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":"42 1","pages":"0"},"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":"15 1","pages":"235 - 236"},"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":"15 1","pages":"524 - 539"},"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":" ","pages":""},"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: 10.1080/19466315.2023.2191989
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":" ","pages":""},"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}
Pub Date : 2023-03-15DOI: 10.1080/19466315.2023.2186945
Yi Liu, Miao Yang, Siyoen Kil, Jiangya Li, Shoubhik Mondal, Y. Shentu, Hong Tian, Liwei Wang, Godwin Yung
Abstract An important goal of precision medicine is to identify biomarkers that are predictive, and tailor the treatment according to the biomarker levels of individual patients. Differentiating prognostic versus predictive biomarkers impacts important decision makings for patients and treating physicians. Using Hazard Ratio (HR) can mistake a purely prognostic biomarker for a predictive one leading to a disheartening possibility of depriving patients of beneficial treatment as demonstrated in the OAK trial. This stems from the illogical issue of HR at population level where marginal HR in the overall population can be larger than those in both subgroups. Instead of trying to circumvent this issue by discouraging comparisons between marginal and conditional HRs, we propose to directly fix it by using alternative logic-respecting efficacy estimands such as ratio of medians, ratio and difference of restricted mean survival times and milestone probabilities. These measures are straightforward, easy to interpret and clinically meaningful. More importantly, they will guarantee agreement between marginal and conditional efficacy and provide cohesive message around efficacy profile of the drug in the presence of subgroups. A step further is the application of Subgroup Mixable Estimation (SME) principle to ensure logical estimates when analyzing real clinical trial data. Detailed guidance is provided for the aforementioned logic-respecting estimands using either parametric, semiparametric or nonparametric approaches. Simultaneous inference can be provided with proper multiplicity adjustment to facilitate joint decision making with user-friendly apps.
{"title":"From Logic-Respecting Efficacy Estimands to Logic-Ensuring Analysis Principle for Time-to-Event Endpoint in Randomized Clinical Trials with Subgroups","authors":"Yi Liu, Miao Yang, Siyoen Kil, Jiangya Li, Shoubhik Mondal, Y. Shentu, Hong Tian, Liwei Wang, Godwin Yung","doi":"10.1080/19466315.2023.2186945","DOIUrl":"https://doi.org/10.1080/19466315.2023.2186945","url":null,"abstract":"Abstract An important goal of precision medicine is to identify biomarkers that are predictive, and tailor the treatment according to the biomarker levels of individual patients. Differentiating prognostic versus predictive biomarkers impacts important decision makings for patients and treating physicians. Using Hazard Ratio (HR) can mistake a purely prognostic biomarker for a predictive one leading to a disheartening possibility of depriving patients of beneficial treatment as demonstrated in the OAK trial. This stems from the illogical issue of HR at population level where marginal HR in the overall population can be larger than those in both subgroups. Instead of trying to circumvent this issue by discouraging comparisons between marginal and conditional HRs, we propose to directly fix it by using alternative logic-respecting efficacy estimands such as ratio of medians, ratio and difference of restricted mean survival times and milestone probabilities. These measures are straightforward, easy to interpret and clinically meaningful. More importantly, they will guarantee agreement between marginal and conditional efficacy and provide cohesive message around efficacy profile of the drug in the presence of subgroups. A step further is the application of Subgroup Mixable Estimation (SME) principle to ensure logical estimates when analyzing real clinical trial data. Detailed guidance is provided for the aforementioned logic-respecting estimands using either parametric, semiparametric or nonparametric approaches. Simultaneous inference can be provided with proper multiplicity adjustment to facilitate joint decision making with user-friendly apps.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"560 - 573"},"PeriodicalIF":1.8,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46379730","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 : 2023-03-13DOI: 10.1080/19466315.2023.2190932
Koichi Hashizume, Jun Tsuchida, T. Sozu
{"title":"Copula-based model for incorporating single-agent historical data into dual-agent phase I cancer trials","authors":"Koichi Hashizume, Jun Tsuchida, T. Sozu","doi":"10.1080/19466315.2023.2190932","DOIUrl":"https://doi.org/10.1080/19466315.2023.2190932","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46604725","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 : 2023-03-13DOI: 10.1080/19466315.2023.2190933
Akalu Banbeta, E. Lesaffre, R. Martina, Joost van Rosmalen
{"title":"Bayesian Borrowing Methods for Count Data: Analysis of Incontinence Episodes in Patients with Overactive Bladder","authors":"Akalu Banbeta, E. Lesaffre, R. Martina, Joost van Rosmalen","doi":"10.1080/19466315.2023.2190933","DOIUrl":"https://doi.org/10.1080/19466315.2023.2190933","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42649019","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 : 2023-03-13DOI: 10.1080/19466315.2023.2190930
Jiangtao Gou
{"title":"A test of the dependence assumptions for the Simes-test-based multiple test procedures","authors":"Jiangtao Gou","doi":"10.1080/19466315.2023.2190930","DOIUrl":"https://doi.org/10.1080/19466315.2023.2190930","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49434033","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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