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Goligher EC, Heath A, Harhay MO. Bayesian statistics for clinical research. Lancet 2024; 404:1067-1076. [PMID: 39277290 DOI: 10.1016/s0140-6736(24)01295-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/25/2024] [Accepted: 06/16/2024] [Indexed: 09/17/2024]
Abstract
Frequentist and Bayesian statistics represent two differing paradigms for the analysis of data. Frequentism became the dominant mode of statistical thinking in medical practice during the 20th century. The advent of modern computing has made Bayesian analysis increasingly accessible, enabling growing use of Bayesian methods in a range of disciplines, including medical research. Rather than conceiving of probability as the expected frequency of an event (purported to be measurable and objective), Bayesian thinking conceives of probability as a measure of strength of belief (an explicitly subjective concept). Bayesian analysis combines previous information (represented by a mathematical probability distribution, the prior) with information from the study (the likelihood function) to generate an updated probability distribution (the posterior) representing the information available for clinical decision making. Owing to its fundamentally different conception of probability, Bayesian statistics offers an intuitive, flexible, and informative approach that facilitates the design, analysis, and interpretation of clinical trials. In this Review, we provide a brief account of the philosophical and methodological differences between Bayesian and frequentist approaches and survey the use of Bayesian methods for the design and analysis of clinical research.
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Affiliation(s)
- Ewan C Goligher
- Interdepartmental Division of Critical Care Medicine and Department of Physiology, University of Toronto, Toronto, ON, Canada; Department of Medicine, Division of Respirology, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, Toronto, ON, Canada.
| | - Anna Heath
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Michael O Harhay
- Department of Statistical Science (A Heath), University College London, London, UK; MRC Clinical Trials Unit, University College London, London, UK; Department of Biostatistics, Epidemiology, and Informatics and Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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2
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Broglio KR, Blau JE, Pilling EA, Wason JMS. Multidisciplinary considerations for implementing Bayesian borrowing in basket trials. Drug Discov Today 2024; 29:104127. [PMID: 39098385 DOI: 10.1016/j.drudis.2024.104127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 07/19/2024] [Accepted: 07/30/2024] [Indexed: 08/06/2024]
Abstract
Drug development has historically relied on phase I-III clinical trials including participants sharing the same disease. However, drug development has evolved as the discovery of mechanistic drivers of disease demonstrated that the same therapeutic target may provide benefits across different diseases. A basket trial condenses evaluation of one therapy among multiple related diseases into a single trial and presents an opportunity to borrow information across them rather than viewing each in isolation. Borrowing is a statistical tool but requires a foundation of clinical and therapeutic mechanistic justification. We review the Bayesian borrowing approach, including its assumptions, and provide a framework for how this approach can be evaluated for successful use in a basket trial for drug development.
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Affiliation(s)
- Kristine R Broglio
- Oncology Statistical Innovation, AstraZeneca Pharmaceuticals, Gaithersburg, MD, USA.
| | - Jenny E Blau
- Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Elizabeth A Pilling
- Biometrics, Late-stage Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - James M S Wason
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
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3
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Tu Y, Renfro LA. Biomarker-driven basket trial designs: origins and new methodological developments. J Biopharm Stat 2024:1-13. [PMID: 38832723 DOI: 10.1080/10543406.2024.2358806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 05/12/2024] [Indexed: 06/05/2024]
Abstract
Due to increased use of gene sequencing techniques, understanding of cancer on a molecular level has evolved, in terms of both diagnosis and evaluation in response to initial therapies. In parallel, clinical trials meant to evaluate molecularly-driven interventions through assessment of both treatment effects and putative predictive biomarker effects are being employed to advance the goals of precision medicine. Basket trials investigate one or more biomarker-targeted therapies across multiple cancer types in a tumor location agnostic fashion. The review article offers an overview of the traditional forms of such designs, the practical challenges facing each type of design, and then review novel adaptations proposed in the last few years, categorized into Bayesian and Classical Frequentist perspectives. The review article concludes by summarizing potential advantages and limitations of the new trial design solutions.
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Affiliation(s)
- Yue Tu
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA
| | - Lindsay A Renfro
- Department of Population and Public Health Sciences, University of Southern California and Children's Oncology Group, Los Angeles, California, USA
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4
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Zhang J, Lin R, Chen X, Yan F. Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses. Clin Trials 2024; 21:308-321. [PMID: 38243401 PMCID: PMC11132956 DOI: 10.1177/17407745231212193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2024]
Abstract
In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.
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Affiliation(s)
- Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xin Chen
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
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5
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Chi X, Yuan Y, Yu Z, Lin R. A generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints. Biom J 2024; 66:e2300122. [PMID: 38368277 PMCID: PMC11323483 DOI: 10.1002/bimj.202300122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 11/05/2023] [Accepted: 12/29/2023] [Indexed: 02/19/2024]
Abstract
A basket trial simultaneously evaluates a treatment in multiple cancer subtypes, offering an effective way to accelerate drug development in multiple indications. Many basket trials are designed and monitored based on a single efficacy endpoint, primarily the tumor response. For molecular targeted or immunotherapy agents, however, a single efficacy endpoint cannot adequately characterize the treatment effect. It is increasingly important to use more complex endpoints to comprehensively assess the risk-benefit profile of such targeted therapies. We extend the calibrated Bayesian hierarchical modeling approach to monitor phase II basket trials with multiple endpoints. We propose two generalizations, one based on the latent variable approach and the other based on the multinomial-normal hierarchical model, to accommodate different types of endpoints and dependence assumptions regarding information sharing. We introduce shrinkage parameters as functions of statistics measuring homogeneity among subgroups and propose a general calibration approach to determine the functional forms. Theoretical properties of the generalized hierarchical models are investigated. Simulation studies demonstrate that the monitoring procedure based on the generalized approach yields desirable operating characteristics.
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Affiliation(s)
- Xiaohan Chi
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
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Takeda K, Hashimoto A, Liu S, Rong A. A basket trial design based on constrained hierarchical Bayesian model for latent subgroups. J Biopharm Stat 2024:1-12. [PMID: 38369872 DOI: 10.1080/10543406.2024.2311851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/24/2024] [Indexed: 02/20/2024]
Abstract
It is well known a basket trial consisting of multiple cancer types has the potential of borrowing strength across the baskets defined by the cancer types, leading to an efficient design in terms of sample size and trial duration. The treatment effects in those baskets are often heterogeneous and categorized by the cancer types being sensitive or insensitive to the treatment. Hence, the assumption of exchangeability in many existing basket trials may be violated, and there is a need to design trials without this assumption. In this paper, we simplify the constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) for two classifiers to deal with the potential heterogeneity of treatment effects due to the single classifier of the cancer type. Different baskets are aggregated into subgroups using a latent subgroup modeling approach. The treatment effects are similar and exchangeable to facilitate information borrowing within each latent subgroup. Applying the simplified CHBM-LS approach to the real basket trials where baskets defined by only cancer types shows better performance than other available approaches. Further simulation study also demonstrates this CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios.
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Affiliation(s)
- Kentaro Takeda
- Data Science, Astellas Pharma Global Development Inc, Northbrook, Illinois, USA
| | | | - Shufang Liu
- Oncology Biostatistics, Gilead Sciences Inc, Foster City, California, USA
| | - Alan Rong
- Oncology Biostatistics, Gilead Sciences Inc, Foster City, California, USA
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Lu CC, Beckman RA, Li XN, Zhang W, Jiang Q, Marchenko O, Sun Z, Tian H, Ye J, Yuan SS, Yung G. Tumor-Agnostic Approvals: Insights and Practical Considerations. Clin Cancer Res 2024; 30:480-488. [PMID: 37792436 DOI: 10.1158/1078-0432.ccr-23-1340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/05/2023] [Accepted: 10/03/2023] [Indexed: 10/05/2023]
Abstract
Since the first approval of a tumor-agnostic indication in 2017, a total of seven tumor-agnostic indications involving six drugs have received approval from the FDA. In this paper, the master protocol subteam of the Statistical Methods in Oncology Scientific Working Group, Biopharmaceutical Session, American Statistical Association, provides a comprehensive summary of these seven tumor-agnostic approvals, describing their mechanisms of action; biomarker prevalence; study design; companion diagnostics; regulatory aspects, including comparisons of global regulatory requirements; and health technology assessment approval. Also discussed are practical considerations relating to the regulatory approval of tumor-agnostic indications, specifically (i) recommendations for the design stage to mitigate the risk that exceptions may occur if a treatment is initially hypothesized to be effective for all tumor types and (ii) because drug development continues after approval of a tumor-agnostic indication, recommendations for further development of tumor-specific indications in first-line patients in the setting of a randomized confirmatory basket trial, acknowledging the challenges in this area. These recommendations and practical considerations may provide insights for the future development of drugs for tumor-agnostic indications.
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Affiliation(s)
| | - Robert A Beckman
- Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
| | | | | | - Qi Jiang
- Biometrics, Seagen, Bothell, Washington
| | - Olga Marchenko
- Statistics and Data Insights, Bayer, Whippany, New Jersey
| | - Zhiping Sun
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey
| | - Hong Tian
- Global Statistics and Data Sciences, BeiGene, Fulton, Maryland
| | - Jingjing Ye
- Global Statistics and Data Sciences, BeiGene, Fulton, Maryland
| | - Shuai Sammy Yuan
- Oncology Statistics, GlaxoSmithKline, Collegeville, Pennsylvania
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Zhou T, Ji Y. Bayesian Methods for Information Borrowing in Basket Trials: An Overview. Cancers (Basel) 2024; 16:251. [PMID: 38254740 PMCID: PMC10813856 DOI: 10.3390/cancers16020251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/22/2023] [Accepted: 01/03/2024] [Indexed: 01/24/2024] Open
Abstract
Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings.
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Affiliation(s)
- Tianjian Zhou
- Department of Statistics, Colorado State University, Fort Collins, CO 80523, USA
| | - Yuan Ji
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA
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Kasim A, Bean N, Hendriksen SJ, Chen TT, Zhou H, Psioda MA. Basket trials in oncology: a systematic review of practices and methods, comparative analysis of innovative methods, and an appraisal of a missed opportunity. Front Oncol 2023; 13:1266286. [PMID: 38033501 PMCID: PMC10684308 DOI: 10.3389/fonc.2023.1266286] [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: 07/24/2023] [Accepted: 10/13/2023] [Indexed: 12/02/2023] Open
Abstract
Background Basket trials are increasingly used in oncology drug development for early signal detection, accelerated tumor-agnostic approvals, and prioritization of promising tumor types in selected patients with the same mutation or biomarker. Participants are grouped into so-called baskets according to tumor type, allowing investigators to identify tumors with promising responses to treatment for further study. However, it remains a question as to whether and how much the adoption of basket trial designs in oncology have translated into patient benefits, increased pace and scale of clinical development, and de-risking of downstream confirmatory trials. Methods Innovation in basket trial design and analysis includes methods that borrow information across tumor types to increase the quality of statistical inference within each tumor type. We build on the existing systematic reviews of basket trials in oncology to discuss the current practices and landscape. We conceptually illustrate recent innovative methods for basket trials, with application to actual data from recently completed basket trials. We explore and discuss the extent to which innovative basket trials can be used to de-risk future trials through their ability to aid prioritization of promising tumor types for subsequent clinical development. Results We found increasing adoption of basket trial design in oncology, but largely in the design of single-arm phase II trials with a very low adoption of innovative statistical methods. Furthermore, the current practice of basket trial design, which does not consider its impact on the clinical development plan, may lead to a missed opportunity in improving the probability of success of a future trial. Gating phase II with a phase Ib basket trial reduced the size of phase II trials, and losses in the probability of success as a result of not using innovative methods may not be recoverable by running a larger phase II trial. Conclusion Innovative basket trial methods can reduce the size of early phase clinical trials, with sustained improvement in the probability of success of the clinical development plan. We need to do more as a community to improve the adoption of these methods.
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Affiliation(s)
- Adetayo Kasim
- Disease Area Strategy, Oncology Biostatistics, GlaxoSmithKline, Brentford, United Kingdom
| | - Nathan Bean
- Statistics and Data Science – Innovation Hub, GlaxoSmithKline, Philadelphia, PA, United States
| | - Sarah Jo Hendriksen
- Medical and Market Access, Oncology Biostatistics, GlaxoSmithKline, Stevenage, United Kingdom
| | - Tai-Tsang Chen
- Disease Area Strategy, Oncology Biostatistics, GlaxoSmithKline, Philadelphia, PA, United States
| | - Helen Zhou
- Disease Area Strategy, Oncology Biostatistics, GlaxoSmithKline, Philadelphia, PA, United States
| | - Matthew A. Psioda
- Statistics and Data Science – Innovation Hub, GlaxoSmithKline, Philadelphia, PA, United States
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Zheng H, Grayling MJ, Mozgunov P, Jaki T, Wason JMS. Bayesian sample size determination in basket trials borrowing information between subsets. Biostatistics 2023; 24:1000-1016. [PMID: 35993875 DOI: 10.1093/biostatistics/kxac033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 07/22/2022] [Accepted: 07/29/2022] [Indexed: 12/31/2022] Open
Abstract
Basket trials are increasingly used for the simultaneous evaluation of a new treatment in various patient subgroups under one overarching protocol. We propose a Bayesian approach to sample size determination in basket trials that permit borrowing of information between commensurate subsets. Specifically, we consider a randomized basket trial design where patients are randomly assigned to the new treatment or control within each trial subset ("subtrial" for short). Closed-form sample size formulae are derived to ensure that each subtrial has a specified chance of correctly deciding whether the new treatment is superior to or not better than the control by some clinically relevant difference. Given prespecified levels of pairwise (in)commensurability, the subtrial sample sizes are solved simultaneously. The proposed Bayesian approach resembles the frequentist formulation of the problem in yielding comparable sample sizes for circumstances of no borrowing. When borrowing is enabled between commensurate subtrials, a considerably smaller trial sample size is required compared to the widely implemented approach of no borrowing. We illustrate the use of our sample size formulae with two examples based on real basket trials. A comprehensive simulation study further shows that the proposed methodology can maintain the true positive and false positive rates at desired levels.
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Affiliation(s)
- Haiyan Zheng
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK and Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4AX, UK
| | - Michael J Grayling
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4AX, UK
| | - Pavel Mozgunov
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK and University of Regensburg, 93040 Regensburg, Germany
| | - James M S Wason
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4AX, UK
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Cao H, Yao C, Yuan Y. Bayesian approach for design and analysis of medical device trials in the era of modern clinical studies. MEDICAL REVIEW (2021) 2023; 3:408-424. [PMID: 38283256 PMCID: PMC10810749 DOI: 10.1515/mr-2023-0026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 07/22/2023] [Indexed: 01/30/2024]
Abstract
Medical device technology develops rapidly, and the life cycle of a medical device is much shorter than drugs. It is necessary to evaluate the safety and effectiveness of a medical device in a timely manner to keep up with technology flux. Bayesian methods provides an efficient approach to addressing this challenge. In this article, we review the characteristics of the Bayesian approach and some Bayesian designs that were commonly used in medical device regulatory setting, including Bayesian adaptive design, Bayesian diagnostic design, Bayesian multiregional design, and Bayesian label expansion study. We illustrate these designs with medical devices approved by the US Food and Drug Administration (FDA). We also review several innovative Bayesian information borrowing methods, and briefly discuss the challenges and future directions of the Bayesian application in medical device trials. Our objective is to promote the use of the Bayesian approach to accelerate the development of innovative medical devices and their accessibility to patients for effective disease diagnoses and treatments.
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Affiliation(s)
- Han Cao
- Department of Biostatistics, Peking University First Hospital, Beijing, China
- Medical Data Science Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Chen Yao
- Department of Biostatistics, Peking University First Hospital, Beijing, China
- Peking University Clinical Research Institute, Beijing, China
- Hainan Institute of Real World Data, Qionghai, Hainan Province, China
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA
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Harari O, Soltanifar M, Verhoek A, Heeg B. Alone, together: On the benefits of Bayesian borrowing in a meta-analytic setting. Pharm Stat 2023; 22:903-920. [PMID: 37321565 DOI: 10.1002/pst.2318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 04/11/2023] [Accepted: 05/26/2023] [Indexed: 06/17/2023]
Abstract
It is common practice to use hierarchical Bayesian model for the informing of a pediatric randomized controlled trial (RCT) by adult data, using a prespecified borrowing fraction parameter (BFP). This implicitly assumes that the BFP is intuitive and corresponds to the degree of similarity between the populations. Generalizing this model to any K ≥ 1 historical studies, naturally leads to empirical Bayes meta-analysis. In this paper we calculate the Bayesian BFPs and study the factors that drive them. We prove that simultaneous mean squared error reduction relative to an uninformed model is always achievable through application of this model. Power and sample size calculations for a future RCT, designed to be informed by multiple external RCTs, are also provided. Potential applications include inference on treatment efficacy from independent trials involving either heterogeneous patient populations or different therapies from a common class.
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Affiliation(s)
- Ofir Harari
- Real World and Advanced Analytics, Cytel Inc., Vancouver, British Columbia, Canada
- Core Clinical Sciences, Vancouver, British Columbia, Canada
| | - Mohsen Soltanifar
- Real World and Advanced Analytics, Cytel Inc., Vancouver, British Columbia, Canada
- Analytics Division, College of Professional Studies, Northeastern University, Vancouver, British Columbia, Canada
| | | | - Bart Heeg
- RWA & HEOR, Cytel Inc., Rotterdam, The Netherlands
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Chen X, Zhang J, Jiang L, Yan F. Shotgun-2: A Bayesian phase I/II basket trial design to identify indication-specific optimal biological doses. Stat Methods Med Res 2023; 32:443-464. [PMID: 36217826 DOI: 10.1177/09622802221129049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
For novel molecularly targeted agents and immunotherapies, the objective of dose-finding is often to identify the optimal biological dose, rather than the maximum tolerated dose. However, optimal biological doses may not be the same for different indications, challenging the traditional dose-finding framework. Therefore, we proposed a Bayesian phase I/II basket trial design, named "shotgun-2," to identify indication-specific optimal biological doses. A dose-escalation part is conducted in stage I to identify the maximum tolerated dose and admissible dose sets. In stage II, dose optimization is performed incorporating both toxicity and efficacy for each indication. Simulation studies under both fixed and random scenarios show that, compared with the traditional "phase I + cohort expansion" design, the shotgun-2 design is robust and can improve the probability of correctly selecting the optimal biological doses. Furthermore, this study provides a useful tool for identifying indication-specific optimal biological doses and accelerating drug development.
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Affiliation(s)
- Xin Chen
- Research Center of Biostatistics and Computational Pharmacy, 56651China Pharmaceutical University, Nanjing, China
| | - Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, 56651China Pharmaceutical University, Nanjing, China
| | - Liyun Jiang
- Research Center of Biostatistics and Computational Pharmacy, 56651China Pharmaceutical University, Nanjing, China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, 56651China Pharmaceutical University, Nanjing, China
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14
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Asano J, Sato H, Hirakawa A. Practical basket design for binary outcomes with control of family-wise error rate. BMC Med Res Methodol 2023; 23:52. [PMID: 36849940 PMCID: PMC9972792 DOI: 10.1186/s12874-023-01872-1] [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: 04/27/2022] [Accepted: 02/20/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND A basket trial is a type of clinical trial in which eligibility is based on the presence of specific molecular characteristics across subpopulations with different cancer types. The existing basket designs with Bayesian hierarchical models often improve the efficiency of evaluating therapeutic effects; however, these models calibrate the type I error rate based on the results of simulation studies under various selected scenarios. The theoretical control of family-wise error rate (FWER) is important for decision-making regarding drug approval. METHODS In this study, we propose a new Bayesian two-stage design with one interim analysis for controlling FWER at the target level, along with the formulations of type I and II error rates. Since the difficulty lies in the complexity of the theoretical formulation of the type I error rate, we devised the simulation-based method to approximate the type I error rate. RESULTS The proposed design enabled adjustment of the cutoff value to control the FWER at the target value in the final analysis. The simulation studies demonstrated that the proposed design can be used to control the well-approximated FWER below the target value even in situations where the number of enrolled patients differed among subpopulations. CONCLUSIONS The accrual number of patients is sometimes unable to reach the pre-defined value; therefore, existing basket designs may not ensure defined operating characteristics before beginning the trial. The proposed design that enables adjustment of the cutoff value to control FWER at the target value based on the results in the final analysis would be a better alternative.
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Affiliation(s)
- Junichi Asano
- Biostatistics Group, Center for Product Evaluation, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan
- 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
| | - Akihiro Hirakawa
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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15
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Liu S, Takeda K, Rong A. An adaptive biomarker basket design in phase II oncology trials. Pharm Stat 2023; 22:128-142. [PMID: 36163614 DOI: 10.1002/pst.2264] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 07/30/2022] [Accepted: 09/09/2022] [Indexed: 02/01/2023]
Abstract
The phase II basket trial in oncology is a novel design that enables the simultaneous assessment of treatment effects of one anti-cancer targeted agent in multiple cancer types. Biomarkers could potentially associate with the clinical outcomes and re-define clinically meaningful treatment effects. It is therefore natural to develop a biomarker-based basket design to allow the prospective enrichment of the trials with the adaptive selection of the biomarker-positive (BM+) subjects who are most sensitive to the experimental treatment. We propose a two-stage phase II adaptive biomarker basket (ABB) design based on a potential predictive biomarker measured on a continuous scale. At Stage 1, the design incorporates a biomarker cutoff estimation procedure via a hierarchical Bayesian model with biomarker as a covariate (HBMbc). At Stage 2, the design enrolls only BM+ subjects, defined as those with the biomarker values exceeding the biomarker cutoff within each cancer type, and subsequently assesses the early efficacy and/or futility stopping through the pre-defined interim analyses. At the end of the trial, the response rate of all BM+ subjects for each cancer type can guide drug development, while the data from all subjects can be used to further model the relationship between the biomarker value and the clinical outcome for potential future research. The extensive simulation studies show that the ABB design could produce a good estimate of the biomarker cutoff to select BM+ subjects with high accuracy and could outperform the existing phase II basket biomarker cutoff design under various scenarios.
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Affiliation(s)
- Shufang Liu
- Data Science, Astellas Pharma Inc., Northbrook, Illinois, USA
| | - Kentaro Takeda
- Data Science, Astellas Pharma Inc., Northbrook, Illinois, USA
| | - Alan Rong
- Data Science, Astellas Pharma Inc., Northbrook, Illinois, USA
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16
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Collignon O, Schiel A, Burman C, Rufibach K, Posch M, Bretz F. Estimands and Complex Innovative Designs. Clin Pharmacol Ther 2022; 112:1183-1190. [PMID: 35253205 PMCID: PMC9790227 DOI: 10.1002/cpt.2575] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/01/2022] [Indexed: 01/31/2023]
Abstract
Since the release of the ICH E9(R1) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials) document in 2019, the estimand framework has become a fundamental part of clinical trial protocols. In parallel, complex innovative designs have gained increased popularity in drug development, in particular in early development phases or in difficult experimental situations. While the estimand framework is relevant to any study in which a treatment effect is estimated, experience is lacking as regards its application to these designs. In a basket trial for example, should a different estimand be specified for each subpopulation of interest, defined, for example, by cancer site? Or can a single estimand focusing on the general population (defined, for example, by the positivity to a certain biomarker) be used? In the case of platform trials, should a different estimand be proposed for each drug investigated? In this work we discuss possible ways of implementing the estimand framework for different types of complex innovative designs. We consider trials that allow adding or selecting experimental treatment arms, modifying the control arm or the standard of care, and selecting or pooling populations. We also address the potentially data-driven, adaptive selection of estimands in an ongoing trial and disentangle certain statistical issues that pertain to estimation rather than to estimands, such as the borrowing of nonconcurrent information. We hope this discussion will facilitate the implementation of the estimand framework and its description in the study protocol when the objectives of the trial require complex innovative designs.
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Affiliation(s)
| | | | - Carl‐Fredrik Burman
- Statistical Innovation, Data Science & Artificial IntelligenceAstraZeneca Research & DevelopmentGothenburgSweden
| | - Kaspar Rufibach
- Methods, Collaboration, and Outreach Group, Product Development Data SciencesF.Hoffmann‐La RocheBaselSwitzerland
| | - Martin Posch
- Section for Medical StatisticsCenter for Medical Statistics Informatics, and Intelligent SystemsMedical University of ViennaViennaAustria
| | - Frank Bretz
- Section for Medical StatisticsCenter for Medical Statistics Informatics, and Intelligent SystemsMedical University of ViennaViennaAustria,NovartisBaselSwitzerland
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17
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Ouma LO, Grayling MJ, Wason JMS, Zheng H. Bayesian modelling strategies for borrowing of information in randomised basket trials. J R Stat Soc Ser C Appl Stat 2022; 71:2014-2037. [PMID: 36636028 PMCID: PMC9827857 DOI: 10.1111/rssc.12602] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 09/01/2022] [Indexed: 02/01/2023]
Abstract
Basket trials are an innovative precision medicine clinical trial design evaluating a single targeted therapy across multiple diseases that share a common characteristic. To date, most basket trials have been conducted in early-phase oncology settings, for which several Bayesian methods permitting information sharing across subtrials have been proposed. With the increasing interest of implementing randomised basket trials, information borrowing could be exploited in two ways; considering the commensurability of either the treatment effects or the outcomes specific to each of the treatment groups between the subtrials. In this article, we extend a previous analysis model based on distributional discrepancy for borrowing over the subtrial treatment effects ('treatment effect borrowing', TEB) to borrowing over the subtrial groupwise responses ('treatment response borrowing', TRB). Simulation results demonstrate that both modelling strategies provide substantial gains over an approach with no borrowing. TRB outperforms TEB especially when subtrial sample sizes are small on all operational characteristics, while the latter has considerable gains in performance over TRB when subtrial sample sizes are large, or the treatment effects and groupwise mean responses are noticeably heterogeneous across subtrials. Further, we notice that TRB, and TEB can potentially lead to different conclusions in the analysis of real data.
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Affiliation(s)
- Luke O. Ouma
- Population Health Sciences InstituteNewcastle UniversityNewcastle upon TyneUK
| | - Michael J. Grayling
- Population Health Sciences InstituteNewcastle UniversityNewcastle upon TyneUK
| | - James M. S. Wason
- Population Health Sciences InstituteNewcastle UniversityNewcastle upon TyneUK
| | - Haiyan Zheng
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
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18
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Zhao Y, Tang RS, Du Y, Yuan Y. A bayesian platform trial design to simultaneously evaluate multiple drugs in multiple indications with mixed endpoints. Biometrics 2022. [PMID: 35546501 DOI: 10.1111/biom.13694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 04/22/2022] [Indexed: 11/29/2022]
Abstract
In the era of targeted therapies and immunotherapies, the traditional drug development paradigm of testing one drug at a time in one indication has become increasingly inefficient. Motivated by a real-world application, we propose a master-protocol-based Bayesian platform trial design with mixed endpoints (PDME) to simultaneously evaluate multiple drugs in multiple indications, where different subsets of efficacy measures (e.g., objective response and landmark progression-free survival) may be used by different indications as single or multiple endpoints. We propose a Bayesian hierarchical model to accommodate mixed endpoints and reflect the trial structure of indications that are nested within treatments. We develop a two-stage approach that first clusters the indications into homogeneous subgroups and then applies the Bayesian hierarchical model to each subgroup to achieve precision information borrowing. Patients are enrolled in a group-sequential way and adaptively assigned to treatments according to their efficacy estimates. At each interim analysis, the posterior probabilities that the treatment effect exceeds prespecified clinically relevant thresholds are used to drop ineffective treatments and "graduate" effective treatments. Simulations show that the PDME design has desirable operating characteristics compared to existing method. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yujie Zhao
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States
| | - Rui Sammi Tang
- Servier Pharmaceuticals, Boston, MA, 02210, United States
| | - Yeting Du
- Servier Pharmaceuticals, Boston, MA, 02210, United States
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States
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19
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Su L, Chen X, Zhang J, Yan F. Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials. JCO Precis Oncol 2022; 6:e2100394. [PMID: 35263169 PMCID: PMC8926037 DOI: 10.1200/po.21.00394] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
With deeper insight into precision medicine, more innovative oncology trial designs have been proposed to contribute to the characteristics of novel antitumor drugs. Bayesian information borrowing is an indispensable part of these designs, which shows great advantages in improving the efficiency of clinical trials. Bayesian methods provide an effective framework when incorporating information. However, the key point lies in how to choose an appropriate method for complex oncology clinical trials.
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Affiliation(s)
- Liwen Su
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Xin Chen
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
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20
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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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21
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Takeda K, Liu S, Rong A. Constrained hierarchical Bayesian model for latent subgroups in basket trials with two classifiers. Stat Med 2021; 41:298-309. [PMID: 34697822 DOI: 10.1002/sim.9237] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 10/10/2021] [Accepted: 10/12/2021] [Indexed: 02/01/2023]
Abstract
The basket trial in oncology is a novel clinical trial design that enables the simultaneous assessment of one treatment in multiple cancer types. In addition to the usual basket classifier of the cancer types, many recent basket trials further contain other classifiers like biomarkers that potentially affect the clinical outcomes. In other words, the treatment effects in those baskets are often categorized by not only the cancer types but also the levels of other classifiers. Therefore, the assumption of exchangeability is often violated when some baskets are more sensitive to the targeted treatment, whereas others are less. In this article, we propose a constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) to deal with potential heterogeneity of treatment effects due to both the cancer type (first classifier) and another classifier (second classifier) in basket trials. Different baskets defined by multiple cancer types and multiple levels of the second classifier are aggregated into subgroups using a latent subgroup modeling approach. Within each latent subgroup, the treatment effects are similar and approximately exchangeable to borrow information. The CHBM-LS approach evaluates the treatment effect for each basket while allowing adaptive information borrowing across the baskets by identifying latent subgroups. The simulation study shows that the CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios with heterogeneous treatment effects across baskets.
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Affiliation(s)
- Kentaro Takeda
- Data Science, Astellas Pharma Global Development, Inc., Northbrook, Illinois, USA
| | - Shufang Liu
- Data Science, Astellas Pharma Global Development, Inc., Northbrook, Illinois, USA
| | - Alan Rong
- Data Science, Astellas Pharma Global Development, Inc., Northbrook, Illinois, USA
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22
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Jiang L, Nie L, Yan F, Yuan Y. Optimal Bayesian hierarchical model to accelerate the development of tissue-agnostic drugs and basket trials. Contemp Clin Trials 2021; 107:106460. [PMID: 34098036 DOI: 10.1016/j.cct.2021.106460] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 05/01/2021] [Accepted: 05/27/2021] [Indexed: 10/21/2022]
Abstract
Tissue-agnostic trials and basket trials enroll patients based on their genetic biomarkers, not tumor type, in an attempt to determine if a new drug can successfully treat disease conditions based on biomarkers. The Bayesian hierarchical model (BHM) provides an attractive approach to design phase II tissue-agnostic trials by allowing information borrowing across multiple disease types. In this article, we elucidate two intrinsic and inevitable issues that may limit the use of BHM to tissue-agnostic trials: sensitivity to the prior specification of the shrinkage parameter and the competing "interest" among disease types in increasing power and controlling type I error. To address these issues, we propose the optimal BHM (OBHM) and clustered OBHM (COBHM) approaches. In these approach, we first specify a flexible utility function to quantify the tradeoff between type I error and power across disease types based on the study objectives, and then we select the prior of the shrinkage parameter to optimize the utility function of clinical and regulatory interest. COBHM further utilizes a simple Bayesian rule to cluster tumor types into sensitive and insensitive subgroups to achieve more accurate information borrowing. Simulation study shows that the OBHM and especially COBHM have desirable operating characteristics, outperforming some existing methods. COBHM effectively balances power and type I error, addresses the sensitivity of the prior selection, and reduces the "unwarranted" subjectivity in the prior selection. It provides a systematic, rigorous way to apply BHM and solve the common problem of blindingly using a non-informative inverse-gamma prior (with a large variance) or priors arbitrarily chosen that may lead to problematic statistical properties.
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Affiliation(s)
- Liyun Jiang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Lei Nie
- Center for Drug Evaluation and Research, Food and Drug Administration (FDA), Silver Spring, MD, United States of America
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America.
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23
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Pohl M, Krisam J, Kieser M. Categories, components, and techniques in a modular construction of basket trials for application and further research. Biom J 2021; 63:1159-1184. [PMID: 33942894 DOI: 10.1002/bimj.202000314] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 02/15/2021] [Accepted: 04/30/2021] [Indexed: 12/24/2022]
Abstract
Basket trials have become a virulent topic in medical and statistical research during the last decade. The core idea of them is to treat patients, who express the same genetic predisposition-either personally or their disease-with the same treatment irrespective of the location of the disease. The location of the disease defines each basket and the pathway of the treatment uses the common genetic predisposition among the baskets. This opens the opportunity to share information among baskets, which can consequently increase the information of the basket-wise response with respect to the investigated treatment. This further allows dynamic decisions regarding futility and efficacy of individual baskets during the ongoing trial. Several statistical designs have been proposed on how a basket trial can be conducted and this has left an unclear situation with many options. The different designs propose different mathematical and statistical techniques, different decision rules, and also different trial purposes. This paper presents a broad overview of existing designs, categorizes them, and elaborates their similarities and differences. A uniform and consistent notation facilitates the first contact, introduction, and understanding of the statistical methodologies and techniques used in basket trials. Finally, this paper presents a modular approach for the construction of basket trials in applied medical science and forms a base for further research of basket trial designs and their techniques.
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Affiliation(s)
- Moritz Pohl
- Institute of Medical Biometry and Informatics, Medical Biometry, University of Heidelberg, Heidelberg, Germany
| | - Johannes Krisam
- Institute of Medical Biometry and Informatics, Medical Biometry, University of Heidelberg, Heidelberg, Germany
| | - Meinhard Kieser
- Institute of Medical Biometry and Informatics, Medical Biometry, University of Heidelberg, Heidelberg, Germany
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24
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Jiang L, Li R, Yan F, Yap TA, Yuan Y. Shotgun: A Bayesian seamless phase I-II design to accelerate the development of targeted therapies and immunotherapy. Contemp Clin Trials 2021; 104:106338. [PMID: 33711459 PMCID: PMC8180491 DOI: 10.1016/j.cct.2021.106338] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 02/22/2021] [Accepted: 02/27/2021] [Indexed: 12/13/2022]
Abstract
Drug development of novel antitumor agents is conventionally divided by phase and cancer indication. With the advent of new molecularly targeted therapies and immunotherapies, this approach has become inefficient and dysfunctional. We propose a Bayesian seamless phase I-II "shotgun" design to evaluate the safety and antitumor efficacy of a new drug in multiple cancer indications simultaneously. "Shotgun" is used to describe the design feature that the trial begins with an all-comer dose finding phase to identify the maximum tolerated dose (MTD) or recommended phase II dose (RP2D), and then is seamlessly split to multiple indication-specific cohort expansions. Patients treated during dose finding are rolled over to the cohort expansion for more efficient evaluation of efficacy, while patients enrolled in cohort expansion contribute to the continuous learning of the safety and tolerability of the new drug. During cohort expansion, interim analyses are performed to discontinue ineffective or unsafe expansion cohorts early. To improve the efficiency of such interim analyses, we propose a clustered Bayesian hierarchical model (CBHM) to adaptively borrow information across indications. A simulation study shows that compared to conventional approaches and the standard Bayesian hierarchical model, the shotgun design has substantially higher probabilities to discover indications that are responsive to the treatment in question, and is associated with a reasonable false discovery rate. The shotgun provides a phase I-II trial design for accelerating drug development and to build a more robust foundation for subsequent phase III trials. The proposed CBHM methodology also provides an efficient design for basket trials.
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Affiliation(s)
- Liyun Jiang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Ruobing Li
- The Center for Drug Evaluation, The National Medical Products Administration, Beijing, China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Timothy A Yap
- Department of Investigational Cancer Therapeutics (Phase I Program), The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA; Khalifa Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA; The Institute for Applied Cancer Science, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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