Journal of Biopharmaceutical Statistics最新文献

{"title":"Linear regression models for analyzing the covariate-adjusted Youden index and associated cut-off points in three diagnostic groups.","authors":"Asieh Maghami-Mehr, Hamzeh Torabi, Hossein Nadeb, Yichuan Zhao","doi":"10.1080/10543406.2025.2558141","DOIUrl":"10.1080/10543406.2025.2558141","url":null,"abstract":"<p><p>In medical diagnostic studies involving a transitional intermediate stage of disease progression, the Youden index offers a valuable summary measure for evaluating test accuracy across three diagnostic groups. However, ignoring covariate effects may lead to misleading assessments. To address this, we incorporate covariate information using linear regression models with normally distributed errors, enabling maximum likelihood estimation of the covariate-adjusted Youden index and its corresponding optimal cut-off points. We further develop several types of confidence intervals for these parameters, including generalized confidence intervals, Bayesian credible intervals, and bootstrap-based intervals. The finite-sample performance of the proposed estimators and interval procedures is evaluated via Monte Carlo simulations. Finally, we apply our methods to a diabetic dataset to illustrate their practical utility.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1075-1099"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076619","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":"Non-constant mean relative potency for antibody-dependent cellular cytotoxicity assays.","authors":"Paul Faya, Tianhui Zhang, Wendy Walton, Steven Novick","doi":"10.1080/10543406.2024.2403435","DOIUrl":"10.1080/10543406.2024.2403435","url":null,"abstract":"<p><p>Bioassays are regulated, analytical methods used to ensure proper activity (potency) of biological products at release and during long-term storage. Potency is commonly reported on a relative basis by comparing and calibrating a concentration-response curve from the test material to that of a reference standard material. The relative potency approach depends on an assumption that the two concentration-response curves exhibit similar (equivalent) shapes, except for a potency shift. In certain circumstances, however, biological factors preclude the similarity assumption, and the traditional approach becomes unworkable. The antibody-mediated cytotoxicity assay is one example where the similarity assumption does not always hold. Other examples also arise in the fields of toxicology and pharmacology. In this work, we present a non-constant mean relative potency approach which averages the relative potency across a common range of the concentration-response curves. The proposed method captures the changing nature of the relative potency into a summary statistic that can be reported for batch calibration and quality control purposes. We provide inferential methods for this statistic and summarize the results of a simulation comparing these methods across a number of non-constant relative potency scenarios and assay conditions.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"753-764"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301313","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":"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":"10.1080/10543406.2025.2450330","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"826-841"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","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}

{"title":"Identifying the clinical relative importance of each correlated outcome variables in multivariate approach: an exploration using ACCORD trial data.","authors":"Akash Mishra, N Sreekumaran Nair, K T Harichandrakumar, Binu Vs, Santhosh Satheesh","doi":"10.1080/10543406.2025.2489360","DOIUrl":"10.1080/10543406.2025.2489360","url":null,"abstract":"<p><p>In scenarios involving correlated endpoints, multivariate methods offer increased robustness for comparisons. However, understanding the individual contribution of each variable toward multivariate hypothesis rejection remains underexplored. Usually, this question is sidelined, and separate univariate analyses are performed. This paper addresses this gap by demonstrating the relative importance and contribution of variables toward the rejection of multivariate hypotheses, comparing it against a univariate approach using clinical trial data. Using the ACCORD lipid trial dataset, which includes lipid measurements of triglycerides (TG), low-density lipoprotein (LDL), and high-density lipoprotein (HDL), we employed Hotelling's T² multivariate statistic for two-group comparisons. We showcased the significance and relative importance of contributions through standardized discriminant function coefficients and partial F tests. Additionally, we investigated the impact of varying correlation levels on the significance of each variable's contribution in multivariate versus univariate approaches. Our results revealed significant lipid differences in a multivariate context at the 12th and 36th months. Across both follow-ups, TG exhibited the highest relative importance and contribution, followed by HDL and LDL. Notably, in the 36th month, the univariate approach rendered LDL's contribution insignificant for group separation, contrasting with the significant contribution identified in the multivariate approach. Furthermore, the significance likelihood of variable contributions in group separation within the multivariate approach increased with rising correlation levels. The simulation technique and the power analysis was also adopted to characterize the features of the proposed method. Our approach enables the evaluation of the relative importance and significance of each variable's contribution within the multivariate framework. This methodology holds promise for enhancing the interpretation of clinical trial analysis outcomes, particularly when dealing with multiple correlated endpoints.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"933-944"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029456","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":"Power priors and type I error control: constrained borrowing of external control data.","authors":"Se Yoon Lee","doi":"10.1080/10543406.2025.2575940","DOIUrl":"10.1080/10543406.2025.2575940","url":null,"abstract":"<p><p>Recently, hybrid designs have garnered significant attention in the healthcare industry due to their potential to improve statistical power and trial efficiency by augmenting randomized controlled trial data with external controls. The power prior methodology provides a versatile framework for constructing and analyzing data from hybrid designs. However, the use of external control data poses a risk of introducing bias, particularly in the presence of prior-data conflict, which can distort treatment effect estimates. Such biases may lead to erroneous conclusions, including the approval of ineffective treatments or the rejection of beneficial ones. To address these concerns, it is essential to borrow an appropriate amount of external data to maintain the type I error rate at an acceptable level, typically determined during trial planning in discussion with regulatory authorities. In this article, we present a novel power prior method to incorporate historical control data while safeguarding against inflation of the type I error rate beyond the maximally allowable nominal level. Through comprehensive simulation studies and an illustrative example, we demonstrate the practical advantages of our approach. The results illustrate that our method provides trial sponsors with a scientifically rigorous strategy for leveraging external control data in constructing efficient and reliable hybrid designs.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1161-1183"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145508085","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":"Statistical considerations for using tolerance interval to set product specification for normally distributed attribute.","authors":"Chang Chen, Yi Tsong, Xutong Zhao, Meiyu Shen","doi":"10.1080/10543406.2025.2473612","DOIUrl":"10.1080/10543406.2025.2473612","url":null,"abstract":"<p><p>Conventionally, the product quality specification and control chart limits are determined as the mean plus and minus 3 sample standard deviations with the assumption that the quality data is normally distributed. These limits correspond to an interval centered at the mean, covering approximately 97.3% of the population. The estimate of such an interval is called the <math><mi>β</mi></math>-content tolerance interval. It has been proposed to use a two one-sided <math><mi>β</mi></math>-content tolerance interval approach for determining drug product quality specifications. For a given confidence level, <math><mn>1</mn><mo>-</mo><mi>α</mi><mo>,</mo></math> and a coverage percentage <i>p</i>, the <math><mi>β</mi></math>-content tolerance interval is not precise when the sample size is small. For the derivation of a precise <math><mi>β</mi></math>-content tolerance interval, Faulkenberry and Daly proposed a \"goodness\" criterion for sample size determination. In order to avoid overestimating the <math><mi>β</mi></math>-content tolerance interval when <i>p</i> is large, we propose to define the precision requirement as the probability of the tolerance interval covering more than <math><mrow><mfrac><mrow><mfenced><mrow><mn>1</mn><mo>+</mo><mi>p</mi></mrow></mfenced></mrow><mn>2</mn></mfrac></mrow></math> is restricted to a pre-specified significance level <math><msup><mi>α</mi><mo>'</mo></msup></math>. Quality specification studies are often not planned with proper sample sizes. To obtain precise <math><mi>β</mi></math>-content tolerance intervals for quality specification studies, the proper coverage <i>p</i> satisfying the \"goodness\" criterion and the minimum sample sizes were also determined with the pre-specified significance level <math><msup><mi>α</mi><mo>'</mo></msup></math>. With this approach, one may properly set the product specificationwhile avoiding over-specifying the quality limits.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"927-932"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617676","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":"Comparison of parametric and hybrid methods for estimating mean survival time in clinical study.","authors":"Yuki Nakagawa, Takashi Sozu","doi":"10.1080/10543406.2025.2557539","DOIUrl":"10.1080/10543406.2025.2557539","url":null,"abstract":"<p><p>The mean survival time (MST) is usually estimated as the area under the curve of the estimated survival function obtained using the Kaplan-Meier method. However, when the maximum observed survival time is censored, the MST cannot be estimated because the survival function does not reach zero. In such cases, parametric and hybrid methods are used to estimate the MST. The parametric method assumes a probability distribution throughout the entire time and has been evaluated in several studies. The hybrid method combines two approaches: it first applies the Kaplan-Meier method up to a specified time point and then extrapolates the survival curve beyond this point using a parametric distribution. Evaluation of the performance of the hybrid method is limited to a few data-generating mechanisms and analysis models. This study evaluated the performance of the parametric and hybrid methods through numerical experiments, assuming nine probability distributions for the data-generating mechanism and 16 analysis models. The bias and root mean square error of the generalized gamma model and the Royston-Parmar models with the log(-log) link function tended to be smaller than those of the other analysis models, even when the assumed probability distribution of the analysis model was inconsistent with that of the data-generating mechanism when the sample size is relatively large. Overall, the performances of the parametric and hybrid methods were comparable across all the data-generating mechanisms.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1065-1074"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024796","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 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":"10.1080/10543406.2025.2456176","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"871-882"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","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}

{"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":"10.1080/10543406.2025.2450321","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"812-825"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","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}

{"title":"Bayesian design of clinical trials with multiple time-to-event outcomes subject to functional cure.","authors":"Seoyoon Cho, Matthew A Psioda, Joseph G Ibrahim","doi":"10.1080/10543406.2025.2451152","DOIUrl":"10.1080/10543406.2025.2451152","url":null,"abstract":"<p><p>With the continuous advancement of medical treatments, there is an increasing demand for clinical trial designs and analyses using cure rate models to accommodate a plateau in the survival curve. This is especially pertinent in oncology, where high proportions of patients, such as those with melanoma, lung cancer, and endometrial cancer, exhibit usual life spans post-cancer detection. A Bayesian clinical trial design methodology for multivariate time-to-event outcomes with cured fractions is developed. This approach employs a copula to jointly model the multivariate time-to-event outcomes. We propose a model that uses a Gaussian copula on the population survival function, irrespective of cure status. The minimum sample size required to achieve high statistical power while maintaining reasonable control over the type I error rate from a Bayesian perspective is identified using point-mass sampling priors. The methodology is demonstrated in simulation studies inspired by an endometrial cancer trial.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"842-853"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048838","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}