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}
Pub Date : 2023-02-22DOI: 10.1080/19466315.2023.2183252
Heng Xu, Yi Liu, R. Beckman
{"title":"Adaptive Endpoints Selection with Application in Rare Disease","authors":"Heng Xu, Yi Liu, R. Beckman","doi":"10.1080/19466315.2023.2183252","DOIUrl":"https://doi.org/10.1080/19466315.2023.2183252","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43344554","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 : 2023-02-21DOI: 10.1080/19466315.2023.2182355
Man Jin, Yixin Fang
{"title":"Methods for Informative Censoring in Time-to-Event Data Analysis","authors":"Man Jin, Yixin Fang","doi":"10.1080/19466315.2023.2182355","DOIUrl":"https://doi.org/10.1080/19466315.2023.2182355","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49101911","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 : 2023-02-21DOI: 10.1080/19466315.2023.2177332
W. Shih, Yunqi Zhao, Tai Xie
Abstract The traditional Simon’s two-stage design for phase IIA clinical trials is modified to enhance the flexibility in conducting the interim analysis and sample size adjustment. The modification is based on the well-established methodology in adaptive designs using the conditional probability and allows for early termination as well as extension with sample size adjustment. The dynamic data monitoring system is naturally suitable for basket trials where several tumor types are monitored simultaneously with different enrollment rates.
{"title":"Modified Simon’s Two-Stage Design for Phase IIA Clinical Trials in Oncology – Dynamic Monitoring and More Flexibility","authors":"W. Shih, Yunqi Zhao, Tai Xie","doi":"10.1080/19466315.2023.2177332","DOIUrl":"https://doi.org/10.1080/19466315.2023.2177332","url":null,"abstract":"Abstract The traditional Simon’s two-stage design for phase IIA clinical trials is modified to enhance the flexibility in conducting the interim analysis and sample size adjustment. The modification is based on the well-established methodology in adaptive designs using the conditional probability and allows for early termination as well as extension with sample size adjustment. The dynamic data monitoring system is naturally suitable for basket trials where several tumor types are monitored simultaneously with different enrollment rates.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43947167","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 : 2023-02-07DOI: 10.1080/19466315.2023.2177726
Chenchen Ma, K. Crimin
{"title":"Joint Analysis of Longitudinal Data and Zero-Inflated Recurrent Events","authors":"Chenchen Ma, K. Crimin","doi":"10.1080/19466315.2023.2177726","DOIUrl":"https://doi.org/10.1080/19466315.2023.2177726","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49421465","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 : 2023-02-07DOI: 10.1080/19466315.2023.2177333
M. Ho, Susan Gruber, Yixin Fang, Douglas E Faris, P. Mishra-Kalyani, D. Benkeser, M. J. van der Laan
{"title":"Examples of Applying RWE Causal-Inference Roadmap to Clinical Studies","authors":"M. Ho, Susan Gruber, Yixin Fang, Douglas E Faris, P. Mishra-Kalyani, D. Benkeser, M. J. van der Laan","doi":"10.1080/19466315.2023.2177333","DOIUrl":"https://doi.org/10.1080/19466315.2023.2177333","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43701464","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 : 2023-01-30DOI: 10.1101/2023.01.28.23285146
Qiang Zhao, Haijun Ma
Incorporating historical information in clinical trials has been of much interest recently because of its potential to reduce the size and cost of clinical trials. Data-conflict is one of the biggest challenges in incorporating historical information. In order to address the conflict between historical data and current data, several methods have been proposed including the robust meta-analytic-predictive (rMAP) prior method. In this paper, we propose to modify the rMAP prior method by using an empirical Bayes approach to estimate the weights for the two components of the rMAP prior. Via numerical calculations, we show that this modification to the rMAP method improves its performance regarding multiple key metrics.
{"title":"Modified Robust Meta-Analytic-Predictive Priors for Incorporating Historical Controls in Clinical Trials","authors":"Qiang Zhao, Haijun Ma","doi":"10.1101/2023.01.28.23285146","DOIUrl":"https://doi.org/10.1101/2023.01.28.23285146","url":null,"abstract":"Incorporating historical information in clinical trials has been of much interest recently because of its potential to reduce the size and cost of clinical trials. Data-conflict is one of the biggest challenges in incorporating historical information. In order to address the conflict between historical data and current data, several methods have been proposed including the robust meta-analytic-predictive (rMAP) prior method. In this paper, we propose to modify the rMAP prior method by using an empirical Bayes approach to estimate the weights for the two components of the rMAP prior. Via numerical calculations, we show that this modification to the rMAP method improves its performance regarding multiple key metrics.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49385509","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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