Pub Date : 2023-08-24DOI: 10.1080/19466315.2023.2252150
Leroy Jide Ovbude, Luca Grassano, B. Cheuvart, F. Solmi
{"title":"Statistical inference for vaccine efficacy: a re-randomization procedure to analyse Poisson outcomes under covariate-adaptive randomization.","authors":"Leroy Jide Ovbude, Luca Grassano, B. Cheuvart, F. Solmi","doi":"10.1080/19466315.2023.2252150","DOIUrl":"https://doi.org/10.1080/19466315.2023.2252150","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46264250","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 : 2023-08-15DOI: 10.1080/19466315.2023.2239521
Kerstine Carter, A. Scheffold, Jone Renteria, V. Berger, Yuqun Abigail Luo, J. Chipman, O. Sverdlov
{"title":"Regulatory Guidance on Randomization and the Use of Randomization Tests in Clinical Trials: A Systematic Review","authors":"Kerstine Carter, A. Scheffold, Jone Renteria, V. Berger, Yuqun Abigail Luo, J. Chipman, O. Sverdlov","doi":"10.1080/19466315.2023.2239521","DOIUrl":"https://doi.org/10.1080/19466315.2023.2239521","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43510143","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 : 2023-08-04DOI: 10.1080/19466315.2023.2241399
Gu Mi, Yue-Jeng Yang, Z. Jin, Ji Lin, C. Lorenzato
{"title":"Enhancement of Basket Trial Designs with Incorporation of a Bayesian Three-Outcome Decision-Making Framework","authors":"Gu Mi, Yue-Jeng Yang, Z. Jin, Ji Lin, C. Lorenzato","doi":"10.1080/19466315.2023.2241399","DOIUrl":"https://doi.org/10.1080/19466315.2023.2241399","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43259712","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 : 2023-07-25DOI: 10.1080/19466315.2023.2241398
Qi Zhang, Yuqian Shen, H. Quan, P. Minini, Lin Wang
{"title":"A Case Study of 2-stage Seamless Adaptive Sample Size Re-Estimation Design with Efficacy Interim analysis When Slope is the Primary Endpoint","authors":"Qi Zhang, Yuqian Shen, H. Quan, P. Minini, Lin Wang","doi":"10.1080/19466315.2023.2241398","DOIUrl":"https://doi.org/10.1080/19466315.2023.2241398","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44395211","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 : 2023-07-25DOI: 10.1080/19466315.2023.2238650
R. Sridhara, Olga V. Marchenko, Qi Jiang, Elizabeth Barksdale, T. Alonzo, Anup K Amatya, David F. Arons, Alex Bliu, Qiuyi Choo, M. Coory, Martha Donoghue, L. Ehrlich, Leonardo Fabio Costa Filho, E. Fox, B. Freidlin, Nancy Goodman, D. Hawkins, D. Häring, Dominik Karres, E. Kolb, Helen Mao, Pallavi S. Mishra Kalyani, A. Naranjo, A. Pappo, M. Posch, Karen L. Price, A. Raven, K. Rantell, Lindsay Renfro, D. Rivera, Pourab Roy, Ming-wei Shan, Richard Simon, Sonia Singh, Malcolm Smith, M. Theoret, Marius Thomas, Z. Thomas, A. Thompson, Hong Tian, Y. Tymofyeyev, Jonathon Vallejo, K. Wathen, Jingjing Ye, R. Pazdur, G. Reaman
Abstract This article provides a summary of discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forum organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group in coordination with the US FDA Oncology Center of Excellence and LUNGevity Foundation on June 24, 2021, and January 13, 2022. Diverse stakeholders engaged in a discussion on how best to use various innovative clinical trial designs in designing future pediatric oncology trials. While standard randomized controlled trials are preferred to evaluate treatment effect in an unbiased manner, given the rarity of pediatric cancers, innovative strategies are needed to promote and assure timely cancer drug development in pediatric populations. The discussions highlighted the need to consider innovative designs with less stringent Type I error specification and Bayesian designs borrowing from external control data, or borrowing treatment effect information from adult data, or both. Such designs are available in the literature and some examples are summarized under the FDA Complex Innovative Trials Design Pilot Program (https://www.fda.gov/drugs/development-resources/complex-innovative-trial-design-meeting-program). Early consultation with global regulatory agencies for pediatric clinical trials can provide a better understanding of different features of the clinical trial design options for successful pediatric cancer drug development.
{"title":"Statistical Considerations in Pediatric Cancer Trials: Report of American Statistical Association Biopharmaceutical Section Open Forum Discussions","authors":"R. Sridhara, Olga V. Marchenko, Qi Jiang, Elizabeth Barksdale, T. Alonzo, Anup K Amatya, David F. Arons, Alex Bliu, Qiuyi Choo, M. Coory, Martha Donoghue, L. Ehrlich, Leonardo Fabio Costa Filho, E. Fox, B. Freidlin, Nancy Goodman, D. Hawkins, D. Häring, Dominik Karres, E. Kolb, Helen Mao, Pallavi S. Mishra Kalyani, A. Naranjo, A. Pappo, M. Posch, Karen L. Price, A. Raven, K. Rantell, Lindsay Renfro, D. Rivera, Pourab Roy, Ming-wei Shan, Richard Simon, Sonia Singh, Malcolm Smith, M. Theoret, Marius Thomas, Z. Thomas, A. Thompson, Hong Tian, Y. Tymofyeyev, Jonathon Vallejo, K. Wathen, Jingjing Ye, R. Pazdur, G. Reaman","doi":"10.1080/19466315.2023.2238650","DOIUrl":"https://doi.org/10.1080/19466315.2023.2238650","url":null,"abstract":"Abstract This article provides a summary of discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forum organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group in coordination with the US FDA Oncology Center of Excellence and LUNGevity Foundation on June 24, 2021, and January 13, 2022. Diverse stakeholders engaged in a discussion on how best to use various innovative clinical trial designs in designing future pediatric oncology trials. While standard randomized controlled trials are preferred to evaluate treatment effect in an unbiased manner, given the rarity of pediatric cancers, innovative strategies are needed to promote and assure timely cancer drug development in pediatric populations. The discussions highlighted the need to consider innovative designs with less stringent Type I error specification and Bayesian designs borrowing from external control data, or borrowing treatment effect information from adult data, or both. Such designs are available in the literature and some examples are summarized under the FDA Complex Innovative Trials Design Pilot Program (https://www.fda.gov/drugs/development-resources/complex-innovative-trial-design-meeting-program). Early consultation with global regulatory agencies for pediatric clinical trials can provide a better understanding of different features of the clinical trial design options for successful pediatric cancer drug development.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"1 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41670769","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 : 2023-07-03DOI: 10.1080/19466315.2022.2096688
Freda Cooner, R. Liao, Junjing Lin, Sophie Barthel, Y. Seifu, Shiling Ruan
Abstract Starting in early 2020, a fast-ravaging viral infection erupted and caused the COVID-19 (coronavirus disease of 2019) pandemic. The disease rapidly spread across the world and has altered people’s lifestyle since its first reporting. Many scientists and medical practitioners have strived to understand the disease and research for treatments and vaccines. As real-world data quickly accumulate, the general public reacts to new findings and government bodies enforce preventive measures accordingly. These actions subsequently alter the real-world data pattern and structure. It creates great challenges in interpreting this maze of data. This article delves into the specificity of COVID-19 real-world data; summarizes some existing COVID-19 databases and the disease modeling strategies; outlines potential trial designs incorporating real-world data to meet evidentiary requirements for treatment effect demonstration; and then presents a few case examples. It provides statistical considerations for real-world data utilization in understanding COVID-19 and finding potential treatments and preventive care.
{"title":"Leveraging Real-World Data in COVID-19 Response","authors":"Freda Cooner, R. Liao, Junjing Lin, Sophie Barthel, Y. Seifu, Shiling Ruan","doi":"10.1080/19466315.2022.2096688","DOIUrl":"https://doi.org/10.1080/19466315.2022.2096688","url":null,"abstract":"Abstract Starting in early 2020, a fast-ravaging viral infection erupted and caused the COVID-19 (coronavirus disease of 2019) pandemic. The disease rapidly spread across the world and has altered people’s lifestyle since its first reporting. Many scientists and medical practitioners have strived to understand the disease and research for treatments and vaccines. As real-world data quickly accumulate, the general public reacts to new findings and government bodies enforce preventive measures accordingly. These actions subsequently alter the real-world data pattern and structure. It creates great challenges in interpreting this maze of data. This article delves into the specificity of COVID-19 real-world data; summarizes some existing COVID-19 databases and the disease modeling strategies; outlines potential trial designs incorporating real-world data to meet evidentiary requirements for treatment effect demonstration; and then presents a few case examples. It provides statistical considerations for real-world data utilization in understanding COVID-19 and finding potential treatments and preventive care.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"582 - 595"},"PeriodicalIF":1.8,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42583569","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 : 2023-07-03DOI: 10.1080/19466315.2023.2210079
C. Gause, Jing Zhao
Abstract The FDA uses advisory committees and panels to obtain independent expert advice on scientific, technical, and policy matters. Advisory Committee Meetings (ACMs) may be convened if the agency has significant questions or concerns about clinical data submitted for review. Statisticians from both FDA and industry can play a key role in providing insight into the data under review in an ACM. This requires extensive preparation and planning which extends beyond the data provided in the submission package. In this article, we review the contributions of industry Statisticians in the planning, preparation and responses for ACMs.
{"title":"Experience with Advisory Committee Meeting Preparation and Execution","authors":"C. Gause, Jing Zhao","doi":"10.1080/19466315.2023.2210079","DOIUrl":"https://doi.org/10.1080/19466315.2023.2210079","url":null,"abstract":"Abstract The FDA uses advisory committees and panels to obtain independent expert advice on scientific, technical, and policy matters. Advisory Committee Meetings (ACMs) may be convened if the agency has significant questions or concerns about clinical data submitted for review. Statisticians from both FDA and industry can play a key role in providing insight into the data under review in an ACM. This requires extensive preparation and planning which extends beyond the data provided in the submission package. In this article, we review the contributions of industry Statisticians in the planning, preparation and responses for ACMs.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"574 - 581"},"PeriodicalIF":1.8,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47113069","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 : 2023-07-03DOI: 10.1080/19466315.2023.2224136
Bo Huang, G. Pennello
{"title":"Statistical Innovation in Healthcare: Celebrating the Past 40 Years and Looking Toward the Future–Special Issue for the 2021 Regulatory-Industry Statistics Workshop","authors":"Bo Huang, G. Pennello","doi":"10.1080/19466315.2023.2224136","DOIUrl":"https://doi.org/10.1080/19466315.2023.2224136","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"457 - 457"},"PeriodicalIF":1.8,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47933015","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 : 2023-07-03DOI: 10.1080/19466315.2022.2081601
Alexei C. Ionan, M. Paterniti, D. Mehrotra, John Scott, B. Ratitch, S. Collins, S. Gomatam, L. Nie, K. Rufibach, F. Bretz
Abstract The ICH E9 (R1) Addendum on “Estimands and Sensitivity Analysis in Clinical Trials (Step 4)” was finalized in November 2019 and subsequently implemented by many regulatory agencies, including FDA (May 2021). This article is based on a session organized to cover experience implementing the estimand framework, including its use, impact on drug/biologic development, common challenges and ways to address them, as well as keys to productive interdisciplinary collaboration.
ICH E9 (R1)附录“临床试验中的估计和敏感性分析(步骤4)”于2019年11月定稿，随后由包括FDA在内的许多监管机构实施(2021年5月)。这篇文章是基于一个会议的基础上组织的，该会议涵盖了实施评估框架的经验，包括它的使用，对药物/生物开发的影响，共同的挑战和解决这些挑战的方法，以及有效的跨学科合作的关键。
{"title":"Clinical and Statistical Perspectives on the ICH E9(R1) Estimand Framework Implementation","authors":"Alexei C. Ionan, M. Paterniti, D. Mehrotra, John Scott, B. Ratitch, S. Collins, S. Gomatam, L. Nie, K. Rufibach, F. Bretz","doi":"10.1080/19466315.2022.2081601","DOIUrl":"https://doi.org/10.1080/19466315.2022.2081601","url":null,"abstract":"Abstract The ICH E9 (R1) Addendum on “Estimands and Sensitivity Analysis in Clinical Trials (Step 4)” was finalized in November 2019 and subsequently implemented by many regulatory agencies, including FDA (May 2021). This article is based on a session organized to cover experience implementing the estimand framework, including its use, impact on drug/biologic development, common challenges and ways to address them, as well as keys to productive interdisciplinary collaboration.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"554 - 559"},"PeriodicalIF":1.8,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46136424","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 : 2023-06-30DOI: 10.1080/19466315.2023.2207487
Ya-Ching M. Hsieh, Leon Chang, Alfred M. Barron
Abstract Estimates of EC 50 1 from dose–response data play an important role in comparing drug potencies. When the sampling data of dose–response studies fail to follow a sigmoidal shaped curve, and the data display a biphasic property at higher dose levels where the response profile concaves and takes an inverted U-shape, this is known as the hook or prozone effect. To address this concern, some research investigators may pursue data removal. Others may choose to ignore the data shape and fit a model blindly. Unfortunately for both practices, the estimates of the fitting parameters, such as the EC 50, will be of poor quality and result in misleading inference. The authors propose the use of an empirical and novel extension of a sigmoid model to properly and effectively capture the information from all of the dose–response data, including that of the inverted U-shaped tail. Methods for using 3- and 4-parameter logistic models with examples, are discussed.
{"title":"A Novel Approach for Modeling Biphasic Dose–Response Curves","authors":"Ya-Ching M. Hsieh, Leon Chang, Alfred M. Barron","doi":"10.1080/19466315.2023.2207487","DOIUrl":"https://doi.org/10.1080/19466315.2023.2207487","url":null,"abstract":"Abstract Estimates of EC 50 1 from dose–response data play an important role in comparing drug potencies. When the sampling data of dose–response studies fail to follow a sigmoidal shaped curve, and the data display a biphasic property at higher dose levels where the response profile concaves and takes an inverted U-shape, this is known as the hook or prozone effect. To address this concern, some research investigators may pursue data removal. Others may choose to ignore the data shape and fit a model blindly. Unfortunately for both practices, the estimates of the fitting parameters, such as the EC 50, will be of poor quality and result in misleading inference. The authors propose the use of an empirical and novel extension of a sigmoid model to properly and effectively capture the information from all of the dose–response data, including that of the inverted U-shaped tail. Methods for using 3- and 4-parameter logistic models with examples, are discussed.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48961118","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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