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Ryan EG, Gao CX, Grantham KL, Thao LTP, Charles-Nelson A, Bowden R, Herschtal A, Lee KJ, Forbes AB, Heritier S, Phillipou A, Wolfe R. Advancing randomized controlled trial methodologies: The place of innovative trial design in eating disorders research. Int J Eat Disord 2024; 57:1337-1349. [PMID: 38469971 DOI: 10.1002/eat.24187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/26/2024] [Accepted: 02/26/2024] [Indexed: 03/13/2024]
Abstract
Randomized controlled trials can be used to generate evidence on the efficacy and safety of new treatments in eating disorders research. Many of the trials previously conducted in this area have been deemed to be of low quality, in part due to a number of practical constraints. This article provides an overview of established and more innovative clinical trial designs, accompanied by pertinent examples, to highlight how design choices can enhance flexibility and improve efficiency of both resource allocation and participant involvement. Trial designs include individually randomized, cluster randomized, and designs with randomizations at multiple time points and/or addressing several research questions (master protocol studies). Design features include the use of adaptations and considerations for pragmatic or registry-based trials. The appropriate choice of trial design, together with rigorous trial conduct, reporting and analysis, can establish high-quality evidence to advance knowledge in the field. It is anticipated that this article will provide a broad and contemporary introduction to trial designs and will help researchers make informed trial design choices for improved testing of new interventions in eating disorders. PUBLIC SIGNIFICANCE: There is a paucity of high quality randomized controlled trials that have been conducted in eating disorders, highlighting the need to identify where efficiency gains in trial design may be possible to advance the eating disorder research field. We provide an overview of some key trial designs and features which may offer solutions to practical constraints and increase trial efficiency.
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Affiliation(s)
- Elizabeth G Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Caroline X Gao
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Orygen, Melbourne, Victoria, Australia
| | - Kelsey L Grantham
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Le Thi Phuong Thao
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Anaïs Charles-Nelson
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Rhys Bowden
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Alan Herschtal
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Katherine J Lee
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Stephane Heritier
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Andrea Phillipou
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Orygen, Melbourne, Victoria, Australia
- Department of Psychological Sciences, Swinburne University of Technology, Melbourne, Victoria, Australia
- Department of Mental Health, Austin Health, Melbourne, Victoria, Australia
- Department of Mental Health, St Vincent's Hospital, Melbourne, Victoria, Australia
| | - Rory Wolfe
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Wang J, Yu B, Dou YN, Mascaro J. Biomarker-Driven Oncology Trial Design and Subgroup Characterization: Challenges and Potential Solutions. JCO Precis Oncol 2024; 8:e2400116. [PMID: 38848518 DOI: 10.1200/po.24.00116] [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: 02/23/2024] [Revised: 04/11/2024] [Accepted: 04/24/2024] [Indexed: 06/09/2024] Open
Abstract
In oncology drug development, using biomarkers to select a study population more likely to benefit from a therapeutic effect is critical to increase the efficiency of a clinical trial in demonstrating effectiveness. This perspective delves into therapeutic product approvals that were tested in pivotal trials with all-comers populations, but ultimately received US Food and Drug Administration approval for use within specific patient subgroups identified by biomarkers. Despite initial designs for efficacy and safety assessments in overall populations, a favorable benefit-risk assessment was primarily established in biomarker-positive subgroups. Analyzing these cases, we summarize key considerations pivotal to totality of evidence for regulatory benefit-risk assessments for biomarker-defined subgroup versus all-comers approvals, including biological and clinical rationales, biomarker prevalence, safety data, overall trial design, and subgroup efficacy characterization. Furthermore, a decision tree is proposed to guide optimal clinical trial design, delineating between patient enrichment and stratification, accounting for key factors including biological and clinical rationale, marker type (discreate or continuous), prevalence, assay readiness, and turnaround times for marker assessment. Finally, a recommended approach for subgroup characterization involves prespecifying magnitude of improvement that would be considered clinically meaningful in the biomarker-negative subgroup, which can be supplemented with methodologies such as Bayesian to incorporate evidence from similar studies when available. In summary, this perspective underscores the importance of clinical trial innovations, statistical methodologies and regulatory considerations, to optimize biomarker-driven drug development for patients with cancer.
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Affiliation(s)
- Jian Wang
- Oncology Regulatory Science, Strategy & Excellence, AstraZeneca, Gaithersburg, MD
| | - Binbing Yu
- Biometrics Oncology, AstraZeneca, Gaithersburg, MD
| | - Yannan Nancy Dou
- Oncology Regulatory Science, Strategy & Excellence, AstraZeneca, Gaithersburg, MD
| | - Jacques Mascaro
- Oncology Regulatory Science, Strategy & Excellence, AstraZeneca, Gaithersburg, MD
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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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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 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, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - 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, USA
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Kanapka L, Ivanova A. A frequentist design for basket trials using adaptive lasso. Stat Med 2024; 43:156-172. [PMID: 37919834 DOI: 10.1002/sim.9947] [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: 07/26/2023] [Revised: 09/26/2023] [Accepted: 09/29/2023] [Indexed: 11/04/2023]
Abstract
A basket trial aims to expedite the drug development process by evaluating a new therapy in multiple populations within the same clinical trial. Each population, referred to as a "basket", can be defined by disease type, biomarkers, or other patient characteristics. The objective of a basket trial is to identify the subset of baskets for which the new therapy shows promise. The conventional approach would be to analyze each of the baskets independently. Alternatively, several Bayesian dynamic borrowing methods have been proposed that share data across baskets when responses appear similar. These methods can achieve higher power than independent testing in exchange for a risk of some inflation in the type 1 error rate. In this paper we propose a frequentist approach to dynamic borrowing for basket trials using adaptive lasso. Through simulation studies we demonstrate adaptive lasso can achieve similar power and type 1 error to the existing Bayesian methods. The proposed approach has the benefit of being easier to implement and faster than existing methods. In addition, the adaptive lasso approach is very flexible: it can be extended to basket trials with any number of treatment arms and any type of endpoint.
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Affiliation(s)
- Lauren Kanapka
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Anastasia Ivanova
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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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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Waeijen-Smit K, Crutsen M, Keene S, Miravitlles M, Crisafulli E, Torres A, Mueller C, Schuetz P, Ringbæk TJ, Fabbian F, Mekov E, Harries TH, Lun CT, Ergan B, Esteban C, Quintana Lopez JM, López-Campos JL, Chang CL, Hancox RJ, Shafuddin E, Ellis H, Janson C, Suppli Ulrik C, Gudmundsson G, Epstein D, Dominguez J, Lacoma A, Osadnik C, Alia I, Spannella F, Karakurt Z, Mehravaran H, Utens C, de Kruif MD, Ko FWS, Trethewey SP, Turner AM, Bumbacea D, Murphy PB, Vermeersch K, Zilberman-Itskovich S, Steer J, Echevarria C, Bourke SC, Lane N, de Batlle J, Sprooten RTM, Russell R, Faverio P, Cross JL, Prins HJ, Spruit MA, Simons SO, Houben-Wilke S, Franssen FME. Global mortality and readmission rates following COPD exacerbation-related hospitalisation: a meta-analysis of 65 945 individual patients. ERJ Open Res 2024; 10:00838-2023. [PMID: 38410700 PMCID: PMC10895439 DOI: 10.1183/23120541.00838-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 12/16/2023] [Indexed: 02/28/2024] Open
Abstract
Background Exacerbations of COPD (ECOPD) have a major impact on patients and healthcare systems across the world. Precise estimates of the global burden of ECOPD on mortality and hospital readmission are needed to inform policy makers and aid preventive strategies to mitigate this burden. The aims of the present study were to explore global in-hospital mortality, post-discharge mortality and hospital readmission rates after ECOPD-related hospitalisation using an individual patient data meta-analysis (IPDMA) design. Methods A systematic review was performed identifying studies that reported in-hospital mortality, post-discharge mortality and hospital readmission rates following ECOPD-related hospitalisation. Data analyses were conducted using a one-stage random-effects meta-analysis model. This study was conducted and reported in accordance with the PRISMA-IPD statement. Results Data of 65 945 individual patients with COPD were analysed. The pooled in-hospital mortality rate was 6.2%, pooled 30-, 90- and 365-day post-discharge mortality rates were 1.8%, 5.5% and 10.9%, respectively, and pooled 30-, 90- and 365-day hospital readmission rates were 7.1%, 12.6% and 32.1%, respectively, with noticeable variability between studies and countries. Strongest predictors of mortality and hospital readmission included noninvasive mechanical ventilation and a history of two or more ECOPD-related hospitalisations <12 months prior to the index event. Conclusions This IPDMA stresses the poor outcomes and high heterogeneity of ECOPD-related hospitalisation across the world. Whilst global standardisation of the management and follow-up of ECOPD-related hospitalisation should be at the heart of future implementation research, policy makers should focus on reimbursing evidence-based therapies that decrease (recurrent) ECOPD.
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Affiliation(s)
- Kiki Waeijen-Smit
- Department of Research and Development, Ciro, Horn, the Netherlands
- Department of Respiratory Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health Medicine and Life Sciences, Maastricht University Medical Centre (MUMC+), Maastricht, the Netherlands
| | - Mieke Crutsen
- Pulmonary Function and Exercise Testing Laboratory, MUMC+, Maastricht, the Netherlands
| | - Spencer Keene
- Department of Research and Development, Ciro, Horn, the Netherlands
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- Department of Clinical Pharmacy and Toxicology, MUMC+, Maastricht, the Netherlands
| | - Marc Miravitlles
- Pneumology Department, Hospital Universitari Vall d'Hebron, Vall d'Hebron Institut de Recerca, Vall d'Hebron Barcelona Hospital Campus, Ciber de Enfermedades Respiratorias (CIBERES), Barcelona, Spain
| | - Ernesto Crisafulli
- Respiratory Medicine Unit, Department of Medicine, University of Verona, Verona, Italy
| | - Antoni Torres
- Department of Pulmonology, Hospital Clinic of Barcelona and University of Barcelona. Institut d'Investigacions Biomèdiques August Pi i Sunyer, Institución Catalana de Investigación y Estudios Avanzados, CIBERES, Barcelona, Spain
| | - Christian Mueller
- Cardiovascular Research Institute Base, Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Philipp Schuetz
- Medical University Department, Kantonsspital Aarau, Aarau, Switzerland
| | - Thomas J Ringbæk
- Department of Respiratory Medicine, Copenhagen University Hospital-Hvidovre, Hvidovre, Denmark
| | - Fabio Fabbian
- Department of Medical Sciences, Faculty of Medicine, Pharmacy and Prevention, University of Ferrara, University Hospital of Ferrara, Ferrara, Italy
| | - Evgeni Mekov
- Department of Occupational Diseases, Medical University Sofia, Sofia, Bulgaria
| | - Timothy H Harries
- Department of Population Health Sciences, School of Life Course and Population Sciences, King's College London, London, UK
| | - Chung-Tat Lun
- Department of Medicine and ICU, Alice Ho Miu Ling Nethersole Hospital, Hong Kong, Hong Kong
| | - Begum Ergan
- Dokuz Eylul University, Faculty of Medicine, Department of Pulmonary and Critical Care, Division of Critical Care, Izmir, Turkey
| | - Cristóbal Esteban
- Respiratory Department, Hospital Galdakao, Galdakao, Spain
- Instituto BioCruces-Bizkaia, Barakaldo, Spain
- Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas, Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud, Bizkaia, Spain
| | - Jose M Quintana Lopez
- Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas, Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud, Bizkaia, Spain
- Unidad de Investigación, Hospital Galdakao-Usansolo, Galdakao, Spain
| | - José Luis López-Campos
- Unidad Médico-Quirúrgica de Enfermedades Respiratorias, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/Universidad de Sevilla, Seville, Spain
- CIBERES, Instituto de Salud Carlos III, Madrid, Spain
| | - Catherina L Chang
- Department of Respiratory Medicine, Waikato Hospital, Hamilton, New Zealand
| | - Robert J Hancox
- Department of Respiratory Medicine, Waikato Hospital, Hamilton, New Zealand
- Department of Preventive and Social Medicine, University of Otago, Dunedin, New Zealand
| | | | - Hollie Ellis
- Department of Respiratory Medicine, Waikato Hospital, Hamilton, New Zealand
| | - Christer Janson
- Department of Medical Sciences: Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden
| | - Charlotte Suppli Ulrik
- Department of Respiratory Medicine, Copenhagen University Hospital-Hvidovre, Hvidovre, Denmark
| | - Gunnar Gudmundsson
- Landspitali - The National University Hospital of Iceland, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland School of Health Sciences, Reykjavik, Iceland
| | - Danny Epstein
- Critical Care Division, Rambam Health Care Campus, Haifa, Israel
| | - José Dominguez
- Servei de Microbiologia, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Universitat Autònoma de Barcelona, CIBERES, Barcelona, Spain
| | - Alicia Lacoma
- Servei de Microbiologia, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Universitat Autònoma de Barcelona, CIBERES, Barcelona, Spain
| | | | - Inmaculada Alia
- Intensive Care Units, Hospital Universitario de Getafe, CIBERES, Getafe, Spain
| | - Francesco Spannella
- Internal Medicine and Geriatrics, Hypertension Excellence Centre of the European Society of Hypertension, IRCCS INRCA, Ancona, Italy
- Department of Clinical and Molecular Sciences, University Politecnica delle Marche, Ancona, Italy
| | - Zuhal Karakurt
- Respiratory Critical Care Unit, University of Health Sciences Istanbul Sureyyapasa Chest Diseases and Thoracic Surgery Training and Research Hospital, Istanbul, Turkey
| | - Hossein Mehravaran
- Pulmonary and Critical Care Division, Department of Internal Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Cecile Utens
- Libra, Rehabilitation and Audiology, Eindhoven, the Netherlands
| | - Martijn D de Kruif
- Department of Pulmonary Medicine, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Fanny Wai San Ko
- Department of Medicine and Therapeutics, Prince of Wales Hospital, Hong Kong, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Samuel P Trethewey
- Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, UK
- University of Exeter, Exeter, UK
| | - Alice M Turner
- Institute for Applied Health Research, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Dragos Bumbacea
- Department of Pneumology and Acute Respiratory Care, Elias Emergency University Hospital, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Patrick B Murphy
- Lane Fox Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK
- Centre for Human and Applied Physiological Sciences, King's College, London, UK
| | - Kristina Vermeersch
- Department of Chronic Diseases, Metabolism and Ageing, Research Group BREATHE, KU Leuven, Leuven, Belgium
| | - Shani Zilberman-Itskovich
- Nephrology Division, Assaf-Harofeh (Shamir) Medical Center, Be'er Ya'akov, Israel
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - John Steer
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Respiratory Department, North Tyneside General Hospital, North Shields, UK
| | - Carlos Echevarria
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Respiratory Department, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Stephen C Bourke
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Respiratory Department, North Tyneside General Hospital, North Shields, UK
| | - Nicholas Lane
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Respiratory Department, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Jordi de Batlle
- Group of Translational Research in Respiratory Medicine, Institut de Recerca Biomèdica de Lleida (Fundació Dr Pifarré), Lleida, Spain
- CIBERES, Madrid, Spain
| | - Roy T M Sprooten
- Department of Respiratory Medicine, MUMC+, Maastricht, The Netherlands
| | - Richard Russell
- School of Immunology and Microbial Sciences, Guy's Campus, Kings College, London, UK
| | - Paola Faverio
- School of Medicine and Surgery, University of Milan Bicocca, Respiratory Unit, San Gerardo Hospital, ASST Monza, Monza, Italy
| | - Jane L Cross
- Faculty of Medicine and Health, University of East Anglia, Norwich, UK
| | - Hendrik J Prins
- Department of PMR, Libra, Rehabilitation and Audiology, Eindhoven, The Netherlands
- Department of PMR, Anna Hospital, Geldrop, The Netherlands
- Department of PMR, Catharina Hospital, Eindhoven, The Netherlands
| | - Martijn A Spruit
- Department of Research and Development, Ciro, Horn, the Netherlands
- Department of Respiratory Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health Medicine and Life Sciences, Maastricht University Medical Centre (MUMC+), Maastricht, the Netherlands
| | - Sami O Simons
- Department of Respiratory Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health Medicine and Life Sciences, Maastricht University Medical Centre (MUMC+), Maastricht, the Netherlands
| | | | - Frits M E Franssen
- Department of Research and Development, Ciro, Horn, the Netherlands
- Department of Respiratory Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health Medicine and Life Sciences, Maastricht University Medical Centre (MUMC+), Maastricht, the Netherlands
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Su L, Chen X, Zhang J, Yan F. MIDAS-2: an enhanced Bayesian platform design for immunotherapy combinations with subgroup efficacy exploration. J Biopharm Stat 2023:1-21. [PMID: 38131109 DOI: 10.1080/10543406.2023.2292211] [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: 02/09/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023]
Abstract
Although immunotherapy combinations have revolutionised cancer treatment, the rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging. This necessitates innovative, integrated, and efficient trial designs. In this study, we extend the MIDAS design to include subgroup exploration and propose an enhanced Bayesian information borrowing platform design called MIDAS-2. MIDAS-2 enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We use a regression model to characterize the efficacy pattern in subgroups. Information borrowing is applied through Bayesian hierarchical modelling to improve trial efficiency considering the limited sample size in subgroups. Time trend calibration is also employed to avoid potential baseline drifts. Simulation results demonstrate that MIDAS-2 yields high probabilities for identifying the effective drug combinations as well as promising subgroups, facilitating appropriate selection of the best treatments for each subgroup. The proposed design is robust against small time trend drifts, and the type I error is successfully controlled after calibration when a large drift is expected. Overall, MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion.
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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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9
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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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10
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Daniells L, Mozgunov P, Bedding A, Jaki T. A comparison of Bayesian information borrowing methods in basket trials and a novel proposal of modified exchangeability-nonexchangeability method. Stat Med 2023; 42:4392-4417. [PMID: 37614070 PMCID: PMC10962580 DOI: 10.1002/sim.9867] [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: 12/15/2022] [Revised: 07/12/2023] [Accepted: 07/24/2023] [Indexed: 08/25/2023]
Abstract
Recent innovation in trial design to improve study efficiency has led to the development of basket trials in which a single therapeutic treatment is tested on several patient populations, each of which forms a basket. In a common setting, patients across all baskets share a genetic marker and as such, an assumption can be made that all patients may have a homogeneous response to treatments. Bayesian information borrowing procedures utilize this assumption to draw on information regarding the response in one basket when estimating the response rate in others. This can improve power and precision of estimates particularly in the presence of small sample sizes, however, can come at a cost of biased estimates and an inflation of error rates, bringing into question validity of trial conclusions. We review and compare the performance of several Bayesian borrowing methods, namely: the Bayesian hierarchical model (BHM), calibrated Bayesian hierarchical model (CBHM), exchangeability-nonexchangeability (EXNEX) model and a Bayesian model averaging procedure. A generalization of the CBHM is made to account for unequal sample sizes across baskets. We also propose a modification of the EXNEX model that allows for better control of a type I error. The proposed method uses a data-driven approach to account for the homogeneity of the response data, measured through Hellinger distances. Through an extensive simulation study motivated by a real basket trial, for both equal and unequal sample sizes across baskets, we show that in the presence of a basket with a heterogeneous response, unlike the other methods discussed, this model can control type I error rates to a nominal level whilst yielding improved power.
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Affiliation(s)
- Libby Daniells
- STOR‐i Centre for Doctoral Training, Department of Mathematics and StatisticsLancaster UniversityLancasterUK
| | - Pavel Mozgunov
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
| | | | - Thomas Jaki
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Faculty of Informatics and Data ScienceUniversity of RegensburgRegensburgGermany
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11
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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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12
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Zheng H, Jaki T, Wason JM. Bayesian sample size determination using commensurate priors to leverage preexperimental data. Biometrics 2023; 79:669-683. [PMID: 35253201 PMCID: PMC10952893 DOI: 10.1111/biom.13649] [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: 03/08/2021] [Accepted: 02/23/2022] [Indexed: 12/01/2022]
Abstract
This paper develops Bayesian sample size formulae for experiments comparing two groups, where relevant preexperimental information from multiple sources can be incorporated in a robust prior to support both the design and analysis. We use commensurate predictive priors for borrowing of information and further place Gamma mixture priors on the precisions to account for preliminary belief about the pairwise (in)commensurability between parameters that underpin the historical and new experiments. Averaged over the probability space of the new experimental data, appropriate sample sizes are found according to criteria that control certain aspects of the posterior distribution, such as the coverage probability or length of a defined density region. Our Bayesian methodology can be applied to circumstances that compare two normal means, proportions, or event times. When nuisance parameters (such as variance) in the new experiment are unknown, a prior distribution can further be specified based on preexperimental data. Exact solutions are available based on most of the criteria considered for Bayesian sample size determination, while a search procedure is described in cases for which there are no closed-form expressions. We illustrate the application of our sample size formulae in the design of clinical trials, where pretrial information is available to be leveraged. Hypothetical data examples, motivated by a rare-disease trial with an elicited expert prior opinion, and a comprehensive performance evaluation of the proposed methodology are presented.
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Affiliation(s)
- Haiyan Zheng
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Population Health Sciences InstituteNewcastle UniversityNewcastle upon TyneUK
| | - Thomas Jaki
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Department of Mathematics and StatisticsLancaster UniversityLancasterUK
| | - James M.S. Wason
- Population Health Sciences InstituteNewcastle UniversityNewcastle upon TyneUK
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13
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Whitehead LE, Sailer O, Witham MD, Wason JMS. Bayesian borrowing for basket trials with longitudinal outcomes. Stat Med 2023. [PMID: 37120858 DOI: 10.1002/sim.9751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/28/2023] [Accepted: 04/16/2023] [Indexed: 05/02/2023]
Abstract
Basket trials are a novel clinical trial design in which a single intervention is investigated in multiple patient subgroups, or "baskets." They offer the opportunity to share information between subgroups, potentially increasing power to detect treatment effects. Basket trials offer several advantages over running a series of separate trials, including reduced sample sizes, increased efficiency, and reduced costs. Primarily, basket trials have been undertaken in Phase II oncology settings, but could be a promising design in other areas where a shared underlying biological mechanism drives different diseases. One such area is chronic aging-related diseases. However, trials in this area frequently have longitudinal outcomes, and therefore suitable methods are needed to share information in this setting. In this paper, we extend three Bayesian borrowing methods for a basket design with continuous longitudinal endpoints. We demonstrate our methods on a real-world dataset and in a simulation study where the aim is to detect positive basketwise treatment effects. Methods are compared with standalone analysis of each basket without borrowing. Our results confirm that methods that share information can improve power to detect positive treatment effects and increase precision over independent analysis in many scenarios. In highly heterogeneous scenarios, there is a trade-off between increased power and increased risk of type I errors. Our proposed methods for basket trials with continuous longitudinal outcomes aim to facilitate their applicability in the area of aging related diseases. Choice of method should be made based on trial priorities and the expected basketwise distribution of treatment effects.
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Affiliation(s)
- Lou E Whitehead
- Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Oliver Sailer
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Miles D Witham
- AGE Research Group, NIHR Newcastle Biomedical Research Centre, Newcastle University and Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, UK
| | - James M S Wason
- Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
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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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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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16
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Hobbs BP, Pestana RC, Zabor EC, Kaizer AM, Hong DS. Basket Trials: Review of Current Practice and Innovations for Future Trials. J Clin Oncol 2022; 40:3520-3528. [PMID: 35537102 PMCID: PMC10476732 DOI: 10.1200/jco.21.02285] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 12/06/2021] [Accepted: 03/31/2022] [Indexed: 02/05/2023] Open
Abstract
Advances in biology and immunology have elucidated genetic and immunologic origins of cancer. Innovations in sequencing technologies revealed that distinct cancer histologies shared common genetic and immune phenotypic traits. Pharmacologic developments made it possible to target these alterations, yielding novel classes of targeted agents whose therapeutic potential span multiple tumor types. Basket trials, one type of master protocol, emerged as a tool for evaluating biomarker-targeted therapies among multiple tumor histologies. Conventionally conducted within the phase II setting and designed to estimate high and durable objective responses, basket trials pose challenges to statistical design and interpretation of results. This article reviews basket trials implemented in oncology studies and discusses issues related to their statistical design and analysis.
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Affiliation(s)
- Brian P. Hobbs
- Dell Medical School, The University of Texas at Austin, Austin, TX
| | - Roberto Carmagnani Pestana
- Centro de Oncologia e Hematologia Einstein Familia Dayan-Daycoval, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Emily C. Zabor
- Quantitative Health Sciences & Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH
| | - Alexander M. Kaizer
- Biostatistics and Informatics, University of Colorado-Anschutz Medical Campus, Aurora, CO
| | - David S. Hong
- Investigational Cancer Therapeutics, University of Texas M.D. Anderson Cancer Center, Houston, TX
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17
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Ouma LO, Wason JMS, Zheng H, Wilson N, Grayling M. Design and analysis of umbrella trials: Where do we stand? Front Med (Lausanne) 2022; 9:1037439. [PMID: 36313987 PMCID: PMC9596938 DOI: 10.3389/fmed.2022.1037439] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 09/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background The efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention. Methods We conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology. Findings We identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse. Conclusions Umbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.
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Affiliation(s)
- Luke O. Ouma
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - James M. S. Wason
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Haiyan Zheng
- Medical Research Council (MRC) Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Nina Wilson
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Michael Grayling
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
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18
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Kaizer A, Zabor E, Nie L, Hobbs B. Bayesian and frequentist approaches to sequential monitoring for futility in oncology basket trials: A comparison of Simon's two-stage design and Bayesian predictive probability monitoring with information sharing across baskets. PLoS One 2022; 17:e0272367. [PMID: 35917296 PMCID: PMC9345361 DOI: 10.1371/journal.pone.0272367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 07/18/2022] [Indexed: 11/18/2022] Open
Abstract
This article discusses and compares statistical designs of basket trial, from both frequentist and Bayesian perspectives. Baskets trials are used in oncology to study interventions that are developed to target a specific feature (often genetic alteration or immune phenotype) that is observed across multiple tissue types and/or tumor histologies. Patient heterogeneity has become pivotal to the development of non-cytotoxic treatment strategies. Treatment targets are often rare and exist among several histologies, making prospective clinical inquiry challenging for individual tumor types. More generally, basket trials are a type of master protocol often used for label expansion. Master protocol is used to refer to designs that accommodates multiple targets, multiple treatments, or both within one overarching protocol. For the purpose of making sequential decisions about treatment futility, Simon's two-stage design is often embedded within master protocols. In basket trials, this frequentist design is often applied to independent evaluations of tumor histologies and/or indications. In the tumor agnostic setting, rarer indications may fail to reach the sample size needed for even the first evaluation for futility. With recent innovations in Bayesian methods, it is possible to evaluate for futility with smaller sample sizes, even for rarer indications. Novel Bayesian methodology for a sequential basket trial design based on predictive probability is introduced. The Bayesian predictive probability designs allow interim analyses with any desired frequency, including continual assessments after each patient observed. The sequential design is compared with and without Bayesian methods for sharing information among a collection of discrete, and potentially non-exchangeable tumor types. Bayesian designs are compared with Simon's two-stage minimax design.
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Affiliation(s)
- Alexander Kaizer
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado-Anschutz Medical Campus, Aurora, CO, United States of America
| | - Emily Zabor
- Department of Quantitative Health Sciences & Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, United States of America
| | - Lei Nie
- Division of Biometrics II, Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States of America
| | - Brian Hobbs
- Department of Population Health, University of Texas-Austin, Austin, TX, United States of America
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19
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Mukherjee A, Grayling MJ, Wason JMS. Adaptive Designs: Benefits and Cautions for Neurosurgery Trials. World Neurosurg 2022; 161:316-322. [PMID: 35505550 DOI: 10.1016/j.wneu.2021.07.061] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 07/11/2021] [Accepted: 07/12/2021] [Indexed: 10/18/2022]
Abstract
BACKGROUND It is well accepted that randomized controlled trials provide the greatest quality of evidence about effectiveness and safety of new interventions. In neurosurgery, randomized controlled trials face challenges, with their use remaining relatively low compared with other clinical areas. Adaptive designs have emerged as a method for improving the efficiency and patient benefit of trials. They allow modifications to the trial design to be made as patient outcome data are collected. The benefit they provide is highly variable, predominantly governed by the time taken to observe the primary endpoint compared with the planned recruitment rate. They also face challenges in design, conduct, and reporting. METHODS We provide an overview of the benefits and challenges of adaptive designs, with a focus on neurosurgery applications. To investigate how often an adaptive design may be advantageous in neurosurgery, we extracted data on recruitment rates and endpoint lengths for ongoing neurosurgery trials registered in ClinicalTrials.gov. RESULTS We found that a majority of neurosurgery trials had a relatively short endpoint length compared with the planned recruitment period and therefore may benefit from an adaptive trial. However, we did not identify any ongoing ClinicalTrials.gov registered neurosurgery trials that mentioned using an adaptive design. CONCLUSIONS Adaptive designs may provide benefits to neurosurgery trials and should be considered for use more widely. Use of some types of adaptive design, such as multiarm multistage, may further increase the number of interventions that can be tested with limited patient and financial resources.
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Affiliation(s)
- Aritra Mukherjee
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Newcastle upon Tyne, United Kingdom
| | - Michael J Grayling
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Newcastle upon Tyne, United Kingdom
| | - James M S Wason
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Newcastle upon Tyne, United Kingdom.
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20
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Zheng H, Jaki T, Wason JMS. Bayesian sample size determination using commensurate priors to leverage pre-experimental data. Biometrics 2022; 79:669-683. [PMID: 38523700 PMCID: PMC7614678 DOI: 10.1111/j.1541-0420.2005.00454.x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
This paper develops Bayesian sample size formulae for experiments comparing two groups, where relevant pre-experimental information from multiple sources can be incorporated in a robust prior to support both the design and analysis. We use commensurate predictive priors for borrowing of information, and further place Gamma mixture priors on the precisions to account for preliminary belief about the pairwise (in)commensurability between parameters that underpin the historical and new experiments. Averaged over the probability space of the new experimental data, appropriate sample sizes are found according to criteria that control certain aspects of the posterior distribution, such as the coverage probability or length of a defined density region. Our Bayesian methodology can be applied to circumstances that compare two normal means, proportions or event times. When nuisance parameters (such as variance) in the new experiment are unknown, a prior distribution can further be specified based on pre-experimental data. Exact solutions are available based on most of the criteria considered for Bayesian sample size determination, while a search procedure is described in cases for which there are no closed-form expressions. We illustrate the application of our sample size formulae in the design of clinical trials, where pre-trial information is available to be leveraged. Hypothetical data examples, motivated by a rare-disease trial with elicited expert prior opinion, and a comprehensive performance evaluation of the proposed methodology are presented.
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Affiliation(s)
- Haiyan Zheng
- MRC Biostatistics Unit, University of Cambridge, U.K
- Population Health Sciences Institute, Newcastle University, U.K
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, U.K
- Department of Mathematics and Statistics, Lancaster University, U.K
| | - James M S Wason
- Population Health Sciences Institute, Newcastle University, U.K
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21
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Grayling MJ, Bigirumurame T, Cherlin S, Ouma L, Zheng H, Wason JMS. Innovative trial approaches in immune-mediated inflammatory diseases: current use and future potential. BMC Rheumatol 2021; 5:21. [PMID: 34210348 PMCID: PMC8252241 DOI: 10.1186/s41927-021-00192-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 04/09/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Despite progress that has been made in the treatment of many immune-mediated inflammatory diseases (IMIDs), there remains a need for improved treatments. Randomised controlled trials (RCTs) provide the highest form of evidence on the effectiveness of a potential new treatment regimen, but they are extremely expensive and time consuming to conduct. Consequently, much focus has been given in recent years to innovative design and analysis methods that could improve the efficiency of RCTs. In this article, we review the current use and future potential of these methods within the context of IMID trials. METHODS We provide a review of several innovative methods that would provide utility in IMID research. These include novel study designs (adaptive trials, Sequential Multi-Assignment Randomised Trials, basket, and umbrella trials) and data analysis methodologies (augmented analyses of composite responder endpoints, using high-dimensional biomarker information to stratify patients, and emulation of RCTs from routinely collected data). IMID trials are now well-placed to embrace innovative methods. For example, well-developed statistical frameworks for adaptive trial design are ready for implementation, whilst the growing availability of historical datasets makes the use of Bayesian methods particularly applicable. To assess whether and how these innovative methods have been used in practice, we conducted a review via PubMed of clinical trials pertaining to any of 51 IMIDs that were published between 2018 and 20 in five high impact factor clinical journals. RESULTS Amongst 97 articles included in the review, 19 (19.6%) used an innovative design method, but most of these were relatively straightforward examples of innovative approaches. Only two (2.1%) reported the use of evidence from routinely collected data, cohorts, or biobanks. Eight (9.2%) collected high-dimensional data. CONCLUSIONS Application of innovative statistical methodology to IMID trials has the potential to greatly improve efficiency, to generalise and extrapolate trial results, and to further personalise treatment strategies. Currently, such methods are infrequently utilised in practice. New research is required to ensure that IMID trials can benefit from the most suitable methods.
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Affiliation(s)
- Michael J Grayling
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK
| | - Theophile Bigirumurame
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK
| | - Svetlana Cherlin
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK
| | - Luke Ouma
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK
| | - Haiyan Zheng
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK
| | - James M S Wason
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK.
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
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22
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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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Zheng H, Hampson LV, Jaki T. Bridging across patient subgroups in phase I oncology trials that incorporate animal data. Stat Methods Med Res 2021; 30:1057-1071. [PMID: 33501882 PMCID: PMC8129464 DOI: 10.1177/0962280220986580] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this paper, we develop a general Bayesian hierarchical model for bridging across patient subgroups in phase I oncology trials, for which preliminary information about the dose-toxicity relationship can be drawn from animal studies. Parameters that re-scale the doses to adjust for intrinsic differences in toxicity, either between animals and humans or between human subgroups, are introduced to each dose-toxicity model. Appropriate priors are specified for these scaling parameters, which capture the magnitude of uncertainty surrounding the animal-to-human translation and bridging assumption. After mapping data onto a common, 'average' human dosing scale, human dose-toxicity parameters are assumed to be exchangeable either with the standardised, animal study-specific parameters, or between themselves across human subgroups. Random-effects distributions are distinguished by different covariance matrices that reflect the between-study heterogeneity in animals and humans. Possibility of non-exchangeability is allowed to avoid inferences for extreme subgroups being overly influenced by their complementary data. We illustrate the proposed approach with hypothetical examples, and use simulation to compare the operating characteristics of trials analysed using our Bayesian model with several alternatives. Numerical results show that the proposed approach yields robust inferences, even when data from multiple sources are inconsistent and/or the bridging assumptions are incorrect.
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Affiliation(s)
- Haiyan Zheng
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.,Department of Mathematics and Statistics, Lancaster University, Lancashire, UK
| | - Lisa V Hampson
- Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancashire, UK.,MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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Psioda MA, Xia HA, Jiang X, Xu J, Ibrahim JG. Bayesian adaptive design for concurrent trials involving biologically related diseases. Biostatistics 2020; 23:kxab008. [PMID: 33982753 DOI: 10.1093/biostatistics/kxab008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 11/30/2020] [Accepted: 02/25/2021] [Indexed: 11/13/2022] Open
Abstract
We develop a Bayesian design method for a clinical program where an investigational product is to be studied concurrently in a set of clinical trials involving related diseases with the goal of demonstrating superiority to a control in each. The approach borrows information on treatment effectiveness using correlated mixture priors using an analysis procedure that is closely related Bayesian model averaging. Mixture priors are constructed by eliciting conjugate priors based on pessimistic and enthusiastic predictions for the data to be observed for each disease and then by eliciting mixture weights for all possible configurations of the pessimistic and enthusiastic priors across the diseases to be studied. The proposed approach provides a robust framework for information borrowing in settings where the diseases may have endpoints based on different data types. We show via simulation that operating characteristics based on the proposed design framework are favorable compared to those based on information borrowing designs using the Bayesian hierarchical model which is poorly suited for information borrowing when there are different data types underpinning the endpoints across which information is to be borrowed.
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Affiliation(s)
- Matthew A Psioda
- Department of Biostatistics, University of North Carolina, McGavran-Greenberg Hall, CB#7420, Chapel Hill, NC 27599, USA
| | | | - Xun Jiang
- Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Jiawei Xu
- Department of Biostatistics, University of North Carolina, McGavran-Greenberg Hall, CB#7420, Chapel Hill, NC 27599, USA
| | - Joseph G Ibrahim
- Department of Biostatistics, University of North Carolina, McGavran-Greenberg Hall, CB#7420, Chapel Hill, NC 27599, USA
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