Journal of Biopharmaceutical Statistics最新文献

Using principal progression rate to quantify and compare disease progression in comparative studies. 在比较研究中使用主要进展率来量化和比较疾病进展。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-05-08 DOI: 10.1080/10543406.2026.2667336

Changyu Shen, Menglan Pang, Ling Zhu, Tian Lu

{"title":"Using principal progression rate to quantify and compare disease progression in comparative studies.","authors":"Changyu Shen, Menglan Pang, Ling Zhu, Tian Lu","doi":"10.1080/10543406.2026.2667336","DOIUrl":"https://doi.org/10.1080/10543406.2026.2667336","url":null,"abstract":"In comparative studies of progressive diseases, such as randomized controlled trials (RCTs), mean Change From Baseline (CFB) of a continuous outcome at a pre-specified follow-up time in the target population is a standard estimand for summarizing overall disease progression. While easy to interpret, the mean CFB may not efficiently capture important features of the mean outcome trajectory relevant to the treatment effect evaluation. Additionally, the estimation of the mean CFB does not use all longitudinal data. To address these limitations, we propose a class of estimands called Principal Progression Rate (PPR), which is a weighted average of local or instantaneous slope of the mean outcome trajectory during the follow-up. The flexibility of the weight function allows the PPR to cover a broad class of intuitive estimands, including the mean CFB, the slope of ordinary least-square fit to the trajectory, and the area under the curve. We showed that properly chosen PPRs can enhance statistical power over the mean CFB by amplifying treatment effect signal and/or improving estimation precision. We evaluated different versions of PPRs and their estimators through numerical studies, and applied them to the dataset of the EMERGE clinical trial, demonstrating the advantage of using alternative PPR over the mean CFB.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-18"},"PeriodicalIF":1.2,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846256","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

On stepwise MTPs between Holm's step-down MTP and the max-p-value MTP for co-primary endpoints: A further extension for free. 关于Holm的降压MTP和共同主端点的最大p值MTP之间的逐步MTP：免费的进一步扩展。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-05-07 DOI: 10.1080/10543406.2026.2664713

Olivier J M Guilbaud

{"title":"On stepwise MTPs between Holm's step-down MTP and the max-p-value MTP for co-primary endpoints: A further extension for free.","authors":"Olivier J M Guilbaud","doi":"10.1080/10543406.2026.2664713","DOIUrl":"https://doi.org/10.1080/10543406.2026.2664713","url":null,"abstract":"This article extends a recently discussed p-value-based multiple testing procedure (MTP) that can be used if study-success requires that at least <math><mi>k</mi></math> out of <math><mi>m</mi></math> primary/important hypotheses become rejected. With <math><mi>k</mi></math> strictly between one and <math><mi>m</mi></math>, this previous MTP is between the two practically important MTPs in the title. It has a certain initial gatekeeping step/test, followed by <math><mi>m</mi></math> steps where primary hypotheses are rejected in a step-down manner. The extension discussed in this article consists in replacing the initial step/test with a Fixed-Sequence MTP for a family of increasing hypotheses about the number of false primary hypotheses. The last step of this gatekeeping family equals the initial step/test of the previous MTP. Thus, even if this last step does not open the gate to the step-down part, it is possible to make inferences about the number of false primary hypotheses through preceding fixed-sequence steps. This extension is for free in that the extended MTP inherits all nice properties of the previous MTP. Notably, it: (a) is as generally valid as Holm's MTP; (b) can reject <math><mi>k</mi></math> or more primary hypotheses even if Holm's MTP stops before its <math><mi>k</mi></math>th step; and (c) has optimal critical constants in that if at least one is increased, the general strong familywise error rate control is lost. A simple generally valid confidence lower bound for the number of false primary hypotheses is discussed. An R-function is provided that calculates all the adjusted p-values needed.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-13"},"PeriodicalIF":1.2,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846333","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 rank-based approach to randomized controlled trials with multiple co-primary endpoints of different scales. 一种基于秩的随机对照试验方法，具有多个不同尺度的共同主要终点。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-05-04 DOI: 10.1080/10543406.2026.2663458

Guangyong Zou

{"title":"A rank-based approach to randomized controlled trials with multiple co-primary endpoints of different scales.","authors":"Guangyong Zou","doi":"10.1080/10543406.2026.2663458","DOIUrl":"https://doi.org/10.1080/10543406.2026.2663458","url":null,"abstract":"Randomized controlled trials with co-primary endpoints refer to trials that are designed to evaluate if the intervention is superior to the control on each endpoint. Data analysis and sample size estimation can be complicated when the endpoints are of different scales. In contrast to trials with multiple primary endpoints, where multiplicity is a concern, multiple co-primary endpoints could cause substantial power/efficiency reduction. We propose a rank-based approach to data analysis and sample size estimation for such studies. For each endpoint, we quantify the treatment effect using the win probability (WinP) that a subject in the treatment group has a better score than (or a win over) a subject in the control group. Inference for the endpoint-specific WinPs is carried out by using multivariate linear mixed models with a unstructured variance-covariance matrix for win fractions, which are derived from (mid)ranks and shown to be asymptotically uncorrelated. We focus on confidence intervals (CIs) for WinPs and testing null hypothesis based on whether all lower limits of the CIs are above 0.50. Sample size formulae are derived with the focus on determining the sample size required to guarantee with a pre-specified assurance probability that the lower limit of CI for each endpoint is above 0.50. Results from a simulation study based on a published trial on ulcerative colitis suggest that our approach performed well in terms of CI coverage and assurance probability. The results also show that baseline adjustments can result in a gain in efficiency, but dichotomizing data can decrease efficiency substantially.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-22"},"PeriodicalIF":1.2,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147823519","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 systematic analysis of the inherent dose uncertainty in autologous CAR T-cell products. 自体CAR - t细胞产品固有剂量不确定性的系统分析。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-05-04 DOI: 10.1080/10543406.2026.2664711

Sangwook Choi, Kedar Dave

{"title":"A systematic analysis of the inherent dose uncertainty in autologous CAR T-cell products.","authors":"Sangwook Choi, Kedar Dave","doi":"10.1080/10543406.2026.2664711","DOIUrl":"https://doi.org/10.1080/10543406.2026.2664711","url":null,"abstract":"Chimeric antigen receptor (CAR) T-cell therapy represents a form of human gene therapy where T-cells are genetically engineered to recognize specific target antigen(s) in the human body. The target dose level of autologous CAR T-cell products is established through clinical trials to ensure the administration of an appropriate amount of viable CAR T-cells to patients. However, the actual dose calculated based on the measured drug product strength inherently includes a degree of uncertainty. This study aims to conduct a systematic analysis to quantify the inherent uncertainty arising from product dose calculation and administration. Key sources of dose uncertainty include those emanating from rounding of dose volume calculations, analytical methods to measure product strength, physical limitations of the syringes used for drug administration and in-use stability during administration. The dose uncertainty is a critical indicator of the accuracy of the dose estimate and is quantified as the total error around a target dose, which is decomposed into systematic error (bias) and random error (variability). We propose a systematic approach for quantifying the bias and variability of each uncertainty source and for integrating them to calculate the total error and determine the dose uncertainty range around the target dose. Two case studies involving lisocabtagene maraleucel (liso-cel), a commercially approved CAR T-cell product, illustrate the proposed dose uncertainty calculation. This statistical uncertainty modeling methodology is useful to understand the distribution of actual CAR T-cell doses and thus to support dose range determination and guide efforts to reduce process and analytical variability.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-11"},"PeriodicalIF":1.2,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846263","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

Efficient estimation of the cox model when incorporating the subgroup restricted mean survival time. 当纳入亚组限制平均生存时间时，cox模型的有效估计。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-05-01 Epub Date: 2025-03-13 DOI: 10.1080/10543406.2024.2444242

Jo-Ying Hung, Junjiang Zhong, Huang-Tz Ou, Pei-Fang Su

引用次数: 0

Different view of the diagnostics test accuracy measures and optimal cut-off point selection procedure under tree or umbrella ordering. 在树状排序或伞状排序下，诊断检测准确度测量和最佳截断点选择程序的不同视角。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-05-01 Epub Date: 2024-10-30 DOI: 10.1080/10543406.2024.2420659

Jing Kersey, Hani Samawi, Marwan Alsharman, Mario Keko, Haresh Rochani, Lili Yu, Jingjing Yin, Kelly Sullivan, Salaheddin Mustafa

{"title":"Different view of the diagnostics test accuracy measures and optimal cut-off point selection procedure under tree or umbrella ordering.","authors":"Jing Kersey, Hani Samawi, Marwan Alsharman, Mario Keko, Haresh Rochani, Lili Yu, Jingjing Yin, Kelly Sullivan, Salaheddin Mustafa","doi":"10.1080/10543406.2024.2420659","DOIUrl":"10.1080/10543406.2024.2420659","url":null,"abstract":"In the realm of medical diagnostic testing, diagnostic tests can assume either binary forms, distinguishing between diseased and non-diseased states, or ordinal forms, categorizing states from non-diseased to various stages (1 to K). Another significant classification scheme for multi-class scenarios is tree or umbrella ordering, which entails several unordered sub-classes (subtypes) within a biomarker. Within tree or umbrella ordering, the classifier assesses whether the marker measurement for one class surpasses or falls below those for the other classes. Although Receiver Operating Characteristic (ROC) curves and summary measures have been adapted to accommodate tree and umbrella ordering, these approaches often yield cut-off points that generate highly sensitive tests for certain disease subtypes while compromising specificity for others. This may not be ideal for all diseases. Hence, in this investigation, we explore diverse measures of diagnostic test accuracy and optimal cut-off point selection procedures under tree or umbrella ordering to foster more specific tests. We present numerical examples and simulation studies and demonstrate the approach using real data on lung cancer.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"456-486"},"PeriodicalIF":1.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549002","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

Applying machine learning-based multiple imputation methods to nonparametric multiple comparisons in longitudinal clinical studies. 将基于机器学习的多重输入方法应用于纵向临床研究的非参数多重比较。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-05-01 Epub Date: 2024-12-21 DOI: 10.1080/10543406.2024.2444243

Tuncay Yanarateş, Erdem Karabulut

{"title":"Applying machine learning-based multiple imputation methods to nonparametric multiple comparisons in longitudinal clinical studies.","authors":"Tuncay Yanarateş, Erdem Karabulut","doi":"10.1080/10543406.2024.2444243","DOIUrl":"10.1080/10543406.2024.2444243","url":null,"abstract":"Dependent samples, in which repeated measurements are made on the same subjects, eliminate potential differences among the subjects. In k-dependent samples, missing data can occur for various reasons. The Skillings-Mack test is used instead of the Friedman test for k-dependent samples with missing observations that are non-normally distributed. If a significant difference exists among groups, nonparametric multiple comparisons need to be performed. In this study, we propose an innovative approach by applying four methods to nonparametric multiple comparisons of incomplete k-dependent samples that are non-normally distributed. The four methods are two nonparametric multiple imputation methods based on machine learning (multiple imputations by chained equations utilizing classification and regression trees (MICE-CART) and random forest (MICE-RF)), one nonparametric imputation method (random hot deck imputation), and the listwise deletion method. We compare the four methods under two missing data mechanisms, four correlation coefficients, two sample sizes, and three percentages of missingness. After implementing different scenarios in a simulation study, the listwise deletion method is inferior to the other methods. MICE-CART and MICE-RF are superior to the other methods for moderate and small sample sizes with well-controlled type 1 error. The two nonparametric multiple imputation methods based on machine learning can be applied to nonparametric multiple comparisons. Therefore, we propose machine learning-based multiple imputation methods for nonparametric multiple comparisons of k-dependent samples with missing observations. The approach was also illustrated with a longitudinal dentistry clinical trial.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"549-560"},"PeriodicalIF":1.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873472","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 operating characteristics of current early phase dose finding designs with toxicity and efficacy in oncology. 目前肿瘤学早期阶段剂量发现设计与毒性和疗效的统计运行特征。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-05-01 Epub Date: 2024-11-16 DOI: 10.1080/10543406.2024.2424845

Hao Sun, Hsin-Yu Lin, Jieqi Tu, Revathi Ananthakrishnan, Eunhee Kim

{"title":"Statistical operating characteristics of current early phase dose finding designs with toxicity and efficacy in oncology.","authors":"Hao Sun, Hsin-Yu Lin, Jieqi Tu, Revathi Ananthakrishnan, Eunhee Kim","doi":"10.1080/10543406.2024.2424845","DOIUrl":"10.1080/10543406.2024.2424845","url":null,"abstract":"Traditional phase I dose finding cancer clinical trial designs aim to determine the maximum tolerated dose (MTD) of the investigational cytotoxic agent based on a single toxicity outcome, assuming a monotone dose-response relationship. However, this assumption might not always hold for newly emerging therapies such as immuno-oncology therapies and molecularly targeted therapies, making conventional dose finding trial designs based on toxicity no longer appropriate. To tackle this issue, numerous early-phase dose finding clinical trial designs have been developed to identify the optimal biological dose (OBD), which takes both toxicity and efficacy outcomes into account. In this article, we review the current model-assisted dose finding designs, BOIN-ET, BOIN12, UBI, TEPI-2, PRINTE, STEIN, and uTPI to identify the OBD and compare their operating characteristics. Extensive simulation studies and a case study using a CAR T-cell therapy phase I trial have been conducted to compare the performance of the aforementioned designs under different possible dose-response relationship scenarios. The simulation results demonstrate that the performance of different designs varies depending on the particular dose-response relationship and the specific metric considered. Based on our simulation results and practical considerations, STEIN, PRINTE, and BOIN12 outperform the other designs from different perspectives.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"398-418"},"PeriodicalIF":1.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645142","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

Revolutionizing cardiovascular disease classification through machine learning and statistical methods. 通过机器学习和统计方法革新心血管疾病分类。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-05-01 Epub Date: 2024-11-24 DOI: 10.1080/10543406.2024.2429524

Tapan Kumar Behera, Siddhartha Sathia, Sibarama Panigrahi, Pradeep Kumar Naik

{"title":"Revolutionizing cardiovascular disease classification through machine learning and statistical methods.","authors":"Tapan Kumar Behera, Siddhartha Sathia, Sibarama Panigrahi, Pradeep Kumar Naik","doi":"10.1080/10543406.2024.2429524","DOIUrl":"10.1080/10543406.2024.2429524","url":null,"abstract":"Background: Cardiovascular diseases (CVDs) include abnormal conditions of the heart, diseased blood vessels, structural problems of the heart, and blood clots. Traditionally, CVD has been diagnosed by clinical experts, physicians, and medical specialists, which is expensive, time-consuming, and requires expert intervention. On the other hand, cost-effective digital diagnosis of CVD is now possible because of the emergence of machine learning (ML) and statistical techniques.Method: In this research, extensive studies were carried out to classify CVD via 19 promising ML models. To evaluate the performance and rank the ML models for CVD classification, two benchmark CVD datasets are considered from well-known sources, such as Kaggle and the UCI repository. The results are analysed considering individual datasets and their combination to assess the efficiency and reliability of ML models on the basis of various performance measures, such as precision, kappa, accuracy, recall, and the F1 score. Since some of the ML models are stochastic, we repeated the simulation 50 times for each dataset using each model and applied nonparametric statistical tests to draw decisive conclusions.Results: The nonparametric Friedman - Nemenyi hypothesis test suggests that the Extra Tree Classifier provides statistically superior accuracy and precision compared with all other models. However, the Extreme Gradient Boost (XGBoost) classifier provides statistically superior recall, kappa, and F1 scores compared with those of all the other models. Additionally, the XGBRF classifier achieves a statistically second-best rank in terms of the recall measures.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"504-526"},"PeriodicalIF":1.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142711177","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

Defective regression models for cure rate data with competing risks. 具有竞争风险的治愈率数据的缺陷回归模型。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-05-01 Epub Date: 2024-11-14 DOI: 10.1080/10543406.2024.2424838

K Silpa, E P Sreedevi, P G Sankaran

引用次数: 0