Pharmaceutical Statistics最新文献

A General Approach for Sample Size Calculation With Nonproportional Hazards and Cure Rates. 非比例风险和治愈率下样本大小计算的一般方法。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-07-01 DOI: 10.1002/pst.70024

Huan Cheng, Xiaoyun Li, Jianghua He

引用次数: 0

Further Practical Guidance on Adjusting Time-To-Event Outcomes for Treatment Switching. 关于调整治疗转换的时间到事件结果的进一步实用指南。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-07-01 DOI: 10.1002/pst.70019

Claire Watkins, Eva Kleine, Miguel Miranda, Emmanuel Bourmaud, Orlando Doehring

{"title":"Further Practical Guidance on Adjusting Time-To-Event Outcomes for Treatment Switching.","authors":"Claire Watkins, Eva Kleine, Miguel Miranda, Emmanuel Bourmaud, Orlando Doehring","doi":"10.1002/pst.70019","DOIUrl":"10.1002/pst.70019","url":null,"abstract":"The objective of this article is to bring together the key current information on practical considerations when conducting statistical analyses adjusting long-term outcomes for treatment switching, combining it with learnings from our own experience, thus providing a useful reference tool for analysts. When patients switch from their randomised treatment to another therapy that affects a subsequently observed outcome such as overall survival, there may be interest in estimating the treatment effect under a hypothetical scenario without the intercurrent event of switching. We describe the theory and provide guidance on how and when to conduct analyses using three commonly used complex approaches: rank preserving structural failure time models (RPSFTM), two-stage estimation (TSE), and inverse probability of censoring weighting (IPCW). Extensions and alternatives to the standard approaches are summarised. Important and sometimes misunderstood concepts such as recensoring and sources of variability are explained. An overview of available software and programming guidance is provided, along with an R code repository for a worked example, reporting recommendations, and a review of the current acceptability of these methods to regulatory and health technology assessment agencies. Since the current guidance on this topic is scattered across multiple sources, it is difficult for an analyst to obtain a good overview of all options and potential pitfalls. This paper is intended to save statisticians time and effort by summarizing important information in a single source. By also including recommendations for best practice, it aims to improve the quality of the analyses and reporting when adjusting time-to-event outcomes for treatment switching.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 4","pages":"e70019"},"PeriodicalIF":1.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340348","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

The Choice Between Pearson's χ² Test and Fisher's Exact Test for 2 × 2 Tables. 2 × 2 表的皮尔逊 χ2 检验和费雪精确检验之间的选择。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-05-01 DOI: 10.1002/pst.70012

Markus Neuhäuser, Graeme D Ruxton

引用次数: 0

Statistical Tutorial for Cut-Point Determination in Immunogenicity Studies. 免疫原性研究中切割点测定的统计教程。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-05-01 DOI: 10.1002/pst.70016

Yulia Mordashova, Xin Huang

{"title":"Statistical Tutorial for Cut-Point Determination in Immunogenicity Studies.","authors":"Yulia Mordashova, Xin Huang","doi":"10.1002/pst.70016","DOIUrl":"https://doi.org/10.1002/pst.70016","url":null,"abstract":"Administration of therapeutic protein products might potentially elicit an immune response via production of Anti-Drug Antibodies (ADA). This immune response can cause some clinical consequences ranging from mild to harmful for the patient, affecting the safety and efficacy of the drug. Therefore, assessment of Immunogenicity and the ability to follow possible associations between ADA assay measurements and clinical events is one of the key parts of clinical safety evaluation in both clinical and preclinical areas. In order to assess the immunogenicity of biological drug molecules, it is important to develop and validate reliable laboratory methods and evaluate various performance characteristics during development and validation phases. Determination of the screening assay cut-point and establishment of the confirmatory assay cut-point are fundamental aspects of ADA assay validation. Existing regulatory guidance documents addressing immunogenicity topics (immunoassays) cover the development and validation of reliable laboratory methods, but there is a need for more comprehensive discussions on statistical evaluation methods. While there is literature available on statistical methods for cut-point estimation, this tutorial aims to provide additional statistical considerations specifically tailored for ADA assay development and validation cut-points. Furthermore, practical R code snippets are provided to facilitate the implementation of the key evaluation steps. This resource aims to enhance the rigor and reliability of ADA assay validation cut-point evaluation, ultimately contributing to more robust immunogenicity assessments in clinical studies.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 3","pages":"e70016"},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015392","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

CUSUMIN Combination: A Cumulative Sum Interval Design for Phase I Cancer Drug-Combination Trials. CUSUMIN联合：一期癌症药物联合试验的累积和区间设计。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-05-01 DOI: 10.1002/pst.70007

Tomoyoshi Hatayama, Seiichi Yasui

{"title":"CUSUMIN Combination: A Cumulative Sum Interval Design for Phase I Cancer Drug-Combination Trials.","authors":"Tomoyoshi Hatayama, Seiichi Yasui","doi":"10.1002/pst.70007","DOIUrl":"https://doi.org/10.1002/pst.70007","url":null,"abstract":"Recently, model-assisted designs, including a Bayesian optimal interval (BOIN) design with optimal thresholds for determining the dose for the next cohort, have been proposed for Phase I cancer studies. Model-assisted designs are useful owing to their good performance in addition to their algorithm-based simplicity. In this era of precision medicine, drug combinations are widely used to enhance treatment efficacy and overcome resistance to monotherapies. However, identification of maximum tolerated dose (MTD) combinations is complicated because the joint toxicity order of paired doses is only partially known. BOIN and Keyboard combination designs are the only model-assisted designs developed to date. Further, both these combination designs show similar operational characteristics. Despite the simplicity and superior performance of model-assisted designs, they have not been sufficiently studied in Phase I drug combination trials. In this study, to provide a new design with simplicity and superior performance compared to model-assisted designs for dose-combination cancer Phase I studies, we extend the cumulative sum interval design (CUSUMIN) developed for single-agent dose-finding design based on statistical quality control methodology, which improves on BOIN and other representative model-assisted designs in terms of controlling overdosing rates while maintaining similar performance in determining the MTD. CUSUMIN can be expected to provide a safer assignment than that of BOIN in drug combination dose-finding studies while maintaining MTD selection performance, as shown in the single-agent dose-finding settings.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 3","pages":"e70007"},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026201","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

Why "Minimal Clinically Important Difference" for Interpreting the Magnitude of the Treatment Effect Is Not Useful. 为什么用“最小临床重要差异”来解释治疗效果的大小是没有用的。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-05-01 DOI: 10.1002/pst.70015

Jitendra Ganju

{"title":"Why \"Minimal Clinically Important Difference\" for Interpreting the Magnitude of the Treatment Effect Is Not Useful.","authors":"Jitendra Ganju","doi":"10.1002/pst.70015","DOIUrl":"https://doi.org/10.1002/pst.70015","url":null,"abstract":"The term \"minimal clinically important difference\" (MCID), though defined as the smallest change in an outcome that is meaningful to the patient, is often used to interpret differences between treatment groups. It is in this context that the limitations of MCID are discussed, which include: the omission of the role of time in its definition for progressive diseases; the unsuitability of adopting MCID derived from open-label studies for randomized, placebo-controlled, blinded studies; the unreliability of MCID in rare disease trials; challenges in interpretation when placebo patients also achieve MCID; the failure to account for how differences in patient populations affect MCID (e.g., inclusion or exclusion of patients on prior treatment); not recognizing the connection between the true treatment effect, MCID and power; lack of consideration of differences in analysis methods (e.g., the extent of missing data and how it is handled); and the limitations of an MCID-based responder analysis. Therefore, the recommendation made is to prospectively define a customized MCID that addresses each deficit. If the deficits cannot be adequately resolved, then the recommendation is that trial results should be interpreted without reference to MCID.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 3","pages":"e70015"},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144024423","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 Tipping Point Method to Evaluate Sensitivity to Potential Violations in Missing Data Assumptions. 一种评估缺失数据假设中潜在违规敏感性的引爆点方法。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-05-01 DOI: 10.1002/pst.70002

Cesar Torres, Gregory Levin, Daniel Rubin, William Koh, Rebecca Chiu, Thomas Permutt

{"title":"A Tipping Point Method to Evaluate Sensitivity to Potential Violations in Missing Data Assumptions.","authors":"Cesar Torres, Gregory Levin, Daniel Rubin, William Koh, Rebecca Chiu, Thomas Permutt","doi":"10.1002/pst.70002","DOIUrl":"10.1002/pst.70002","url":null,"abstract":"It is critical to evaluate the sensitivity of conclusions from a clinical trial to potential violations in the missing data assumptions of the statistical analysis. Sensitivity analyses should not consist of a few methods that might have been reasonable alternatives to the chosen analysis method, nor should they explore only a limited space of violations in the assumptions of the analysis. Instead, sensitivity analyses should target the same estimand as that targeted in the main analysis, and they should systematically and comprehensively explore the space of possible assumptions to evaluate whether the key conclusions hold up under all plausible scenarios. In a randomized, controlled trial, this can be achieved by tipping point analyses that vary assumptions about missing outcomes on the experimental and control arms to identify and discuss the plausibility of scenarios under which there is no longer evidence of a treatment effect. We introduce a simple, novel tipping point approach in which, for a variable that is quantitative or can be analyzed as if it is quantitative, inference on the treatment effect is based on the observed data and two sensitivity parameters, with minimal assumptions and no need for imputation. The sensitivity parameters to be varied are the mean differences between outcomes in dropouts and outcomes in completers on each of the two treatment arms. We derive the asymptotic properties of the proposed statistic and illustrate the utility of such an approach with two examples of drug reviews in which the methodology was utilized to inform regulatory decision-making.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 3","pages":"e70002"},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143721035","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

Randomization in Pre-Clinical Studies: When Evolution Theory Meets Statistics. 临床前研究中的随机化：当进化论与统计学相遇。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-05-01 DOI: 10.1002/pst.70005

Sofia Weigle, Davit Sargsyan, Javier Cabrera, Luwis Diya, Jocelyn Sendecki, Mariusz Lubomirski

引用次数: 0

Control of Unconditional Type I Error in Clinical Trials With External Control Borrowing-A Two-Stage Adaptive Design Perspective. 外部对照借用对临床试验中无条件I型错误的控制——两阶段自适应设计视角。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-05-01 DOI: 10.1002/pst.70011

Ping Gao, Xiao Ni, Jing Li, Rachel Chu

引用次数: 0

Applying the Principal Stratum Strategy in Equivalence Trials: A Case Study. 主层策略在等效试验中的应用：一个案例研究。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-05-01 DOI: 10.1002/pst.70008

Jerome Sepin, Thomas P A Debray, Wei Wei, Hans C Ebbers, Cristina Fernandez-Mendivil, Marian Mitroiu

{"title":"Applying the Principal Stratum Strategy in Equivalence Trials: A Case Study.","authors":"Jerome Sepin, Thomas P A Debray, Wei Wei, Hans C Ebbers, Cristina Fernandez-Mendivil, Marian Mitroiu","doi":"10.1002/pst.70008","DOIUrl":"https://doi.org/10.1002/pst.70008","url":null,"abstract":"The estimand framework, introduced in the ICH E9 (R1) Addendum, provides a structured approach for defining precise research questions in randomised clinical trials. It suggests five strategies for addressing intercurrent events (ICE). This case study examines the principal stratum strategy, highlighting its potential for estimating causal treatment effects in specific subpopulations and the challenges involved. The occurrence of anti-drug antibodies (ADAs) and their potential clinical impact are important factors in evaluating biosimilars. Typically, analyses focus on subgroups of patients who develop ADAs during the study. However, conducting subgroup analyses based on post-randomisation variables, such as immunogenicity, can introduce substantial bias into treatment effect estimates and is therefore methodologically not optimal. The principal stratum strategy provides a statistical pathway for estimating treatment effects in subpopulations that cannot be anticipated at baseline. By leveraging counterfactuals to assess treatment outcomes, with and without the incidence of intercurrent events (ICEs), this approach can be implemented through a missing data perspective. We demonstrate the implementation of the principal stratum strategy in a phase 3 equivalence trial of a biosimilar for the treatment of rheumatoid arthritis. Using a multiple imputation approach, we leverage longitudinal measurements to create analysis datasets for subpopulations who develop ADAs as ICE. Our results highlight the principal stratum strategy's potential and challenges, emphasising its reliance on unobserved ICE states and the need for complex and rigorous modelling. This study contributes to a nuanced understanding and practical implementation of the principal stratum strategy within the ICH E9 (R1) framework.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 3","pages":"e70008"},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047831","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