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":" ","pages":""},"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":"15 1","pages":"458 - 467"},"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 : 2023-05-09DOI: 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":" ","pages":""},"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":" ","pages":""},"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":" ","pages":""},"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}
Pub Date : 2023-04-03DOI: 10.1080/19466315.2023.2203007
Y. Shen, Sirisha L. Mushti, Flora Mulkey, T. Gwise, Xin Wang, Jiaxi Zhou, Xin Gao, Shenghui Tang, M. Theoret, R. Pazdur, R. Sridhara
This rejoinder continues a discussion initiated by the Oncology Center of Excellence’s call (2016–2017) for statistical approaches to address the problem of assessing treatment effects in the presence of non-proportional hazards (NPH) (Duke-Margolis Workshop 2018; Lin et al. 2020a, Lin et al. 2020b; Roychoudhury et al. 2021). The publication of the method was met with much discussion, several commentary articles (Freidlin and Korn 2019; Bartlett et al. 2020; Lin et al. 2020a; Magirr and Burman 2021; Roychoudhury et al. 2021) and a rejoinder by the MaxCombo test coauthors (Lin et al. 2020b). After consideration of the proposed method and review of its accompanying responses and rejoinder, we expressed our views on the MaxCombo tests and provided general thoughts on design issues when NPH is expected in our article (Shen et al. 2021). In response to the publication of our article, the Cross-PhRMA working group (Lin et al. 2023) and Posch, Ristl, and König (2022) published additional commentary providing further clarification and views. We appreciate the great interest in NPH issues in the regulatory statistical community and would like to take this opportunity to provide additional clarifications and comments. Although the primary objective of our 2021 article was to focus on the MaxCombo test, as noted by Lin et al. (2023), a number of the issues are more general and are equally applicable when using many other methods and testing statistics, for example, the difficulty in interpretation or failure to incorporate underlying reasons of NPH. In fact, recognizing the shortcomings of the more commonly used tests such as log-rank test, etc., FDA initiated the dialogue and invited PhRMA to come together to develop methodology to address this issue. The MaxCombo test is presented as representing a flexible testing procedure impervious to a variety of shapes of curves under NPH. Cross-PhRMA working group suggested a 3-step method for evaluation of treatment effect when NPH is expected (Lin et al. 2020a, Lin et al. 2020b, Roychoudhury et al. 2021). Lin et al. (2023) clarified that in this scenario, a successful treatment effect would not be claimed based solely on results from the MaxCombo test, as the Cross-PhRMA working group recommends such decisions be based on the totality of data
该反驳继续了肿瘤卓越中心(2016-2017)发起的关于统计方法的讨论，以解决在存在非比例风险(NPH)的情况下评估治疗效果的问题(Duke-Margolis Workshop 2018;Lin et al. 2020a, Lin et al. 2020b;Roychoudhury et al. 2021)。该方法的发表引起了很多讨论，几篇评论文章(Freidlin and Korn 2019;Bartlett et al. 2020;Lin等。2020a;Magirr and Burman 2021;Roychoudhury等人，2021)和MaxCombo测试合著者的反驳(Lin等人，2020b)。在考虑了提议的方法并审查了相关的回应和反驳后，我们在文章中表达了我们对MaxCombo测试的看法，并就NPH预期时的设计问题提供了总体思路(Shen et al. 2021)。作为对我们文章发表的回应，Cross-PhRMA工作组(Lin et al. 2023)、Posch、Ristl和König(2022)发表了额外的评论，提供了进一步的澄清和观点。我们感谢监管统计界对NPH问题的极大兴趣，并希望借此机会提供额外的澄清和评论。正如Lin等人(2023)所指出的，尽管我们2021年文章的主要目标是关注MaxCombo测试，但许多问题更为普遍，在使用许多其他方法和测试统计数据时同样适用，例如，难以解释或未能纳入NPH的潜在原因。事实上，认识到更常用的测试(如log-rank测试等)的缺点，FDA发起了对话，并邀请PhRMA一起制定解决这一问题的方法。MaxCombo测试代表了一种灵活的测试程序，不受NPH下各种形状曲线的影响。跨phrma工作组提出了一种评估NPH预期治疗效果的三步法(Lin et al. 2020a, Lin et al. 2020b, Roychoudhury et al. 2021)。Lin等人(2023)澄清说，在这种情况下，不能仅仅根据MaxCombo测试的结果来宣称成功的治疗效果，因为Cross-PhRMA工作组建议基于整体数据做出此类决定
{"title":"Rejoinder to Comments on “Non-Proportional Hazards – An Evaluation of the MaxCombo Test in Cancer Clinical Trials”","authors":"Y. Shen, Sirisha L. Mushti, Flora Mulkey, T. Gwise, Xin Wang, Jiaxi Zhou, Xin Gao, Shenghui Tang, M. Theoret, R. Pazdur, R. Sridhara","doi":"10.1080/19466315.2023.2203007","DOIUrl":"https://doi.org/10.1080/19466315.2023.2203007","url":null,"abstract":"This rejoinder continues a discussion initiated by the Oncology Center of Excellence’s call (2016–2017) for statistical approaches to address the problem of assessing treatment effects in the presence of non-proportional hazards (NPH) (Duke-Margolis Workshop 2018; Lin et al. 2020a, Lin et al. 2020b; Roychoudhury et al. 2021). The publication of the method was met with much discussion, several commentary articles (Freidlin and Korn 2019; Bartlett et al. 2020; Lin et al. 2020a; Magirr and Burman 2021; Roychoudhury et al. 2021) and a rejoinder by the MaxCombo test coauthors (Lin et al. 2020b). After consideration of the proposed method and review of its accompanying responses and rejoinder, we expressed our views on the MaxCombo tests and provided general thoughts on design issues when NPH is expected in our article (Shen et al. 2021). In response to the publication of our article, the Cross-PhRMA working group (Lin et al. 2023) and Posch, Ristl, and König (2022) published additional commentary providing further clarification and views. We appreciate the great interest in NPH issues in the regulatory statistical community and would like to take this opportunity to provide additional clarifications and comments. Although the primary objective of our 2021 article was to focus on the MaxCombo test, as noted by Lin et al. (2023), a number of the issues are more general and are equally applicable when using many other methods and testing statistics, for example, the difficulty in interpretation or failure to incorporate underlying reasons of NPH. In fact, recognizing the shortcomings of the more commonly used tests such as log-rank test, etc., FDA initiated the dialogue and invited PhRMA to come together to develop methodology to address this issue. The MaxCombo test is presented as representing a flexible testing procedure impervious to a variety of shapes of curves under NPH. Cross-PhRMA working group suggested a 3-step method for evaluation of treatment effect when NPH is expected (Lin et al. 2020a, Lin et al. 2020b, Roychoudhury et al. 2021). Lin et al. (2023) clarified that in this scenario, a successful treatment effect would not be claimed based solely on results from the MaxCombo test, as the Cross-PhRMA working group recommends such decisions be based on the totality of data","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"315 - 317"},"PeriodicalIF":1.8,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47685200","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 : 2023-04-03DOI: 10.1080/19466315.2023.2200113
T. Hamasaki
In several disease areas, such as cardiovascular disease, oncology/cancer or HIV, clinical trials often collect and analyze multiple time-to-event (or survival) outcomes from patients to assess the effects of interventions. Methods for time-to-event outcomes are more complex than for binary or continuous outcomes. The design, monitoring, analysis and reporting of clinical trials with time-to-event outcomes (time-to-event clinical trials) will require considerable care. A common practice in time-to-event clinical trials is to first create a composite endpoint that combines several clinically relevant time-to-event outcomes (e.g., major adverse cardiovascular events (MACE), consisting of death, myocardial infarction, and stroke in cardiovascular disease; progression free survival (PFS) consisting of time-to-progression and overall survival), and then to perform a time-to-first-event analysis for the composite endpoint. The advantages and challenges of using composite endpoints are well known and have been discussed in the statistical and medical literature. Recently, many statisticians have attempted to redefine the estimand(s) of interest to capture the effects of interventions and the corresponding estimators of the estimand(s) (statistical methods) since the implementation of the estimand framework highlighted in the ICH-E9(R1) guideline (ICH 2019). Common survival analysis methods, such as Kaplan-Meier method, log-rank test, or Cox proportional hazards regression, have many strengths and are well accepted in practice. However, there are situations in which they may not be feasible or provide reliable results. The common methods are based
{"title":"Editor’s Note: Special Section on Estimands, Design and Analysis of Clinical Trials with Time-to-Event Outcomes","authors":"T. Hamasaki","doi":"10.1080/19466315.2023.2200113","DOIUrl":"https://doi.org/10.1080/19466315.2023.2200113","url":null,"abstract":"In several disease areas, such as cardiovascular disease, oncology/cancer or HIV, clinical trials often collect and analyze multiple time-to-event (or survival) outcomes from patients to assess the effects of interventions. Methods for time-to-event outcomes are more complex than for binary or continuous outcomes. The design, monitoring, analysis and reporting of clinical trials with time-to-event outcomes (time-to-event clinical trials) will require considerable care. A common practice in time-to-event clinical trials is to first create a composite endpoint that combines several clinically relevant time-to-event outcomes (e.g., major adverse cardiovascular events (MACE), consisting of death, myocardial infarction, and stroke in cardiovascular disease; progression free survival (PFS) consisting of time-to-progression and overall survival), and then to perform a time-to-first-event analysis for the composite endpoint. The advantages and challenges of using composite endpoints are well known and have been discussed in the statistical and medical literature. Recently, many statisticians have attempted to redefine the estimand(s) of interest to capture the effects of interventions and the corresponding estimators of the estimand(s) (statistical methods) since the implementation of the estimand framework highlighted in the ICH-E9(R1) guideline (ICH 2019). Common survival analysis methods, such as Kaplan-Meier method, log-rank test, or Cox proportional hazards regression, have many strengths and are well accepted in practice. However, there are situations in which they may not be feasible or provide reliable results. The common methods are based","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"237 - 237"},"PeriodicalIF":1.8,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48172110","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 : 2023-04-03DOI: 10.1080/19466315.2022.2103180
Ray S. Lin, P. Mukhopadhyay, Satrajit Roychoudhury, K. Anderson, Tianle Hu, Bo Huang, L. F. León, J. Liao, Ji Lin, Rong Liu, Xiaodong Luo, Yabing Mai, R. Qin, K. Tatsuoka, Yang Wang, Jiabu Ye, Jian Zhu, Tai-Tsang Chen, R. Iacona
aGenentech/Roche, South San Francisco, CA; bOtsuka America Pharmaceuticals, Inc, Rockville, MD, 20850; cPfizer Inc, New York, NY; dMerck & Co., Inc, Kenilworth, NJ; eSarepta Therapeutics, Cambridge, MA; fPfizer Inc, Groton, CT; gIncyte Corporation, Wilmington, DE; hSanofi US, Cambridge, MA; iBristolMyers Squibb, Berkeley Heights, NJ; jSanofi US, Bridgewater, NJ; kBoehringer Ingelheim, Shanghai, China; lJanssen Research & Development, LLC, Raritan, NJ; mSanten Pharmaceuticals, Emeryville, CA; nZ&W Consulting, Chester Springs, PA; oServier Pharmaceuticals, Boston, MA; pGSK, Collegeville, PA; qAstra Zeneca, Washington, DC; rThe Cross-Pharma NPH working group includes all the authors of this manuscript as listed above and the following members who have contributed tremendously to this work: Prabhu Bhagavatheeswaran, Julie Cong, Margarida Geraldes, Dominik Heinzmann, Yifan Huang, Zhengrong Li, Honglu Liu, Jane Qian, Xuejing Wang, Li-an Xu, Luping Zhao
{"title":"Comment on “Non-Proportional Hazards – an Evaluation of the MaxCombo Test in Cancer Clinical Trials” by the Cross-Pharma Non-Proportional Hazards Working Group","authors":"Ray S. Lin, P. Mukhopadhyay, Satrajit Roychoudhury, K. Anderson, Tianle Hu, Bo Huang, L. F. León, J. Liao, Ji Lin, Rong Liu, Xiaodong Luo, Yabing Mai, R. Qin, K. Tatsuoka, Yang Wang, Jiabu Ye, Jian Zhu, Tai-Tsang Chen, R. Iacona","doi":"10.1080/19466315.2022.2103180","DOIUrl":"https://doi.org/10.1080/19466315.2022.2103180","url":null,"abstract":"aGenentech/Roche, South San Francisco, CA; bOtsuka America Pharmaceuticals, Inc, Rockville, MD, 20850; cPfizer Inc, New York, NY; dMerck & Co., Inc, Kenilworth, NJ; eSarepta Therapeutics, Cambridge, MA; fPfizer Inc, Groton, CT; gIncyte Corporation, Wilmington, DE; hSanofi US, Cambridge, MA; iBristolMyers Squibb, Berkeley Heights, NJ; jSanofi US, Bridgewater, NJ; kBoehringer Ingelheim, Shanghai, China; lJanssen Research & Development, LLC, Raritan, NJ; mSanten Pharmaceuticals, Emeryville, CA; nZ&W Consulting, Chester Springs, PA; oServier Pharmaceuticals, Boston, MA; pGSK, Collegeville, PA; qAstra Zeneca, Washington, DC; rThe Cross-Pharma NPH working group includes all the authors of this manuscript as listed above and the following members who have contributed tremendously to this work: Prabhu Bhagavatheeswaran, Julie Cong, Margarida Geraldes, Dominik Heinzmann, Yifan Huang, Zhengrong Li, Honglu Liu, Jane Qian, Xuejing Wang, Li-an Xu, Luping Zhao","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"312 - 314"},"PeriodicalIF":1.8,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43011148","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 : 2023-04-03DOI: 10.1080/19466315.2022.2108138
Matias Janvin, Jessica G. Young, M. J. Stensrud
Abstract We summarize what we consider to be the two main limitations of the “Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event” (Schmidli et al. 2022). First, the authors did not give detailed guidance on how to choose an appropriate estimand in light of subject-matter considerations. Reasoning about the mechanism by which treatment affects different types of events is central when selecting a causal estimand, and such reasoning can be grounded in the interventionist mediation literature. Second, the article also did not discuss the crucial task of identification when the aim is to study a causal question. Thereby, the authors omit important differences in the uncertainty of the assumptions needed to target each estimand by particular statistical methods. These assumptions have crucial implications for the confidence that can be placed in a given effect estimate, and for the planning and collection of relevant variables in the study design.
摘要我们总结了我们认为的“在终端事件存在的情况下对重复事件终点的估计”的两个主要限制(Schmidli et al. 2022)。首先，作者没有就如何根据主题事项考虑选择适当的估计给出详细的指导。在选择因果估计时，关于治疗影响不同类型事件的机制的推理是核心，这种推理可以在干预主义调解文献中建立基础。其次，本文也没有讨论识别的关键任务，当目的是研究一个因果问题。因此，作者通过特定的统计方法忽略了针对每个估计所需的假设不确定性中的重要差异。这些假设对于给定效果估计的置信度以及研究设计中相关变量的规划和收集具有至关重要的意义。
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