principle in BLRM design for phase 1 oncology trials.","authors":"Xiaohan Guo, Sean Kent, Arnab Maity, Wei Zhong","doi":"10.1080/10543406.2024.2333530","DOIUrl":"10.1080/10543406.2024.2333530","url":null,"abstract":"<p><p>Bayesian logistic regression model (BLRM) is widely used to guide dose escalation decisions in phase 1 oncology trials. An important feature of BLRM design is the appealing safety performance due to its escalation with overdose control (EWOC). However, some recent literature indicates that BLRM with EWOC may have a relatively low probability to find the maximum tolerated dose (MTD) compared to some other dose escalation designs. This work discusses this design problem and proposes a practical solution to improve the performance of BLRM design. Specifically, we suggest increasing the EWOC cutoff from routine value 0.25 to a value between 0.3 and 0.4, which will increase the chance of finding the correct MTD with minimal compromise to overdosing risk. Our comparative simulation studies indicate that BLRM with an increased EWOC cutoff has comparable operating characteristics on the correct MTD selection and over-toxicity control as other dose escalation designs (BOIN, mTPI, keyboard, etc.). Moreover, we compare the methodology and operating characteristics of BLRM designs with various decision rules that allow more flexible overdosing control. A case study of dose escalation in a recent phase 1 oncology trial is provided to show how BLRM with optimal EWOC cutoff operates well in practice.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"407-423"},"PeriodicalIF":1.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140337725","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}

{"title":"Association of the medication protocols and longitudinal change of COVID-19 symptoms: a hospital-based mixed-statistical methods study.","authors":"Zahra Rezaei Ghahroodi, Samaneh Eftekhari Mahabadi, Alireza Esberizi, Ramin Sami, Marjan Mansourian","doi":"10.1080/10543406.2024.2333527","DOIUrl":"10.1080/10543406.2024.2333527","url":null,"abstract":"<p><p>The objective of this study was to identify the relationship between hospitalization treatment strategies leading to change in symptoms during 12-week follow-up among hospitalized patients during the COVID-19 outbreak. In this article, data from a prospective cohort study on COVID-19 patients admitted to Khorshid Hospital, Isfahan, Iran, from February 2020 to February 2021, were analyzed and reported. Patient characteristics, including socio-demographics, comorbidities, signs and symptoms, and treatments during hospitalization, were investigated. Also, to investigate the treatment effects adjusted by other confounding factors that lead to symptom change during follow-up, the binary classification trees, generalized linear mixed model, machine learning, and joint generalized estimating equation methods were applied. This research scrutinized the effects of various medications on COVID-19 patients in a prospective hospital-based cohort study, and found that heparin, methylprednisolone, ceftriaxone, and hydroxychloroquine were the most frequently prescribed medications. The results indicate that of patients under 65 years of age, 76% had a cough at the time of admission, while of patients with Cr levels of 1.1 or more, 80% had not lost weight at the time of admission. The results of fitted models showed that, during the follow-up, women are more likely to have shortness of breath (OR = 1.25; P-value: 0.039), fatigue (OR = 1.31; P-value: 0.013) and cough (OR = 1.29; P-value: 0.019) compared to men. Additionally, patients with symptoms of chest pain, fatigue and decreased appetite during admission are at a higher risk of experiencing fatigue during follow-up. Each day increase in the duration of ceftriaxone multiplies the odds of shortness of breath by 1.15 (P-value: 0.012). With each passing week, the odds of losing weight increase by 1.41 (P-value: 0.038), while the odds of shortness of breath and cough decrease by 0.84 (P-value: 0.005) and 0.56 (P-value: 0.000), respectively. In addition, each day increase in the duration of meropenem or methylprednisolone decreased the odds of weight loss at follow-up by 0.88 (P-value: 0.026) and 0.91 (P-value: 0.023), respectively (among those who took these medications). Identified prognostic factors can help clinicians and policymakers adapt management strategies for patients in any pandemic like COVID-19, which ultimately leads to better hospital decision-making and improved patient quality of life outcomes.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"386-406"},"PeriodicalIF":1.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140186335","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}

{"title":"Modelling alternately recurring events using subject specific hazard estimation approach.","authors":"Moumita Chatterjee, Sugata Sen Roy, Bhaswati Ganguli","doi":"10.1080/10543406.2024.2317772","DOIUrl":"10.1080/10543406.2024.2317772","url":null,"abstract":"<p><p>The motivation for this paper is to account for subject specific variations in a Cox proportional hazard model for alternating recurrent events. This is done through two sets of frailty components, whose marginal distributions are bound together by a copula function. The likelihood function involves unobservable variables, which requires the use of the EM algorithm. This leads to intractable integrals, which after some approximations, are solved using computationally intensive techniques. The results are applied to a real-life data. A simulation study is also carried out to check for consistency.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"321-342"},"PeriodicalIF":1.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140023352","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}

{"title":"Meta-analysis application to hERG safety evaluation in clinical trials.","authors":"Xutong Zhao, Jing Sun, Dalong Huang","doi":"10.1080/10543406.2024.2365972","DOIUrl":"10.1080/10543406.2024.2365972","url":null,"abstract":"<p><p>One objective of meta-analysis, which synthesizes evidence across multiple studies, is to assess the consistency and investigate the heterogeneity across studies. In this project, we performed a meta-analysis on moxifloxacin (positive control in QT assessment studies) data to characterize the exposure-response relationship and determine the safety margin associated with 10-msec QTc effects for moxifloxacin based on 26 thorough QT studies submitted to the FDA. Multiple meta-analysis methods were used (including two novel methods) to evaluate the exposure-response relationship and estimate the critical concentration and the corresponding confidence interval of moxifloxacin associated with a 10-msec QTc effect based on the concentration-QTc models. These meta-analysis methods (aggregate data vs. individual participant data; fixed effect vs. random effect) were compared in terms of their precision and robustness. With the selected meta-analysis method, we demonstrated the homogeneity and heterogeneity of the moxifloxacin concentration-QTc relationship in studies. We also estimated the critical concentration of moxifloxacin that can be used to calculate the hERG safety margin of this drug.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"343-355"},"PeriodicalIF":1.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141321958","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}

{"title":"Bayesian spatial cluster signal learning with application to adverse event (AE).","authors":"Hou-Cheng Yang, Guanyu Hu","doi":"10.1080/10543406.2024.2325148","DOIUrl":"10.1080/10543406.2024.2325148","url":null,"abstract":"<p><p>There is growing interest in understanding geographic patterns of medical device-related adverse events (AEs). A spatial scan method combined with the likelihood ratio test (LRT) for spatial-cluster signal detection over the geographical region is universally used. The spatial scan method used a moving window to scan the entire study region and collected some candidate sub-regions from which the spatial-cluster signal(s) will be found. However, it has some challenges, especially in computation. First, the computational cost increased when the number of sub-regions increased. Second, the computational cost may increase if a large spatial-cluster pattern is present and a flexible-shaped window is used. To reduce the computational cost, we propose a Bayesian nonparametric method that combines the ideas of Markov random field (MRF) to leverage geographical information to find potential signal clusters. Then, the LRT is applied for the detection of spatial cluster signals. The proposed method provides an ability to capture both locally spatially contiguous clusters and globally discontiguous clusters, and is manifested to be effective and tractable using hypothetical Left Ventricular Assist Device (LVAD) data as an illustration.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"373-385"},"PeriodicalIF":1.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140186336","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}