Pub Date : 2023-02-22DOI: 10.1080/19466315.2023.2183252
Heng Xu, Yi Liu, R. Beckman
{"title":"Adaptive Endpoints Selection with Application in Rare Disease","authors":"Heng Xu, Yi Liu, R. Beckman","doi":"10.1080/19466315.2023.2183252","DOIUrl":"https://doi.org/10.1080/19466315.2023.2183252","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43344554","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}
Pub Date : 2023-02-21DOI: 10.1080/19466315.2023.2182355
Man Jin, Yixin Fang
{"title":"Methods for Informative Censoring in Time-to-Event Data Analysis","authors":"Man Jin, Yixin Fang","doi":"10.1080/19466315.2023.2182355","DOIUrl":"https://doi.org/10.1080/19466315.2023.2182355","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49101911","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}
Pub Date : 2023-02-21DOI: 10.1080/19466315.2023.2177332
W. Shih, Yunqi Zhao, Tai Xie
Abstract The traditional Simon’s two-stage design for phase IIA clinical trials is modified to enhance the flexibility in conducting the interim analysis and sample size adjustment. The modification is based on the well-established methodology in adaptive designs using the conditional probability and allows for early termination as well as extension with sample size adjustment. The dynamic data monitoring system is naturally suitable for basket trials where several tumor types are monitored simultaneously with different enrollment rates.
{"title":"Modified Simon’s Two-Stage Design for Phase IIA Clinical Trials in Oncology – Dynamic Monitoring and More Flexibility","authors":"W. Shih, Yunqi Zhao, Tai Xie","doi":"10.1080/19466315.2023.2177332","DOIUrl":"https://doi.org/10.1080/19466315.2023.2177332","url":null,"abstract":"Abstract The traditional Simon’s two-stage design for phase IIA clinical trials is modified to enhance the flexibility in conducting the interim analysis and sample size adjustment. The modification is based on the well-established methodology in adaptive designs using the conditional probability and allows for early termination as well as extension with sample size adjustment. The dynamic data monitoring system is naturally suitable for basket trials where several tumor types are monitored simultaneously with different enrollment rates.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43947167","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}
Pub Date : 2023-02-07DOI: 10.1080/19466315.2023.2177726
Chenchen Ma, K. Crimin
{"title":"Joint Analysis of Longitudinal Data and Zero-Inflated Recurrent Events","authors":"Chenchen Ma, K. Crimin","doi":"10.1080/19466315.2023.2177726","DOIUrl":"https://doi.org/10.1080/19466315.2023.2177726","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49421465","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}
Pub Date : 2023-02-07DOI: 10.1080/19466315.2023.2177333
M. Ho, Susan Gruber, Yixin Fang, Douglas E Faris, P. Mishra-Kalyani, D. Benkeser, M. J. van der Laan
{"title":"Examples of Applying RWE Causal-Inference Roadmap to Clinical Studies","authors":"M. Ho, Susan Gruber, Yixin Fang, Douglas E Faris, P. Mishra-Kalyani, D. Benkeser, M. J. van der Laan","doi":"10.1080/19466315.2023.2177333","DOIUrl":"https://doi.org/10.1080/19466315.2023.2177333","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43701464","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}
Pub Date : 2023-01-30DOI: 10.1101/2023.01.28.23285146
Qiang Zhao, Haijun Ma
Incorporating historical information in clinical trials has been of much interest recently because of its potential to reduce the size and cost of clinical trials. Data-conflict is one of the biggest challenges in incorporating historical information. In order to address the conflict between historical data and current data, several methods have been proposed including the robust meta-analytic-predictive (rMAP) prior method. In this paper, we propose to modify the rMAP prior method by using an empirical Bayes approach to estimate the weights for the two components of the rMAP prior. Via numerical calculations, we show that this modification to the rMAP method improves its performance regarding multiple key metrics.
{"title":"Modified Robust Meta-Analytic-Predictive Priors for Incorporating Historical Controls in Clinical Trials","authors":"Qiang Zhao, Haijun Ma","doi":"10.1101/2023.01.28.23285146","DOIUrl":"https://doi.org/10.1101/2023.01.28.23285146","url":null,"abstract":"Incorporating historical information in clinical trials has been of much interest recently because of its potential to reduce the size and cost of clinical trials. Data-conflict is one of the biggest challenges in incorporating historical information. In order to address the conflict between historical data and current data, several methods have been proposed including the robust meta-analytic-predictive (rMAP) prior method. In this paper, we propose to modify the rMAP prior method by using an empirical Bayes approach to estimate the weights for the two components of the rMAP prior. Via numerical calculations, we show that this modification to the rMAP method improves its performance regarding multiple key metrics.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49385509","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}
Pub Date : 2023-01-30DOI: 10.1080/19466315.2023.2173645
Susan Mayo, Yongman Kim
Abstract The ICH E9(R1) guidance on estimands is a key tool for the creation and review of protocol design and analysis planning, for both industry and regulatory statisticians. The framework has been described as useful for improving study design, intercurrent event handling, data collection, analysis, and interpretation to align the estimand with the primary clinical question to add clarity and precision to support regulatory decision-making. In this article, we describe our experience as regulatory statisticians in review of Investigational New Drug protocols and statistical analysis plans, with an emphasis on trials used to support substantial evidence of effectiveness in New Drug Applications and Biologic License Applications. Our intent is to describe our experience with this powerful and effective framework tool, to align the clinical trial’s primary objective with its analysis outcomes and interpretation.
ICH E9(R1)评估指南是行业和监管统计人员创建和审查方案设计和分析计划的关键工具。该框架被描述为有助于改进研究设计、并发事件处理、数据收集、分析和解释，以使评估与主要临床问题保持一致，从而增加清晰度和准确性，以支持监管决策。在本文中，我们描述了我们作为监管统计学家在审查新药研究方案和统计分析计划方面的经验，重点是用于支持新药申请和生物许可证申请中有效性的实质性证据的试验。我们的目的是描述我们使用这个强大而有效的框架工具的经验，使临床试验的主要目标与其分析结果和解释保持一致。
{"title":"What Can Be Achieved with the Estimand Framework?","authors":"Susan Mayo, Yongman Kim","doi":"10.1080/19466315.2023.2173645","DOIUrl":"https://doi.org/10.1080/19466315.2023.2173645","url":null,"abstract":"Abstract The ICH E9(R1) guidance on estimands is a key tool for the creation and review of protocol design and analysis planning, for both industry and regulatory statisticians. The framework has been described as useful for improving study design, intercurrent event handling, data collection, analysis, and interpretation to align the estimand with the primary clinical question to add clarity and precision to support regulatory decision-making. In this article, we describe our experience as regulatory statisticians in review of Investigational New Drug protocols and statistical analysis plans, with an emphasis on trials used to support substantial evidence of effectiveness in New Drug Applications and Biologic License Applications. Our intent is to describe our experience with this powerful and effective framework tool, to align the clinical trial’s primary objective with its analysis outcomes and interpretation.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"549 - 553"},"PeriodicalIF":1.8,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42953720","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}
Pub Date : 2023-01-18DOI: 10.1080/19466315.2023.2169752
Arkendra De
Abstract The application of Artificial Intelligence to medical testing has received much attention in recent years, as evidenced by the flurry of published studies describing Artificial Intelligence software developed to solve problems in medical testing. While this recent activity is exciting, developed Artificial Intelligence medical tests ultimately can only be considered as candidates for widespread use if these tests demonstrate good performance in pivotal clinical studies. What are pivotal clinical studies for Artificial Intelligence medical tests aimed for widespread use? What are some of the major considerations and challenges for assessing performance of these tests in this context? What are some of the outstanding areas where statisticians, in collaboration with professionals outside the statistical community, could help in this endeavor? This article addresses these questions. This article is meant to appeal to a broad audience with varying levels of statistical and medical testing knowledge so that inter-disciplinary collaboration could be enhanced.
{"title":"Statistical Considerations and Challenges for Pivotal Clinical Studies of Artificial Intelligence Medical Tests for Widespread Use: Opportunities for Inter-Disciplinary Collaboration","authors":"Arkendra De","doi":"10.1080/19466315.2023.2169752","DOIUrl":"https://doi.org/10.1080/19466315.2023.2169752","url":null,"abstract":"Abstract The application of Artificial Intelligence to medical testing has received much attention in recent years, as evidenced by the flurry of published studies describing Artificial Intelligence software developed to solve problems in medical testing. While this recent activity is exciting, developed Artificial Intelligence medical tests ultimately can only be considered as candidates for widespread use if these tests demonstrate good performance in pivotal clinical studies. What are pivotal clinical studies for Artificial Intelligence medical tests aimed for widespread use? What are some of the major considerations and challenges for assessing performance of these tests in this context? What are some of the outstanding areas where statisticians, in collaboration with professionals outside the statistical community, could help in this endeavor? This article addresses these questions. This article is meant to appeal to a broad audience with varying levels of statistical and medical testing knowledge so that inter-disciplinary collaboration could be enhanced.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"476 - 490"},"PeriodicalIF":1.8,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41493376","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}
Pub Date : 2023-01-09DOI: 10.1080/19466315.2023.2166099
Olga V. Marchenko, R. Sridhara, Qi Jiang, Elizabeth Barksdale, Y. Ando, D. D. Alwis, Katie Brown, L. Fernandes, M. V. van Bussel, Qiuyi Choo, M. Coory, E. Garrett-Mayer, T. Gwise, Lorenzo Hess, Rong Liu, S. Mandrekar, D. Ouellet, J. Pinheiro, M. Posch, N. Rahman, K. Rantell, A. Raven, Sarem Sarem, S. Sen, M. Shah, Y. Shen, Richard Simon, M. Theoret, Ying Yuan, R. Pazdur
Abstract The article provides a summary of discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forums on March 18th, June 10th, and July 8th of 2021, organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group in coordination with the U.S. Food and Drug Administration (FDA) Oncology Center of Excellence and the LUNGevity Foundation. Diverse stakeholders including oncologists, patient advocates, experts from regulatory agencies across the world, academicians, and representatives from the pharmaceutical industry engaged in a lively discussion on strategies for and designs of dose-optimization studies in cancer drug development. Dose-optimization is one of the major challenges in oncology drug development. The discussions were focused on considerations in designing dose-optimization studies of products for treatment of cancer patients in pre-approval and post-approval stages. Presenters and panelists discussed diverse ideas and methods and agreed that a shift in paradigm is required in oncology drug development that should improve dose optimization while not unnecessarily delaying patient access to potentially efficacious new treatments.
{"title":"Designing Dose-Optimization Studies in Cancer Drug Development: Discussions with Regulators","authors":"Olga V. Marchenko, R. Sridhara, Qi Jiang, Elizabeth Barksdale, Y. Ando, D. D. Alwis, Katie Brown, L. Fernandes, M. V. van Bussel, Qiuyi Choo, M. Coory, E. Garrett-Mayer, T. Gwise, Lorenzo Hess, Rong Liu, S. Mandrekar, D. Ouellet, J. Pinheiro, M. Posch, N. Rahman, K. Rantell, A. Raven, Sarem Sarem, S. Sen, M. Shah, Y. Shen, Richard Simon, M. Theoret, Ying Yuan, R. Pazdur","doi":"10.1080/19466315.2023.2166099","DOIUrl":"https://doi.org/10.1080/19466315.2023.2166099","url":null,"abstract":"Abstract The article provides a summary of discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forums on March 18th, June 10th, and July 8th of 2021, organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group in coordination with the U.S. Food and Drug Administration (FDA) Oncology Center of Excellence and the LUNGevity Foundation. Diverse stakeholders including oncologists, patient advocates, experts from regulatory agencies across the world, academicians, and representatives from the pharmaceutical industry engaged in a lively discussion on strategies for and designs of dose-optimization studies in cancer drug development. Dose-optimization is one of the major challenges in oncology drug development. The discussions were focused on considerations in designing dose-optimization studies of products for treatment of cancer patients in pre-approval and post-approval stages. Presenters and panelists discussed diverse ideas and methods and agreed that a shift in paradigm is required in oncology drug development that should improve dose optimization while not unnecessarily delaying patient access to potentially efficacious new treatments.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"697 - 703"},"PeriodicalIF":1.8,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48005392","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}
Pub Date : 2023-01-02DOI: 10.1080/19466315.2022.2152090
N. Flournoy
CONTACT Nancy Flournoy flournoyn@missouri.edu Department of Statistics, University of Missouri (emerita), Columbia. The efforts arose from working groups formed during a NISS Ingram Olkin Forum series on the following topics: (1)Estimands and Missing Data, (2) The Role of Randomization Tests, (3) Methods to Cope with Information Loss and the Use of Auxiliary Sources of Data and (4) Bayes and Frequentist Approaches to Rescuing Disrupted Trials. These groups consider how existing methods can be applied in the context of unplanned clinical trial disruptions and uncover unsolved issues requiring further research. In addition to introducing you to these research projects, I am pleased to provide a brief introduction to the NISS Ingram Olkin Forums. The National Institute of Statistical Sciences (NISS) created Ingram Olkin Forums (IOFs) to foster Statistics Serving Society (S3) in memory of Professor Ingram Olkin. Motivated by the aspirations set forth by Olkin et al. (1990), each forum focuses on a current societal issue that might benefit from new or renewed attention from the statistical community. IOFs aim to bring the latest innovations in statistical methodology and data science into new research and public policy collaborations, working to accelerate the development of innovative approaches that impact societal problems. As a Forum brings a particular group of experts together for the first time to consider an issue, new energy and synergy is expected to produce a flurry of new ideas and approaches. The inaugural IOF was held in June 1919 on Gun Violence, prior to the arrival of the Covid-19 pandemic. Knowing that many statisticians would use their expertise to monitor the pandemic and to design vaccine and therapeutic trials, the IOF Committee looked for a need that might be neglected and decided to host an online IOF on Unplanned Clinical Trial Disruptions. A major concern in moving online was not to get stuck with one-directional webinars, but to get statisticians and other scientists who did not know each other previously to work together without meeting in-person. I am delighted to announce four papers resulting from this IOF will appear in Statistics in Biopharmaceutical Research. NISS is very happy with how well the IOF on Unplanned Clinical Trial Disruptions met its S3 objectives, with enthusiastic collegiality and productivity, and although in-person and hybrid launches will again be possible, this IOF is now NISS’s model.
{"title":"The NISS Ingram Olkin Forum on Unplanned Clinical Trial Disruptions","authors":"N. Flournoy","doi":"10.1080/19466315.2022.2152090","DOIUrl":"https://doi.org/10.1080/19466315.2022.2152090","url":null,"abstract":"CONTACT Nancy Flournoy flournoyn@missouri.edu Department of Statistics, University of Missouri (emerita), Columbia. The efforts arose from working groups formed during a NISS Ingram Olkin Forum series on the following topics: (1)Estimands and Missing Data, (2) The Role of Randomization Tests, (3) Methods to Cope with Information Loss and the Use of Auxiliary Sources of Data and (4) Bayes and Frequentist Approaches to Rescuing Disrupted Trials. These groups consider how existing methods can be applied in the context of unplanned clinical trial disruptions and uncover unsolved issues requiring further research. In addition to introducing you to these research projects, I am pleased to provide a brief introduction to the NISS Ingram Olkin Forums. The National Institute of Statistical Sciences (NISS) created Ingram Olkin Forums (IOFs) to foster Statistics Serving Society (S3) in memory of Professor Ingram Olkin. Motivated by the aspirations set forth by Olkin et al. (1990), each forum focuses on a current societal issue that might benefit from new or renewed attention from the statistical community. IOFs aim to bring the latest innovations in statistical methodology and data science into new research and public policy collaborations, working to accelerate the development of innovative approaches that impact societal problems. As a Forum brings a particular group of experts together for the first time to consider an issue, new energy and synergy is expected to produce a flurry of new ideas and approaches. The inaugural IOF was held in June 1919 on Gun Violence, prior to the arrival of the Covid-19 pandemic. Knowing that many statisticians would use their expertise to monitor the pandemic and to design vaccine and therapeutic trials, the IOF Committee looked for a need that might be neglected and decided to host an online IOF on Unplanned Clinical Trial Disruptions. A major concern in moving online was not to get stuck with one-directional webinars, but to get statisticians and other scientists who did not know each other previously to work together without meeting in-person. I am delighted to announce four papers resulting from this IOF will appear in Statistics in Biopharmaceutical Research. NISS is very happy with how well the IOF on Unplanned Clinical Trial Disruptions met its S3 objectives, with enthusiastic collegiality and productivity, and although in-person and hybrid launches will again be possible, this IOF is now NISS’s model.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"92 - 93"},"PeriodicalIF":1.8,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44043081","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}
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