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Lee SY. Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review. BMC Med Res Methodol 2024; 24:110. [PMID: 38714936 PMCID: PMC11077897 DOI: 10.1186/s12874-024-02235-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/24/2024] [Indexed: 05/12/2024] Open
Abstract
Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare companies, regulators, and stakeholders to assess the safety and efficacy of new treatments, interventions, and medical procedures. The Bayesian framework offers a unique advantage over the classical framework, especially when incorporating prior information into a new trial with quality external data, such as historical data or another source of co-data. In recent years, there has been a significant increase in regulatory submissions using Bayesian statistics due to its flexibility and ability to provide valuable insights for decision-making, addressing the modern complexity of clinical trials where frequentist trials are inadequate. For regulatory submissions, companies often need to consider the frequentist operating characteristics of the Bayesian analysis strategy, regardless of the design complexity. In particular, the focus is on the frequentist type I error rate and power for all realistic alternatives. This tutorial review aims to provide a comprehensive overview of the use of Bayesian statistics in sample size determination, control of type I error rate, multiplicity adjustments, external data borrowing, etc., in the regulatory environment of clinical trials. Fundamental concepts of Bayesian sample size determination and illustrative examples are provided to serve as a valuable resource for researchers, clinicians, and statisticians seeking to develop more complex and innovative designs.
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Affiliation(s)
- Se Yoon Lee
- Department of Statistics, Texas A &M University, 3143 TAMU, College Station, TX, 77843, USA.
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Omerovic E, Petrie M, Redfors B, Fremes S, Murphy G, Marquis-Gravel G, Lansky A, Velazquez E, Perera D, Reid C, Smith J, van der Meer P, Lipsic E, Juni P, McMurray J, Bauersachs J, Køber L, Rouleau JL, Doenst T. Pragmatic randomized controlled trials: strengthening the concept through a robust international collaborative network: PRIME-9-Pragmatic Research and Innovation through Multinational Experimentation. Trials 2024; 25:80. [PMID: 38263138 PMCID: PMC10807265 DOI: 10.1186/s13063-024-07935-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/15/2024] [Indexed: 01/25/2024] Open
Abstract
In an era focused on value-based healthcare, the quality of healthcare and resource allocation should be underpinned by empirical evidence. Pragmatic clinical trials (pRCTs) are essential in this endeavor, providing randomized controlled trial (RCT) insights that encapsulate real-world effects of interventions. The rising popularity of pRCTs can be attributed to their ability to mirror real-world practices, accommodate larger sample sizes, and provide cost advantages over traditional RCTs. By harmonizing efficacy with effectiveness, pRCTs assist decision-makers in prioritizing interventions that have a substantial public health impact and align with the tenets of value-based health care. An international network for pRCT provides several advantages, including larger and diverse patient populations, access to a broader range of healthcare settings, sharing knowledge and expertise, and overcoming ethical and regulatory barriers. The hypothesis and study design of pRCT answers the decision-maker's questions. pRCT compares clinically relevant alternative interventions, recruits participants from diverse practice settings, and collects data on various health outcomes. They are scarce because the medical products industry typically does not fund pRCT. Prioritizing these studies by expanding the infrastructure to conduct clinical research within the healthcare delivery system and increasing public and private funding for these studies will be necessary to facilitate pRCTs. These changes require more clinical and health policy decision-makers in clinical research priority setting, infrastructure development, and funding. This paper presents a comprehensive overview of pRCTs, emphasizing their importance in evidence-based medicine and the advantages of an international collaborative network for their execution. It details the development of PRIME-9, an international initiative across nine countries to advance pRCTs, and explores various statistical approaches for these trials. The paper underscores the need to overcome current challenges, such as funding limitations and infrastructural constraints, to leverage the full potential of pRCTs in optimizing healthcare quality and resource utilization.
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Affiliation(s)
- Elmir Omerovic
- Department of Cardiology, Sahlgrenska University Hospital, Institute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy at University of Gothenburg, Bruna Stråket 16, 41345, Gothenburg, Sweden.
| | - Mark Petrie
- British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, UK
| | - Björn Redfors
- Department of Cardiology, Sahlgrenska University Hospital, Institute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy at University of Gothenburg, Bruna Stråket 16, 41345, Gothenburg, Sweden
| | - Stephen Fremes
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Sunnybrook Research Institute, Toronto, ON, Canada
| | - Gavin Murphy
- Cardiovascular Research Centre, University of Leicester, Glenfield Hospital, Groby Road, Leicester, LE3 9QP, UK
| | | | - Alexandra Lansky
- Division of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Eric Velazquez
- Division of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Divaka Perera
- British Heart Foundation Centre of Research Excellence and National Institute for Health and Care Research Biomedical Research Centre at the School of Cardiovascular and Metabolic Medicine and Sciences, King's College London, London, UK
| | - Christopher Reid
- Curtin School of Population Health, Faculty of Health Sciences, Curtin University, Kent Street, Bentley, WA, 6102, Australia
| | - Julian Smith
- Department of Surgery (School of Clinical Sciences at Monash Health), Monash University, Melbourne, VIC, Australia
- Department of Cardiothoracic Surgery, Monash Health, Melbourne, VIC, Australia
| | - Peter van der Meer
- Department of Cardiology, Center for Blistering Diseases, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Eric Lipsic
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, PO Box 30.001, 9700 RB, Groningen, the Netherlands
| | - Peter Juni
- Oxford Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
| | - John McMurray
- British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, UK
| | - Johann Bauersachs
- Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany
| | - Lars Køber
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Jean L Rouleau
- Institut de Cardiologie de Montréal, Université de Montréal, Montréal, Canada
| | - Torsten Doenst
- Department of Cardiothoracic Surgery, Friedrich-Schiller-University Jena, University Hospital, Jena, Germany
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Bayesian Statistics for Medical Devices: Progress Since 2010. Ther Innov Regul Sci 2023; 57:453-463. [PMID: 36869194 PMCID: PMC9984131 DOI: 10.1007/s43441-022-00495-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/24/2022] [Indexed: 03/05/2023]
Abstract
The use of Bayesian statistics to support regulatory evaluation of medical devices began in the late 1990s. We review the literature, focusing on recent developments of Bayesian methods, including hierarchical modeling of studies and subgroups, borrowing strength from prior data, effective sample size, Bayesian adaptive designs, pediatric extrapolation, benefit-risk decision analysis, use of real-world evidence, and diagnostic device evaluation. We illustrate how these developments were utilized in recent medical device evaluations. In Supplementary Material, we provide a list of medical devices for which Bayesian statistics were used to support approval by the US Food and Drug Administration (FDA), including those since 2010, the year the FDA published their guidance on Bayesian statistics for medical devices. We conclude with a discussion of current and future challenges and opportunities for Bayesian statistics, including artificial intelligence/machine learning (AI/ML) Bayesian modeling, uncertainty quantification, Bayesian approaches using propensity scores, and computational challenges for high dimensional data and models.
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Hirakawa A, Sato H, Igeta M, Fujikawa K, Daimon T, Teramukai S. Regulatory issues and the potential use of Bayesian approaches for early drug approval systems in Japan. Pharm Stat 2022; 21:691-695. [PMID: 34994060 DOI: 10.1002/pst.2192] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 10/20/2021] [Accepted: 12/28/2021] [Indexed: 11/11/2022]
Abstract
Bayesian methods quantify and interpret the therapeutic effects of investigational drugs based on probability statements of the posterior distribution. However, the basic principle underlying the use of Bayesian methods in registration trials for new drug applications in Japan has not been adequately discussed. Motivated by the two drug approval systems for early approval recently enacted in Japan, we present our perspectives on the application of the Bayesian approach in registration trials in Japan. These are based on discussions among academic, industry, and regulatory experts at invited workshops. Based on the aforementioned early approval systems, we discuss putative common regulatory issues related to the use of the Bayesian approach and introduce instances of clinical trials in which the Bayesian approach is expected to be used. This article provides a well-defined premise for the discussion between industry and regulatory agencies on the use of Bayesian approaches for early drug approval in Japan.
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Affiliation(s)
- Akihiro Hirakawa
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hiroyuki Sato
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Masataka Igeta
- Department of Biostatistics, Hyogo College of Medicine, Nishinomiya, Japan
| | - Kei Fujikawa
- Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Takashi Daimon
- Department of Biostatistics, Hyogo College of Medicine, Nishinomiya, Japan
| | - Satoshi Teramukai
- Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
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Clark J, Muhlemann N, Natanegara F, Hartley A, Wenkert D, Wang F, Harrell FE, Bray R. Why are not There More Bayesian Clinical Trials? Perceived Barriers and Educational Preferences Among Medical Researchers Involved in Drug Development. Ther Innov Regul Sci 2022; 57:417-425. [PMID: 34978048 PMCID: PMC8720547 DOI: 10.1007/s43441-021-00357-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 11/08/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVE AND BACKGROUND The clinical trials community has been hesitant to adopt Bayesian statistical methods, which are often more flexible and efficient with more naturally interpretable results than frequentist methods. We aimed to identify self-reported barriers to implementing Bayesian methods and preferences for becoming comfortable with them. METHODS We developed a 22-question survey submitted to medical researchers (non-statisticians) from industry, academia, and regulatory agencies. Question areas included demographics, experience, comfort levels with Bayesian analyses, perceived barriers to these analyses, and preferences for increasing familiarity with Bayesian methods. RESULTS Of the 323 respondents, most were affiliated with pharmaceutical companies (33.4%), clinical research organizations (29.7%), and regulatory agencies (18.6%). The rest represented academia, medical practice, or other. Over 56% of respondents expressed little to no comfort in interpreting Bayesian analyses. "Insufficient knowledge of Bayesian approaches" was ranked the most important perceived barrier to implementing Bayesian methods by a plurality (48%). Of the approaches listed, in-person training was the most preferred for gaining comfort with Bayesian methods. CONCLUSIONS Based on these survey results, we recommend that introductory level training on Bayesian statistics be presented in an in-person workshop that could also be broadcast online with live Q&A. Other approaches such as online training or collaborative projects may be better suited for higher-level trainings where instructors may assume a baseline understanding of Bayesian statistics. Increased coverage of Bayesian methods at medical conferences and medical school trainings would help improve comfort and overcome the substantial knowledge barriers medical researchers face when implementing these methods.
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Affiliation(s)
- Jennifer Clark
- Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
| | | | | | | | | | - Fei Wang
- Boehringer Ingelheim, Ingelheim Am Rhein, Germany
| | - Frank E Harrell
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Ross Bray
- Eli Lilly and Company, Indianapolis, IN, USA
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