Pub Date : 2023-01-30DOI: 10.1080/19466315.2023.2173645
Susan Mayo, Yongman Kim
Abstract The ICH E9(R1) guidance on estimands is a key tool for the creation and review of protocol design and analysis planning, for both industry and regulatory statisticians. The framework has been described as useful for improving study design, intercurrent event handling, data collection, analysis, and interpretation to align the estimand with the primary clinical question to add clarity and precision to support regulatory decision-making. In this article, we describe our experience as regulatory statisticians in review of Investigational New Drug protocols and statistical analysis plans, with an emphasis on trials used to support substantial evidence of effectiveness in New Drug Applications and Biologic License Applications. Our intent is to describe our experience with this powerful and effective framework tool, to align the clinical trial’s primary objective with its analysis outcomes and interpretation.
ICH E9(R1)评估指南是行业和监管统计人员创建和审查方案设计和分析计划的关键工具。该框架被描述为有助于改进研究设计、并发事件处理、数据收集、分析和解释，以使评估与主要临床问题保持一致，从而增加清晰度和准确性，以支持监管决策。在本文中，我们描述了我们作为监管统计学家在审查新药研究方案和统计分析计划方面的经验，重点是用于支持新药申请和生物许可证申请中有效性的实质性证据的试验。我们的目的是描述我们使用这个强大而有效的框架工具的经验，使临床试验的主要目标与其分析结果和解释保持一致。
{"title":"What Can Be Achieved with the Estimand Framework?","authors":"Susan Mayo, Yongman Kim","doi":"10.1080/19466315.2023.2173645","DOIUrl":"https://doi.org/10.1080/19466315.2023.2173645","url":null,"abstract":"Abstract The ICH E9(R1) guidance on estimands is a key tool for the creation and review of protocol design and analysis planning, for both industry and regulatory statisticians. The framework has been described as useful for improving study design, intercurrent event handling, data collection, analysis, and interpretation to align the estimand with the primary clinical question to add clarity and precision to support regulatory decision-making. In this article, we describe our experience as regulatory statisticians in review of Investigational New Drug protocols and statistical analysis plans, with an emphasis on trials used to support substantial evidence of effectiveness in New Drug Applications and Biologic License Applications. Our intent is to describe our experience with this powerful and effective framework tool, to align the clinical trial’s primary objective with its analysis outcomes and interpretation.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42953720","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 : 2023-01-18DOI: 10.1080/19466315.2023.2169752
Arkendra De
Abstract The application of Artificial Intelligence to medical testing has received much attention in recent years, as evidenced by the flurry of published studies describing Artificial Intelligence software developed to solve problems in medical testing. While this recent activity is exciting, developed Artificial Intelligence medical tests ultimately can only be considered as candidates for widespread use if these tests demonstrate good performance in pivotal clinical studies. What are pivotal clinical studies for Artificial Intelligence medical tests aimed for widespread use? What are some of the major considerations and challenges for assessing performance of these tests in this context? What are some of the outstanding areas where statisticians, in collaboration with professionals outside the statistical community, could help in this endeavor? This article addresses these questions. This article is meant to appeal to a broad audience with varying levels of statistical and medical testing knowledge so that inter-disciplinary collaboration could be enhanced.
{"title":"Statistical Considerations and Challenges for Pivotal Clinical Studies of Artificial Intelligence Medical Tests for Widespread Use: Opportunities for Inter-Disciplinary Collaboration","authors":"Arkendra De","doi":"10.1080/19466315.2023.2169752","DOIUrl":"https://doi.org/10.1080/19466315.2023.2169752","url":null,"abstract":"Abstract The application of Artificial Intelligence to medical testing has received much attention in recent years, as evidenced by the flurry of published studies describing Artificial Intelligence software developed to solve problems in medical testing. While this recent activity is exciting, developed Artificial Intelligence medical tests ultimately can only be considered as candidates for widespread use if these tests demonstrate good performance in pivotal clinical studies. What are pivotal clinical studies for Artificial Intelligence medical tests aimed for widespread use? What are some of the major considerations and challenges for assessing performance of these tests in this context? What are some of the outstanding areas where statisticians, in collaboration with professionals outside the statistical community, could help in this endeavor? This article addresses these questions. This article is meant to appeal to a broad audience with varying levels of statistical and medical testing knowledge so that inter-disciplinary collaboration could be enhanced.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41493376","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 : 2023-01-09DOI: 10.1080/19466315.2023.2166099
Olga V. Marchenko, R. Sridhara, Qi Jiang, Elizabeth Barksdale, Y. Ando, D. D. Alwis, Katie Brown, L. Fernandes, M. V. van Bussel, Qiuyi Choo, M. Coory, E. Garrett-Mayer, T. Gwise, Lorenzo Hess, Rong Liu, S. Mandrekar, D. Ouellet, J. Pinheiro, M. Posch, N. Rahman, K. Rantell, A. Raven, Sarem Sarem, S. Sen, M. Shah, Y. Shen, Richard Simon, M. Theoret, Ying Yuan, R. Pazdur
Abstract The article provides a summary of discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forums on March 18th, June 10th, and July 8th of 2021, organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group in coordination with the U.S. Food and Drug Administration (FDA) Oncology Center of Excellence and the LUNGevity Foundation. Diverse stakeholders including oncologists, patient advocates, experts from regulatory agencies across the world, academicians, and representatives from the pharmaceutical industry engaged in a lively discussion on strategies for and designs of dose-optimization studies in cancer drug development. Dose-optimization is one of the major challenges in oncology drug development. The discussions were focused on considerations in designing dose-optimization studies of products for treatment of cancer patients in pre-approval and post-approval stages. Presenters and panelists discussed diverse ideas and methods and agreed that a shift in paradigm is required in oncology drug development that should improve dose optimization while not unnecessarily delaying patient access to potentially efficacious new treatments.
{"title":"Designing Dose-Optimization Studies in Cancer Drug Development: Discussions with Regulators","authors":"Olga V. Marchenko, R. Sridhara, Qi Jiang, Elizabeth Barksdale, Y. Ando, D. D. Alwis, Katie Brown, L. Fernandes, M. V. van Bussel, Qiuyi Choo, M. Coory, E. Garrett-Mayer, T. Gwise, Lorenzo Hess, Rong Liu, S. Mandrekar, D. Ouellet, J. Pinheiro, M. Posch, N. Rahman, K. Rantell, A. Raven, Sarem Sarem, S. Sen, M. Shah, Y. Shen, Richard Simon, M. Theoret, Ying Yuan, R. Pazdur","doi":"10.1080/19466315.2023.2166099","DOIUrl":"https://doi.org/10.1080/19466315.2023.2166099","url":null,"abstract":"Abstract The article provides a summary of discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forums on March 18th, June 10th, and July 8th of 2021, organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group in coordination with the U.S. Food and Drug Administration (FDA) Oncology Center of Excellence and the LUNGevity Foundation. Diverse stakeholders including oncologists, patient advocates, experts from regulatory agencies across the world, academicians, and representatives from the pharmaceutical industry engaged in a lively discussion on strategies for and designs of dose-optimization studies in cancer drug development. Dose-optimization is one of the major challenges in oncology drug development. The discussions were focused on considerations in designing dose-optimization studies of products for treatment of cancer patients in pre-approval and post-approval stages. Presenters and panelists discussed diverse ideas and methods and agreed that a shift in paradigm is required in oncology drug development that should improve dose optimization while not unnecessarily delaying patient access to potentially efficacious new treatments.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48005392","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 : 2023-01-02DOI: 10.1080/19466315.2022.2128405
S. Vansteelandt
I would like to thank the editor, Prof. Hamasaki, for the opportunity to comment on the thought-provoking work by the NISS working group on unplanned clinical trial disruptions (Van Lancker et al. 2022). The working group’s proposals focus on two basic problems relevant to clinical trials affected by the COVID19 pandemic. The first problem is that, due to the pandemic, the patient population may change systematically over the course of the trial. This raises questions over what is the relevant patient population for which the effect is of interest. The second problem, which receives the major focus in the paper, relates to problems of intercurrent events fueled by the pandemic. The solutions proposed by the working group are interesting and useful. In this commentary, I will nonetheless raise two conceptual shortcomings, which I will attempt to resolve by making more explicit use of methods from causal inference (as opposed to missing data analysis). First, the data collected in a randomized clinical trial are so precious that it is generally difficult to justify ignoring the data collected before or after the start of the pandemic. Those data will often still carry useful information about treatment efficacy, and should ideally be used. Second, whenever possible, analyses of randomized clinical trials should protect the null hypothesis of no treatment effect in the sense that rejection rates should be no larger than the nominal (5%) rate, even when the adopted assumptions fail. Intercurrent events 6 and 7 appear such that they will occur with equal rates in both arms of the trial. If this is so, then this suggests that standard analyses that target the treatment policy estimand, thus ignoring intercurrent events, will protect the null hypothesis of no treatment effect; indeed, the treatment policy estimand then reduces to the balanced estimand of Michiels et al. (2021), which expresses what the treatment effect had been had intercurrent events occurred at “equal rates” in both arms. In this light, analyses that invoke Missing At Random (MAR) assumptions must be taken with caution, as they may be biased whenever the MAR assumption fails. More importantly, analyses that explicitly combine biased and unbiased estimators, as in
{"title":"Comment on” Estimands and their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions”","authors":"S. Vansteelandt","doi":"10.1080/19466315.2022.2128405","DOIUrl":"https://doi.org/10.1080/19466315.2022.2128405","url":null,"abstract":"I would like to thank the editor, Prof. Hamasaki, for the opportunity to comment on the thought-provoking work by the NISS working group on unplanned clinical trial disruptions (Van Lancker et al. 2022). The working group’s proposals focus on two basic problems relevant to clinical trials affected by the COVID19 pandemic. The first problem is that, due to the pandemic, the patient population may change systematically over the course of the trial. This raises questions over what is the relevant patient population for which the effect is of interest. The second problem, which receives the major focus in the paper, relates to problems of intercurrent events fueled by the pandemic. The solutions proposed by the working group are interesting and useful. In this commentary, I will nonetheless raise two conceptual shortcomings, which I will attempt to resolve by making more explicit use of methods from causal inference (as opposed to missing data analysis). First, the data collected in a randomized clinical trial are so precious that it is generally difficult to justify ignoring the data collected before or after the start of the pandemic. Those data will often still carry useful information about treatment efficacy, and should ideally be used. Second, whenever possible, analyses of randomized clinical trials should protect the null hypothesis of no treatment effect in the sense that rejection rates should be no larger than the nominal (5%) rate, even when the adopted assumptions fail. Intercurrent events 6 and 7 appear such that they will occur with equal rates in both arms of the trial. If this is so, then this suggests that standard analyses that target the treatment policy estimand, thus ignoring intercurrent events, will protect the null hypothesis of no treatment effect; indeed, the treatment policy estimand then reduces to the balanced estimand of Michiels et al. (2021), which expresses what the treatment effect had been had intercurrent events occurred at “equal rates” in both arms. In this light, analyses that invoke Missing At Random (MAR) assumptions must be taken with caution, as they may be biased whenever the MAR assumption fails. More importantly, analyses that explicitly combine biased and unbiased estimators, as in","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48715465","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 : 2023-01-02DOI: 10.1080/19466315.2022.2152090
N. Flournoy
CONTACT Nancy Flournoy flournoyn@missouri.edu Department of Statistics, University of Missouri (emerita), Columbia. The efforts arose from working groups formed during a NISS Ingram Olkin Forum series on the following topics: (1)Estimands and Missing Data, (2) The Role of Randomization Tests, (3) Methods to Cope with Information Loss and the Use of Auxiliary Sources of Data and (4) Bayes and Frequentist Approaches to Rescuing Disrupted Trials. These groups consider how existing methods can be applied in the context of unplanned clinical trial disruptions and uncover unsolved issues requiring further research. In addition to introducing you to these research projects, I am pleased to provide a brief introduction to the NISS Ingram Olkin Forums. The National Institute of Statistical Sciences (NISS) created Ingram Olkin Forums (IOFs) to foster Statistics Serving Society (S3) in memory of Professor Ingram Olkin. Motivated by the aspirations set forth by Olkin et al. (1990), each forum focuses on a current societal issue that might benefit from new or renewed attention from the statistical community. IOFs aim to bring the latest innovations in statistical methodology and data science into new research and public policy collaborations, working to accelerate the development of innovative approaches that impact societal problems. As a Forum brings a particular group of experts together for the first time to consider an issue, new energy and synergy is expected to produce a flurry of new ideas and approaches. The inaugural IOF was held in June 1919 on Gun Violence, prior to the arrival of the Covid-19 pandemic. Knowing that many statisticians would use their expertise to monitor the pandemic and to design vaccine and therapeutic trials, the IOF Committee looked for a need that might be neglected and decided to host an online IOF on Unplanned Clinical Trial Disruptions. A major concern in moving online was not to get stuck with one-directional webinars, but to get statisticians and other scientists who did not know each other previously to work together without meeting in-person. I am delighted to announce four papers resulting from this IOF will appear in Statistics in Biopharmaceutical Research. NISS is very happy with how well the IOF on Unplanned Clinical Trial Disruptions met its S3 objectives, with enthusiastic collegiality and productivity, and although in-person and hybrid launches will again be possible, this IOF is now NISS’s model.
{"title":"The NISS Ingram Olkin Forum on Unplanned Clinical Trial Disruptions","authors":"N. Flournoy","doi":"10.1080/19466315.2022.2152090","DOIUrl":"https://doi.org/10.1080/19466315.2022.2152090","url":null,"abstract":"CONTACT Nancy Flournoy flournoyn@missouri.edu Department of Statistics, University of Missouri (emerita), Columbia. The efforts arose from working groups formed during a NISS Ingram Olkin Forum series on the following topics: (1)Estimands and Missing Data, (2) The Role of Randomization Tests, (3) Methods to Cope with Information Loss and the Use of Auxiliary Sources of Data and (4) Bayes and Frequentist Approaches to Rescuing Disrupted Trials. These groups consider how existing methods can be applied in the context of unplanned clinical trial disruptions and uncover unsolved issues requiring further research. In addition to introducing you to these research projects, I am pleased to provide a brief introduction to the NISS Ingram Olkin Forums. The National Institute of Statistical Sciences (NISS) created Ingram Olkin Forums (IOFs) to foster Statistics Serving Society (S3) in memory of Professor Ingram Olkin. Motivated by the aspirations set forth by Olkin et al. (1990), each forum focuses on a current societal issue that might benefit from new or renewed attention from the statistical community. IOFs aim to bring the latest innovations in statistical methodology and data science into new research and public policy collaborations, working to accelerate the development of innovative approaches that impact societal problems. As a Forum brings a particular group of experts together for the first time to consider an issue, new energy and synergy is expected to produce a flurry of new ideas and approaches. The inaugural IOF was held in June 1919 on Gun Violence, prior to the arrival of the Covid-19 pandemic. Knowing that many statisticians would use their expertise to monitor the pandemic and to design vaccine and therapeutic trials, the IOF Committee looked for a need that might be neglected and decided to host an online IOF on Unplanned Clinical Trial Disruptions. A major concern in moving online was not to get stuck with one-directional webinars, but to get statisticians and other scientists who did not know each other previously to work together without meeting in-person. I am delighted to announce four papers resulting from this IOF will appear in Statistics in Biopharmaceutical Research. NISS is very happy with how well the IOF on Unplanned Clinical Trial Disruptions met its S3 objectives, with enthusiastic collegiality and productivity, and although in-person and hybrid launches will again be possible, this IOF is now NISS’s model.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44043081","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 : 2023-01-02DOI: 10.1080/19466315.2022.2094459
Kelly Van Lancker, S. Tarima, J. Bartlett, M. Bauer, Bharani Bharani-Dharan, F. Bretz, N. Flournoy, Hege Michiels, Camila Olarte Parra, J. L. Rosenberger, S. Cro
Abstract The COVID-19 pandemic continues to affect the conduct of clinical trials globally. Complications may arise from pandemic-related operational challenges such as site closures, travel limitations and interruptions to the supply chain for the investigational product, or from health-related challenges such as COVID-19 infections. Some of these complications lead to unforeseen intercurrent events in the sense that they affect either the interpretation or the existence of the measurements associated with the clinical question of interest. In this article, we demonstrate how the ICH E9(R1) Addendum on estimands and sensitivity analyses provides a rigorous basis to discuss potential pandemic-related trial disruptions and to embed these disruptions in the context of study objectives and design elements. We introduce several hypothetical estimand strategies and review various causal inference and missing data methods, as well as a statistical method that combines unbiased and possibly biased estimators for estimation. To illustrate, we describe the features of a stylized trial, and how it may have been impacted by the pandemic. This stylized trial will then be revisited by discussing the changes to the estimand and the estimator to account for pandemic disruptions. Finally, we outline considerations for designing future trials in the context of unforeseen disruptions.
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Pub Date : 2023-01-02DOI: 10.1080/19466315.2022.2128402
Mark Baillie, Conor Moloney, Carsten Philipp Mueller, Jonas Dorn, J. Branson, D. Ohlssen
Fang and He ask why we focus on exploratory (cite “[...] 26 times exploratory [...] only 3 times confirmatory [/.]”) over confirmatory activities and if as a consequence our data science definition is limited in scope. They also ask if the definition of data science should be more specific, with a focus on treatment effectiveness: “exploratory activities are insufficient for the purpose of establishing the existence and estimating the magnitude of treatment effects, which is confirmatory in nature.”
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Pub Date : 2023-01-02DOI: 10.1080/19466315.2022.2151507
M. Akacha, Tianmeng Lyu
We
我们
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Pub Date : 2023-01-02DOI: 10.1080/19466315.2022.2162291
T. Hamasaki
There have been increasing discussions on how real-world data (RWD) and real-world evidence (RWE) can play a role in health care decisions, particularly in medical product regulation, where RWD are the data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources (e.g., observational studies, electronic health records, product, and disease registries, etc.), and RWE is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD (Food and Drug Administration (FDA) 2017). Unitizing external data sources in the design and analysis of clinical trials or medical product development is not a new idea. In assessing clinical trial feasibility of a medical product, external data sources have often been used to find new hypotheses/findings, characterizing relevant patient populations and subpopulations, understanding unmet need, identifying important assumptions about the impact of potential eligibility criteria on trial feasibility. At the protocol development of the clinical trials, they have been used to estimate the expected effect size of the medical products, to calculate the sample size, and to support patient recruitment, and during the trial conduct, they might be used to change or modify the trial protocol or designs, or sometimes to stop the trial. At the end of the development of the medical product, in general, comprehensive integrated analysis of the efficacy and safety has been conducted, including other sources of information relevant to efficacy and safety of the product. Furthermore, in Japan, there is a very unique regulatory decision-making framework for evaluating off-label use of unapproved medical products, so called “Public KnowledgeBased Applications” (“Kochi Shinsei” in Japanese) (Ministry of Health and Welfare (MHLW) 1980). A sponsor is able to submit an application without conducting (additional) clinical trials, if efficacy and safety for a new indication of the medical product are recognized to be well known in the medical and pharmacological field through publications. This framework is a great practice of regulatory decision-making based on RWD/RWE. What is happening right now? What is different from current practice? Due to the latest advanced technologies, it is much easier to gather and store huge amounts of health-related data in “real time.” It is expected that RWD/RWE can be used into
{"title":"Editor’s Note: Special Section on a Collection of Articles on Opportunities and Challenges in Utilizing Real-World Data for Clinical Trials and Medical Product Development","authors":"T. Hamasaki","doi":"10.1080/19466315.2022.2162291","DOIUrl":"https://doi.org/10.1080/19466315.2022.2162291","url":null,"abstract":"There have been increasing discussions on how real-world data (RWD) and real-world evidence (RWE) can play a role in health care decisions, particularly in medical product regulation, where RWD are the data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources (e.g., observational studies, electronic health records, product, and disease registries, etc.), and RWE is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD (Food and Drug Administration (FDA) 2017). Unitizing external data sources in the design and analysis of clinical trials or medical product development is not a new idea. In assessing clinical trial feasibility of a medical product, external data sources have often been used to find new hypotheses/findings, characterizing relevant patient populations and subpopulations, understanding unmet need, identifying important assumptions about the impact of potential eligibility criteria on trial feasibility. At the protocol development of the clinical trials, they have been used to estimate the expected effect size of the medical products, to calculate the sample size, and to support patient recruitment, and during the trial conduct, they might be used to change or modify the trial protocol or designs, or sometimes to stop the trial. At the end of the development of the medical product, in general, comprehensive integrated analysis of the efficacy and safety has been conducted, including other sources of information relevant to efficacy and safety of the product. Furthermore, in Japan, there is a very unique regulatory decision-making framework for evaluating off-label use of unapproved medical products, so called “Public KnowledgeBased Applications” (“Kochi Shinsei” in Japanese) (Ministry of Health and Welfare (MHLW) 1980). A sponsor is able to submit an application without conducting (additional) clinical trials, if efficacy and safety for a new indication of the medical product are recognized to be well known in the medical and pharmacological field through publications. This framework is a great practice of regulatory decision-making based on RWD/RWE. What is happening right now? What is different from current practice? Due to the latest advanced technologies, it is much easier to gather and store huge amounts of health-related data in “real time.” It is expected that RWD/RWE can be used into","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41767006","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 : 2023-01-01Epub Date: 2021-08-31DOI: 10.1080/19466315.2021.1961854
Fengming Tang, Byron J Gajewski
Slow accrual rate is a major challenge in clinical trials for rare diseases and is identified as the most frequent reason for clinical trials to fail. This challenge is amplified in comparative effectiveness research where multiple treatments are compared to identify the best treatment. Novel efficient clinical trial designs are in urgent need in these areas. Our proposed response adaptive randomization (RAR) reusing participants trial design mimics the real-world clinical practice that allows patients to switch treatments when desired outcome is not achieved. The proposed design increases efficiency by two strategies: 1) Allowing participants to switch treatments so that each participant can have more than one observation and hence it is possible to control for participant specific variability to increase statistical power; and 2) Utilizing RAR to allocate more participants to the promising arms such that ethical and efficient studies will be achieved. Extensive simulations were conducted and showed that, compared with trials where each participant receives one treatment, the proposed participants reusing RAR design can achieve comparable power with a smaller sample size and a shorter trial duration, especially when the accrual rate is low. The efficiency gain decreases as the accrual rate increases.
{"title":"Comparative Effectiveness Research using Bayesian Adaptive Designs for Rare Diseases: Response Adaptive Randomization Reusing Participants.","authors":"Fengming Tang, Byron J Gajewski","doi":"10.1080/19466315.2021.1961854","DOIUrl":"10.1080/19466315.2021.1961854","url":null,"abstract":"<p><p>Slow accrual rate is a major challenge in clinical trials for rare diseases and is identified as the most frequent reason for clinical trials to fail. This challenge is amplified in comparative effectiveness research where multiple treatments are compared to identify the best treatment. Novel efficient clinical trial designs are in urgent need in these areas. Our proposed response adaptive randomization (RAR) reusing participants trial design mimics the real-world clinical practice that allows patients to switch treatments when desired outcome is not achieved. The proposed design increases efficiency by two strategies: 1) Allowing participants to switch treatments so that each participant can have more than one observation and hence it is possible to control for participant specific variability to increase statistical power; and 2) Utilizing RAR to allocate more participants to the promising arms such that ethical and efficient studies will be achieved. Extensive simulations were conducted and showed that, compared with trials where each participant receives one treatment, the proposed participants reusing RAR design can achieve comparable power with a smaller sample size and a shorter trial duration, especially when the accrual rate is low. The efficiency gain decreases as the accrual rate increases.</p>","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9979780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10845588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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