Pharmaceutical Statistics最新文献_第2页

The Impact of Model Misspecification on the Individual Causal Association in Surrogate Endpoint Evaluation. 替代终点评估中模型错配对个体因果关联的影响。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70110

Gokce Deliorman, Florian Stijven, Wim Van der Elst, Maria Del Carmen Pardo, Ariel Alonso

{"title":"The Impact of Model Misspecification on the Individual Causal Association in Surrogate Endpoint Evaluation.","authors":"Gokce Deliorman, Florian Stijven, Wim Van der Elst, Maria Del Carmen Pardo, Ariel Alonso","doi":"10.1002/pst.70110","DOIUrl":"10.1002/pst.70110","url":null,"abstract":"Surrogate endpoints are often used in place of expensive, delayed, or rare clinical endpoints in clinical trials. However, regulatory authorities require thorough evaluation to accept these surrogate endpoints as reliable substitutes. One evaluation approach is the information-theoretic causal inference framework, which quantifies surrogacy using the individual causal association (ICA). Like most causal inference methods, this approach relies on models that are only partially identifiable. For continuous outcomes, a normal model is often used. In this study, we explored the effects of model misspecification across various scenarios. We first considered true data-generating mechanisms based on multivariate <math> <semantics><mrow><mi>t</mi></mrow> <annotation>$$ t $$</annotation></semantics> </math> and log-normal distributions. We then used D-vine copulas with Gaussian, Clayton, Gumbel, and Frank families to vary the unidentifiable copulas involving counterfactual pairs while preserving the observable bivariate margins, and considered intuitive restrictions on nonidentified correlations, including positivity and conditional independence. In all settings, the identifiability issue was addressed through sensitivity analysis. Finally, we illustrate the proposed sensitivity analyses using clinical-trial data from schizophrenia studies, evaluating the ICA under several modeling assumptions. The results show that, in most scenarios considered, the impact of model misspecification is small; however, certain departures from the assumed model can materially affect the surrogacy assessment.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 4","pages":"e70110"},"PeriodicalIF":1.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13356973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430821","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}

引用次数: 0

Early Phase Dose-Finding Designs for CAR-T Cell Therapies. CAR-T细胞疗法的早期剂量发现设计。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70102

Weishi Chen, Pavel Mozgunov, Jimmy Mullaert, Xavier Paoletti

{"title":"Early Phase Dose-Finding Designs for CAR-T Cell Therapies.","authors":"Weishi Chen, Pavel Mozgunov, Jimmy Mullaert, Xavier Paoletti","doi":"10.1002/pst.70102","DOIUrl":"10.1002/pst.70102","url":null,"abstract":"Chimeric Antigen Receptor (CAR)-T cell is an immunotherapy which revolutionised the treatment of relapsed/refractory lymphoma and leukaemia. It is shown to have a higher response rate, higher mid-to-long term overall survival, and lower toxicity than standard treatments. However, due to a lack of dose-limiting toxicity (DLT) and unclear dose-effect relationship, traditional phase I designs of clinical trials cannot lead to accurate selections of the optimal dose (OD). Beside clinical outcomes, the CAR-T cell expansion from serial blood samples is measured at various time points. We propose a novel early phase dose-finding design for CAR-T cells, using both toxicity and activity endpoints to locate the OD. The number of CAR-T cells measured in the peripheral blood is used to indicate activity, which is more sensitive than the short-term clinical responses traditionally used. A Bi-Exponential model is used for the repeated measures of the number of cells for each patient, and is estimated under a Bayesian framework. The model is motivated by biological concerns and is flexible enough to accommodate different shapes of the cell-expansion curve. Three criteria for activity are considered: (1) the number of cells at specific time points, (2) the duration before all cells are eliminated, (3) the area under the cell-expansion curve. Simulation studies show that the OD can be selected with high accuracy even under small sample sizes.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 4","pages":"e70102"},"PeriodicalIF":1.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13277398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148272436","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}

引用次数: 0

Optimizing Randomization Ratios in Clinical Trials With Survival Endpoints. 在有生存终点的临床试验中优化随机化比例。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70103

Luoying Yang, Weijia Mai, Yuanyuan Han

{"title":"Optimizing Randomization Ratios in Clinical Trials With Survival Endpoints.","authors":"Luoying Yang, Weijia Mai, Yuanyuan Han","doi":"10.1002/pst.70103","DOIUrl":"10.1002/pst.70103","url":null,"abstract":"An equal randomization ratio (1:1) is the most commonly used allocation strategy in confirmatory clinical trials. Recently, there has been growing discussion around the use of unequal randomization, which may be favored for several reasons, including encouraging trial recruitment, reducing costs, and improving the robustness of estimates in the treatment arm. However, despite these potential benefits, unequal randomization is still rarely applied in trial design. A key barrier is the lack of a method to determine the optimal randomization ratio that balances the various considerations of a trial. To address this challenge, we developed an optimization framework that identifies the optimal randomization ratio to maximize a trial's probability of success and profit-two major considerations in trial design. The framework incorporates trial parameters such as prior knowledge of treatment efficacy, sample size, budget, and per-subject cost. We evaluate the proposed method through simulations and a hypothetical trial. The results show that the optimal randomization ratio is highly influenced by sample size and cost differences between treatment arms. Our framework demonstrates the ability to reduce costs while maintaining a high probability of success.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 4","pages":"e70103"},"PeriodicalIF":1.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148272399","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

Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control. 有生存终点的平台试验中信息泄漏对I型错误控制的影响

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70106

Quynh Nguyen, Martin Posch, Benjamin Hofner, Franz König

{"title":"Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control.","authors":"Quynh Nguyen, Martin Posch, Benjamin Hofner, Franz König","doi":"10.1002/pst.70106","DOIUrl":"10.1002/pst.70106","url":null,"abstract":"Platform trials evaluate multiple treatments within a single trial infrastructure. Such designs have gained a lot of attraction in clinical research. If information gained from platform trials should provide confirmatory evidence for regulatory decisions, control of the Type I error rate is key. One critical issue is information leakage, for example, if any information of the ongoing trial is available, especially if it may impact the further conduct of treatments still in the platform trial and bias their results. This paper evaluates the potential impact of information leakage on the control of the Type I error rate in platform trials with time-to-event endpoints such as overall survival. We explore different strategies how information on the treatment effect of an still ongoing treatment could be obtained if a pre-planned analysis for another arm is conducted. This (leaked) information might be used to decide whether to continue the other arm as planned or conduct its final analysis immediately. By means of clinical trial simulations we evaluate the impact of different levels of information leakage on the Type I error rate. We show how the conditional error principle can be applied to estimate worst case Type I error rate inflation for the different forms of information leakage. We do not aim to quantify the exact maximum Type I error rate inflation but rather to raise awareness of the potential risk for estimation of comparative results. Finally, we discuss the regulatory implications of information leakage and propose strategies to mitigate these risks.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 4","pages":"e70106"},"PeriodicalIF":1.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13305155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148331771","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}

引用次数: 0

Will the Pharmaceutical Industry Need Statisticians in an AI World? 制药行业在人工智能世界中需要统计学家吗？

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70100

Chris Harbron

引用次数: 0

Fiducial-Based Statistical Intervals for Bounded Bioanalytical Data Using the Kumaraswamy Distribution. 使用Kumaraswamy分布的有界生物分析数据的基于基准的统计区间。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70098

Jorge Quiroz, Jingwei Xiong, Satrajit Roychoudhury, Heliang Shi

{"title":"Fiducial-Based Statistical Intervals for Bounded Bioanalytical Data Using the Kumaraswamy Distribution.","authors":"Jorge Quiroz, Jingwei Xiong, Satrajit Roychoudhury, Heliang Shi","doi":"10.1002/pst.70098","DOIUrl":"10.1002/pst.70098","url":null,"abstract":"Biological measurements such as percent monomer from size exclusion chromatography (SEC) or cell-based percentages from flow cytometry are continuous quantities bounded between 0 and 1. These continuous bounded data are frequently skewed and therefore construction of normal-based confidence, prediction, and tolerance intervals may not be appropriate. Transformation based approaches (e.g., logit or arcsine-square-root) are often recommended to analyze bounded data; however, for highly skewed data with sample size, they may yield intervals that are too wide for practical applications. Although the beta distribution is a natural model for continuous bounded data, routine interval procedures remain limited in Chemical, Manufacturing, and Controls (CMC) practice. The Kumaraswamy distribution is a flexible and mathematically tractable alternative. In this manuscript, we develop two new pivotal quantities for the Kumaraswamy shape parameter <math> <semantics><mrow><mi>α</mi></mrow> <annotation>$$ alpha $$</annotation></semantics> </math> . Combined with an existing generalized pivotal quantity for <math> <semantics><mrow><mi>β</mi></mrow> <annotation>$$ beta $$</annotation></semantics> </math> , these pivot quantities support fiducial-based construction of statistical intervals for functions of the shape parameters <math> <semantics><mrow><mi>α</mi></mrow> <annotation>$$ alpha $$</annotation></semantics> </math> and <math> <semantics><mrow><mi>β</mi></mrow> <annotation>$$ beta $$</annotation></semantics> </math> , such as confidence intervals for the mean and fiducial-type tolerance and prediction intervals. We investigate finite-sample performance of these intervals through a simulation study and compare the proposed methods with a published pivotal approach and commonly used transformations. We also illustrate the methods with a simulated percent monomer data representative of CMC bioanalytical practice. Practical recommendations for routine use are provided.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 4","pages":"e70098"},"PeriodicalIF":1.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147974244","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

An Ensemble Classifier for Ordinal Outcomes in High-Dimensional Genomics Data. 高维基因组数据中有序结果的集成分类器。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-05-06 DOI: 10.1002/pst.70097

Heranga K Rathnasekara, Sinjini Sikdar

{"title":"An Ensemble Classifier for Ordinal Outcomes in High-Dimensional Genomics Data.","authors":"Heranga K Rathnasekara, Sinjini Sikdar","doi":"10.1002/pst.70097","DOIUrl":"10.1002/pst.70097","url":null,"abstract":"Analysis of genomics data for predicting disease outcomes is a fast-growing field in medical research. There often exist categorical, specifically, ordinal outcomes that need to be predicted based on genomic profiles. This has led to recent development of some high-dimensional ordinal classification methods that can address the large dimensionality of the genomic covariate set. These high-dimensional ordinal models tend to vary widely in their performance depending on the data they are applied to and the evaluation criteria used. In this article, we outline an ensemble ordinal classifier that integrates different ordinal modeling approaches through bootstrap-based model evaluation, multi-metric performance assessment, and rank aggregation to produce a final prediction that can alleviate the uncertainty of relying on a single model. Through multiple simulated studies and real genomic data analyses, we show that the ensemble method consistently ranks among the top-performing models. These findings underscore the potential of ensemble learning to improve the robustness and predictive accuracy of high-dimensional ordinal classification in genomic research.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 3","pages":"e70097"},"PeriodicalIF":1.5,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13158174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147869261","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}

引用次数: 0

Consistency Assessment and Regional Sample Size Calculation for MRCT Under Random Effects Model. 随机效应模型下MRCT一致性评价及区域样本量计算。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-05-01 DOI: 10.1002/pst.70090

Xinru Ren, Jin Xu

{"title":"Consistency Assessment and Regional Sample Size Calculation for MRCT Under Random Effects Model.","authors":"Xinru Ren, Jin Xu","doi":"10.1002/pst.70090","DOIUrl":"10.1002/pst.70090","url":null,"abstract":"Multi-regional clinical trials (MRCTs) have become common practice for drug development and global registration. Once overall significance is established, demonstrating regional consistency is critical for local health authorities. Methods for evaluating such consistency and calculating regional sample sizes have been proposed based on the fixed effects model using various criteria. To better account for the heterogeneity of treatment effects across regions, the random effects model naturally arises as a more effective alternative for both design and inference. In this paper, we present the design of the overall sample size along with regional sample fractions. We also provide the theoretical properties for assessing consistency probability using Method 1 of Ministry of Health, Labour and Welfare of Japan (MHLW), based on the empirical shrinkage estimator. The latter is then used to determine the regional sample size of interest. We elaborate on the applications to normal, binary, and survival endpoints in detail. Simulation studies show that the proposed method retains the consistency probability at the desired level. We illustrate the application using a real cardiovascular outcome trial in diabetes. An R package is provided for implementation.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 3","pages":"e70090"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623465","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

Diagnostic Accuracy Measures and Optimal Cut-Off Points for Nested Disease Subtypes Within the Disease Ordinal States. 疾病序数状态内嵌套疾病亚型的诊断准确度测量和最佳分界点。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-05-01 DOI: 10.1002/pst.70099

Hani Samawi, Marwan Alsharman, Jing Kersey

{"title":"Diagnostic Accuracy Measures and Optimal Cut-Off Points for Nested Disease Subtypes Within the Disease Ordinal States.","authors":"Hani Samawi, Marwan Alsharman, Jing Kersey","doi":"10.1002/pst.70099","DOIUrl":"10.1002/pst.70099","url":null,"abstract":"In medical diagnostic testing, classifications are commonly divided into two primary types: binary tests, which distinguish between diseased and nondiseased cases, and ordinal states, which categorize cases into nondiseased states and disease stages ranging from 1 to k. Additionally, there exists a multiclass classification scheme, referred to as tree or umbrella ordering, in which a classifier determines whether a biomarker measurement for one class is higher or lower than that of the other classes. We introduce a novel concept called Nested Disease Subtypes Within the Disease Ordinal States, which extends the summary measures of receiver operating characteristic (ROC) curves to accommodate this unique classification approach. Our method aims to refine current diagnostic categorization by accounting for the complexity of certain diseases that do not conform to traditional classifications. To validate the effectiveness of our approach, we conducted simulation studies and applied it to real data on tuberculosis. These findings highlight the potential of our methods to enhance both the precision and comprehension of medical diagnostic testing, particularly, for complex diseases.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 3","pages":"e70099"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147932447","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

Integrating Preclinical Insights for Adaptive Dose Escalation in Phase I Oncology Trials. 整合I期肿瘤试验中适应性剂量递增的临床前见解。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-05-01 DOI: 10.1002/pst.70093

Helen Barnett, Melanie Guhl, Fulvio Di Stefano, Donia Skanji, Sandrine Guilleminot, Gaelle Saint-Hilary, Pavel Mozgunov, Marie-Karelle Riviere

引用次数: 0