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":null,"pages":null},"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: 10.1080/19466315.2023.2203007
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":null,"pages":null},"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: 10.1080/19466315.2022.2103180
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":null,"pages":null},"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。
{"title":"We Need Subject Matter Expertise to Choose and Identify Causal Estimands: Comment on “Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event”","authors":"Matias Janvin, Jessica G. Young, M. J. Stensrud","doi":"10.1080/19466315.2022.2108138","DOIUrl":"https://doi.org/10.1080/19466315.2022.2108138","url":null,"abstract":"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.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45443091","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: 10.1080/19466315.2023.2166098
Heinz Schmidli, James H. Roger, Mouna Akacha
{"title":"Rejoinder to Commentaries on “Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event”","authors":"Heinz Schmidli, James H. Roger, Mouna Akacha","doi":"10.1080/19466315.2023.2166098","DOIUrl":"https://doi.org/10.1080/19466315.2023.2166098","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135717238","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: 10.1080/19466315.2023.2200112
T. Hamasaki, Freda Cooner
We are pleased to announce the recipients of the 2023 Best Paper Award for the articles published in Statistics in Biopharmaceutical Research (SBR). The following five articles were selected from those published in the 2021 and 2022 issues. These articles exhibit excellent examples of current statistical advancements in biopharmaceutical research. In selecting the winners, the editors reflected SBRs goal of publishing articles that focus on the development of novel statistical methods, advanced applications of existing methods
{"title":"Statistics in Biopharmaceutical Research Best Papers Award 2023","authors":"T. Hamasaki, Freda Cooner","doi":"10.1080/19466315.2023.2200112","DOIUrl":"https://doi.org/10.1080/19466315.2023.2200112","url":null,"abstract":"We are pleased to announce the recipients of the 2023 Best Paper Award for the articles published in Statistics in Biopharmaceutical Research (SBR). The following five articles were selected from those published in the 2021 and 2022 issues. These articles exhibit excellent examples of current statistical advancements in biopharmaceutical research. In selecting the winners, the editors reflected SBRs goal of publishing articles that focus on the development of novel statistical methods, advanced applications of existing methods","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43517345","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: 10.1080/19466315.2023.2173644
David Chen, M. Petersen, H. Rytgaard, Randi Grøn, T. Lange, S. Rasmussen, R. Pratley, S. Marso, K. Kvist, J. Buse, M. J. van der Laan
Abstract The Hazard Ratio (HR) is a well-established treatment effect measure in randomized trials involving right-censored time-to-events, and the Cardiovascular Outcome Trials (CVOTs) conducted since the FDA’s 2008 guidance have indeed largely evaluated excess risk by estimating a Cox HR. On the other hand, the limitations of the Cox model and of the HR as a causal estimand are well known, and the FDA’s updated 2020 CVOT guidance invites us to reassess this default approach to survival analyses. We highlight the shortcomings of Cox HR-based analyses and present an alternative following the causal roadmap—moving in a principled way from a counterfactual causal question to identifying a statistical estimand, and finally to targeted estimation in a large statistical model. We show in simulations the robustness of Targeted Maximum Likelihood Estimation (TMLE) to informative censoring and model misspecification and demonstrate a targeted learning analogue of the original Cox HR-based analysis of the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results (LEADER) trial. We discuss the potential reliability, interpretability, and efficiency gains to be had by updating our survival methods to incorporate the recent decades of advancements in formal causal frameworks and efficient nonparametricestimation.
{"title":"Beyond the Cox Hazard Ratio: A Targeted Learning Approach to Survival Analysis in a Cardiovascular Outcome Trial Application","authors":"David Chen, M. Petersen, H. Rytgaard, Randi Grøn, T. Lange, S. Rasmussen, R. Pratley, S. Marso, K. Kvist, J. Buse, M. J. van der Laan","doi":"10.1080/19466315.2023.2173644","DOIUrl":"https://doi.org/10.1080/19466315.2023.2173644","url":null,"abstract":"Abstract The Hazard Ratio (HR) is a well-established treatment effect measure in randomized trials involving right-censored time-to-events, and the Cardiovascular Outcome Trials (CVOTs) conducted since the FDA’s 2008 guidance have indeed largely evaluated excess risk by estimating a Cox HR. On the other hand, the limitations of the Cox model and of the HR as a causal estimand are well known, and the FDA’s updated 2020 CVOT guidance invites us to reassess this default approach to survival analyses. We highlight the shortcomings of Cox HR-based analyses and present an alternative following the causal roadmap—moving in a principled way from a counterfactual causal question to identifying a statistical estimand, and finally to targeted estimation in a large statistical model. We show in simulations the robustness of Targeted Maximum Likelihood Estimation (TMLE) to informative censoring and model misspecification and demonstrate a targeted learning analogue of the original Cox HR-based analysis of the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results (LEADER) trial. We discuss the potential reliability, interpretability, and efficiency gains to be had by updating our survival methods to incorporate the recent decades of advancements in formal causal frameworks and efficient nonparametricestimation.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47801734","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 : 2023-03-30DOI: 10.1080/19466315.2023.2197402
Xuekui Zhang, Haijun Jia, Li Xing, Cong Chen
{"title":"Application of group sequential methods to the 2-in-1 design and its extensions for interim monitoring","authors":"Xuekui Zhang, Haijun Jia, Li Xing, Cong Chen","doi":"10.1080/19466315.2023.2197402","DOIUrl":"https://doi.org/10.1080/19466315.2023.2197402","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45886466","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 : 2023-03-16DOI: 10.1080/19466315.2023.2191990
Hiroshi Komazaki, Masaaki Doi, N. Yonemoto, Tosiya Sato
{"title":"Multiply Robust Weighted Generalized Estimating Equations for Incomplete Longitudinal Binary Data Using Empirical Likelihood","authors":"Hiroshi Komazaki, Masaaki Doi, N. Yonemoto, Tosiya Sato","doi":"10.1080/19466315.2023.2191990","DOIUrl":"https://doi.org/10.1080/19466315.2023.2191990","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47856181","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 : 2023-03-16DOI: 10.1080/19466315.2023.2191989
Xiaodong Luo, H. Quan
{"title":"Some multiplicity adjustment procedures for clinical trials with sequential design and multiple endpoints","authors":"Xiaodong Luo, H. Quan","doi":"10.1080/19466315.2023.2191989","DOIUrl":"https://doi.org/10.1080/19466315.2023.2191989","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42820553","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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