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Tran NA, McGrory A, Poonai N, Heath A. A comparison of alternative ranking methods in two-stage clinical trials with multiple interventions: An application to the anxiolysis for laceration repair in children trial. Clin Trials 2024:17407745241251812. [PMID: 38771021 DOI: 10.1177/17407745241251812] [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: 05/22/2024]
Abstract
BACKGROUND/AIMS Multi-arm, multi-stage trials frequently include a standard care to which all interventions are compared. This may increase costs and hinders comparisons among the experimental arms. Furthermore, the standard care may not be evident, particularly when there is a large variation in standard practice. Thus, we aimed to develop an adaptive clinical trial that drops ineffective interventions following an interim analysis before selecting the best intervention at the final stage without requiring a standard care. METHODS We used Bayesian methods to develop a multi-arm, two-stage adaptive trial and evaluated two different methods for ranking interventions, the probability that each intervention was optimal (Pbest) and using the surface under the cumulative ranking curve (SUCRA), at both the interim and final analysis. The proposed trial design determines the maximum sample size for each intervention using the Average Length Criteria. The interim analysis takes place at approximately half the pre-specified maximum sample size and aims to drop interventions for futility if either Pbest or the SUCRA is below a pre-specified threshold. The final analysis compares all remaining interventions at the maximum sample size to conclude superiority based on either Pbest or the SUCRA. The two ranking methods were compared across 12 scenarios that vary the number of interventions and the assumed differences between the interventions. The thresholds for futility and superiority were chosen to control type 1 error, and then the predictive power and expected sample size were evaluated across scenarios. A trial comparing three interventions that aim to reduce anxiety for children undergoing a laceration repair in the emergency department was then designed, known as the Anxiolysis for Laceration Repair in Children Trial (ALICE) trial. RESULTS As the number of interventions increases, the SUCRA results in a higher predictive power compared with Pbest. Using Pbest results in a lower expected sample size when there is an effective intervention. Using the Average Length Criterion, the ALICE trial has a maximum sample size for each arm of 100 patients. This sample size results in a 86% and 85% predictive power using Pbest and the SUCRA, respectively. Thus, we chose Pbest as the ranking method for the ALICE trial. CONCLUSION Bayesian ranking methods can be used in multi-arm, multi-stage trials with no clear control intervention. When more interventions are included, the SUCRA results in a higher power than Pbest. Future work should consider whether other ranking methods may also be relevant for clinical trial design.
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Affiliation(s)
- Nam-Anh Tran
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
| | - Abigail McGrory
- Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Naveen Poonai
- Departments of Paediatrics, Internal Medicine, Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
| | - Anna Heath
- Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Statistical Science, University College London, London, UK
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Buffart LM, Bassi A, Stuiver MM, Aaronson NK, Sonke GS, Berkhof J, van de Ven PM. A Bayesian-adaptive decision-theoretic approach can reduce the sample sizes for multiarm exercise oncology trials. J Clin Epidemiol 2023; 159:190-198. [PMID: 37245703 DOI: 10.1016/j.jclinepi.2023.05.019] [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: 11/16/2022] [Revised: 04/25/2023] [Accepted: 05/22/2023] [Indexed: 05/30/2023]
Abstract
OBJECTIVES Adaptive designs may reduce trial sample sizes and costs. This study illustrates a Bayesian-adaptive decision-theoretic design applied to a multiarm exercise oncology trial. STUDY DESIGN AND SETTING In the Physical exercise during Adjuvant Chemotherapy Effectiveness Study (PACES) trial, 230 breast cancer patients receiving chemotherapy were randomized to supervised resistance and aerobic exercise (OnTrack), home-based physical activity (OncoMove) or usual care (UC). Data were reanalyzed as an adaptive trial using both Bayesian decision-theoretic and a frequentist group-sequential approach incorporating interim analyses after every 36 patients. Endpoint was chemotherapy treatment modifications (any vs. none). Bayesian analyses were performed for different continuation thresholds and settings with and without arm dropping and both in a 'pick-the-winner' and a 'pick-all-treatments-superior-to-control' setting. RESULTS Treatment modifications occurred in 34% of patients in UC and OncoMove vs. 12% in OnTrack (P = 0.002). Using a Bayesian-adaptive decision-theoretic design, OnTrack was identified as most effective after 72 patients in the 'pick-the-winner' setting and after 72-180 patients in the 'pick-all-treatments-superior-to-control' setting. In a frequentist setting, the trial would have been stopped after 180 patients, and the proportion of patients with treatment modifications was significantly lower for OnTrack than UC. CONCLUSION A Bayesian-adaptive decision-theoretic approach substantially reduced the sample size required for this three-arm exercise trial, especially in the 'pick-the-winner' setting.
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Affiliation(s)
- Laurien M Buffart
- Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Andrea Bassi
- Department of Epidemiology and Data Science, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Martijn M Stuiver
- Department of Epidemiology and Data Science, Amsterdam University Medical Center, Amsterdam, The Netherlands; Center for Quality of Life, The Netherlands Cancer Institute, Amsterdam, The Netherlands; Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Neil K Aaronson
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Gabe S Sonke
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Johannes Berkhof
- Department of Epidemiology and Data Science, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Peter M van de Ven
- Department of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
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Bon JJ, Bretherton A, Buchhorn K, Cramb S, Drovandi C, Hassan C, Jenner AL, Mayfield HJ, McGree JM, Mengersen K, Price A, Salomone R, Santos-Fernandez E, Vercelloni J, Wang X. Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20220156. [PMID: 36970822 PMCID: PMC10041356 DOI: 10.1098/rsta.2022.0156] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/06/2023] [Indexed: 06/18/2023]
Abstract
Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
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Affiliation(s)
- Joshua J. Bon
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Adam Bretherton
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Katie Buchhorn
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Susanna Cramb
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Christopher Drovandi
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Conor Hassan
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Adrianne L. Jenner
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Helen J. Mayfield
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Public Health, The University of Queensland, Saint Lucia, Queensland, Australia
| | - James M. McGree
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kerrie Mengersen
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Aiden Price
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Robert Salomone
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Computer Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Edgar Santos-Fernandez
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Julie Vercelloni
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Xiaoyu Wang
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
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Arjas E, Gasbarra D. Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective. BMC Med Res Methodol 2022; 22:50. [PMID: 35184731 PMCID: PMC8858379 DOI: 10.1186/s12874-022-01526-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/19/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Adaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power.
Results
The purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during phase II and III. This approach is based on comparing the performance of the different treatment arms in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm, and treatment selection, removing an arm from the trial permanently. The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package ’barts’.
Conclusion
The proposed methods for trial design provide an attractive alternative to their frequentist counterparts.
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