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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
Pub Date : 2023-06-15DOI: 10.1080/19466315.2023.2225451
O. Sverdlov, Kerstine Carter, R. Hilgers, C. Everett, V. Berger, Yuqun Abigail Luo, Jonathan J. Chipman, Y. Ryeznik, Jennifer Ross, Ruth Knight, Kazumi Yamada
{"title":"Which Randomization Methods Are Used Most Frequently in Clinical Trials? Results of a Survey by the Randomization Working Group","authors":"O. Sverdlov, Kerstine Carter, R. Hilgers, C. Everett, V. Berger, Yuqun Abigail Luo, Jonathan J. Chipman, Y. Ryeznik, Jennifer Ross, Ruth Knight, Kazumi Yamada","doi":"10.1080/19466315.2023.2225451","DOIUrl":"https://doi.org/10.1080/19466315.2023.2225451","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48274407","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 : 2023-06-09DOI: 10.1080/19466315.2023.2224259
F. Bretz, J. Greenhouse
Abstract The development of new drugs has evolved dramatically over the past decade. Advances in technology enable scientists to generate “big data” faster than ever before. The availability of complex, high-volume data in turn creates demand for innovative quantitative solutions and tools in a rapidly evolving landscape. As a result, the role of the statistical scientist in collaborative research has never been more important. Reflecting on these changes, Cox (2012) wrote, “…[A]lthough the tactics of statistical analysis have been utterly changed… the strategy of research design and analysis has been much less affected…” In this article, we argue that the practice of statistics is built on the foundation of good statistical thinking and consists of a complex combination of problem-solving skills, the essence of what Cox meant by the “strategy of research.” Although others have highlighted the role of statistical thinking in research design and analysis, in the age of data science, machine learning and artificial intelligence, it cannot be emphasized enough. We outline four general steps that contribute to good statistical thinking and illustrate them with five use cases (“vignettes”) as well as a detailed case study discussion from a maintenance therapy clinical trial for depression.
{"title":"The Role of Statistical Thinking in Biopharmaceutical Research","authors":"F. Bretz, J. Greenhouse","doi":"10.1080/19466315.2023.2224259","DOIUrl":"https://doi.org/10.1080/19466315.2023.2224259","url":null,"abstract":"Abstract The development of new drugs has evolved dramatically over the past decade. Advances in technology enable scientists to generate “big data” faster than ever before. The availability of complex, high-volume data in turn creates demand for innovative quantitative solutions and tools in a rapidly evolving landscape. As a result, the role of the statistical scientist in collaborative research has never been more important. Reflecting on these changes, Cox (2012) wrote, “…[A]lthough the tactics of statistical analysis have been utterly changed… the strategy of research design and analysis has been much less affected…” In this article, we argue that the practice of statistics is built on the foundation of good statistical thinking and consists of a complex combination of problem-solving skills, the essence of what Cox meant by the “strategy of research.” Although others have highlighted the role of statistical thinking in research design and analysis, in the age of data science, machine learning and artificial intelligence, it cannot be emphasized enough. We outline four general steps that contribute to good statistical thinking and illustrate them with five use cases (“vignettes”) as well as a detailed case study discussion from a maintenance therapy clinical trial for depression.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46212063","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 : 2023-05-18DOI: 10.1080/19466315.2023.2215735
Qing Liu, Wenxi Yu, Leiwen Gao, Xun Jiang, Michael Wolf, M. Mo
{"title":"A Bayesian Adaptive Umbrella Trial Design with Robust Information Borrowing for Screening Multiple Combination Therapies","authors":"Qing Liu, Wenxi Yu, Leiwen Gao, Xun Jiang, Michael Wolf, M. Mo","doi":"10.1080/19466315.2023.2215735","DOIUrl":"https://doi.org/10.1080/19466315.2023.2215735","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49073402","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 : 2023-05-09DOI: 10.1080/19466315.2023.2211538
B. Hofner, E. Asikanius, W. Jacquet, T. Framke, K. Oude Rengerink, L. Aguirre Dávila, Maria Grünewald, Florian Klinglmüller, M. Posch, Finbarr P. Leacy, Thomas Lang, Armin Koch, J. Zinserling, Kit Roes
The COVID-19 pandemic triggered an unprecedented research effort to develop vaccines and therapeutics. Urgency dictated that development and regulatory assessment were accelerated, while maintaining all standards for quality, safety and efficacy. To speed up evaluation the European Medicines Agency (EMA) implemented "rolling reviews” allowing developers to submit data for assessment as they became available.We discuss the clinical trial designs and the applied statistical approaches in vaccine efficacy trials, focusing on aspects such as multiple testing, interim and updated analyses, and reporting of results for the first four vaccines recommended for approval by the EMA. The fast accrual of COVID-19 cases in the clinical vaccine efficacy trials led to multiple data updates within a short time frame, which had consequences for the evaluation and interpretation of results. Key trial results are discussed in the light of these aspects. Notably, the aspects discussed did not affect the benefit/risk relationship in a meaningful way, which was clearly positive for all four vaccines.Assessment of the development and evaluation of the four vaccine trials during the pandemic has led to a proposal for standardised terminology for trials with multiple analyses and a recommendation to appropriately pre-plan the timing of primary and updated analyses. For the reporting of updated estimates of vaccine efficacy, we discuss how to best describe the uncertainty around estimates of vaccine efficacy (e.g., via confidence intervals). Finally, we briefly highlight the benefit of a comprehensive discussion on estimands for vaccine efficacy trials. [ FROM AUTHOR] Copyright of Statistics in Biopharmaceutical Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
{"title":"Vaccine development during a pandemic: General lessons for clinical trial design","authors":"B. Hofner, E. Asikanius, W. Jacquet, T. Framke, K. Oude Rengerink, L. Aguirre Dávila, Maria Grünewald, Florian Klinglmüller, M. Posch, Finbarr P. Leacy, Thomas Lang, Armin Koch, J. Zinserling, Kit Roes","doi":"10.1080/19466315.2023.2211538","DOIUrl":"https://doi.org/10.1080/19466315.2023.2211538","url":null,"abstract":"The COVID-19 pandemic triggered an unprecedented research effort to develop vaccines and therapeutics. Urgency dictated that development and regulatory assessment were accelerated, while maintaining all standards for quality, safety and efficacy. To speed up evaluation the European Medicines Agency (EMA) implemented \"rolling reviews” allowing developers to submit data for assessment as they became available.We discuss the clinical trial designs and the applied statistical approaches in vaccine efficacy trials, focusing on aspects such as multiple testing, interim and updated analyses, and reporting of results for the first four vaccines recommended for approval by the EMA. The fast accrual of COVID-19 cases in the clinical vaccine efficacy trials led to multiple data updates within a short time frame, which had consequences for the evaluation and interpretation of results. Key trial results are discussed in the light of these aspects. Notably, the aspects discussed did not affect the benefit/risk relationship in a meaningful way, which was clearly positive for all four vaccines.Assessment of the development and evaluation of the four vaccine trials during the pandemic has led to a proposal for standardised terminology for trials with multiple analyses and a recommendation to appropriately pre-plan the timing of primary and updated analyses. For the reporting of updated estimates of vaccine efficacy, we discuss how to best describe the uncertainty around estimates of vaccine efficacy (e.g., via confidence intervals). Finally, we briefly highlight the benefit of a comprehensive discussion on estimands for vaccine efficacy trials. [ FROM AUTHOR] Copyright of Statistics in Biopharmaceutical Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45206355","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 : 2023-05-09DOI: 10.1080/19466315.2023.2208061
O. Kuznetsova
{"title":"Minimizing Selection Bias Under the Blackwell and Hodges Model with an Equal Allocation Procedure in a Symmetric Allocation Space","authors":"O. Kuznetsova","doi":"10.1080/19466315.2023.2208061","DOIUrl":"https://doi.org/10.1080/19466315.2023.2208061","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46308530","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 : 2023-04-27DOI: 10.1080/19466315.2023.2241415
Neal Thomas
Stratification in both the design and analysis of randomized clinical trials is common. Despite features in automated randomization systems to re-confirm the stratifying variables, incorrect values of these variables may be entered. These errors are often detected during subsequent data collection and verification. Questions remain about whether to use the mis-reported initial stratification or the corrected values in subsequent analyses. It is shown that the likelihood function resulting from the design of randomized clinical trials supports the use of the corrected values. New definitions are proposed that characterize misclassification errors as `ignorable' and `non-ignorable'. Ignorable errors may depend on the correct strata and any other modeled baseline covariates, but they are otherwise unrelated to potential treatment outcomes. Data management review suggests most misclassification errors are arbitrarily produced by distracted investigators, so they are ignorable or at most weakly dependent on measured and unmeasured baseline covariates. Ignorable misclassification errors may produce a small increase in standard errors, but other properties of the planned analyses are unchanged (e.g., unbiasedness, confidence interval coverage). It is shown that unbiased linear estimation in the absence of misclassification errors remains unbiased when there are non-ignorable misclassification errors, and the corresponding confidence intervals based on the corrected strata values are conservative.
{"title":"A note on stratification errors in the analysis of clinical trials","authors":"Neal Thomas","doi":"10.1080/19466315.2023.2241415","DOIUrl":"https://doi.org/10.1080/19466315.2023.2241415","url":null,"abstract":"Stratification in both the design and analysis of randomized clinical trials is common. Despite features in automated randomization systems to re-confirm the stratifying variables, incorrect values of these variables may be entered. These errors are often detected during subsequent data collection and verification. Questions remain about whether to use the mis-reported initial stratification or the corrected values in subsequent analyses. It is shown that the likelihood function resulting from the design of randomized clinical trials supports the use of the corrected values. New definitions are proposed that characterize misclassification errors as `ignorable' and `non-ignorable'. Ignorable errors may depend on the correct strata and any other modeled baseline covariates, but they are otherwise unrelated to potential treatment outcomes. Data management review suggests most misclassification errors are arbitrarily produced by distracted investigators, so they are ignorable or at most weakly dependent on measured and unmeasured baseline covariates. Ignorable misclassification errors may produce a small increase in standard errors, but other properties of the planned analyses are unchanged (e.g., unbiasedness, confidence interval coverage). It is shown that unbiased linear estimation in the absence of misclassification errors remains unbiased when there are non-ignorable misclassification errors, and the corresponding confidence intervals based on the corrected strata values are conservative.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42800000","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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