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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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White IR, Szubert AJ, Choodari-Oskooei B, Walker AS, Parmar MKB. When should factorial designs be used for late-phase randomised controlled trials? Clin Trials 2024; 21:162-170. [PMID: 37904490 PMCID: PMC7615816 DOI: 10.1177/17407745231206261] [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: 11/01/2023]
Abstract
BACKGROUND A 2×2 factorial design evaluates two interventions (A versus control and B versus control) by randomising to control, A-only, B-only or both A and B together. Extended factorial designs are also possible (e.g. 3×3 or 2×2×2). Factorial designs often require fewer resources and participants than alternative randomised controlled trials, but they are not widely used. We identified several issues that investigators considering this design need to address, before they use it in a late-phase setting. METHODS We surveyed journal articles published in 2000-2022 relating to designing factorial randomised controlled trials. We identified issues to consider based on these and our personal experiences. RESULTS We identified clinical, practical, statistical and external issues that make factorial randomised controlled trials more desirable. Clinical issues are (1) interventions can be easily co-administered; (2) risk of safety issues from co-administration above individual risks of the separate interventions is low; (3) safety or efficacy data are wanted on the combination intervention; (4) potential for interaction (e.g. effect of A differing when B administered) is low; (5) it is important to compare interventions with other interventions balanced, rather than allowing randomised interventions to affect the choice of other interventions; (6) eligibility criteria for different interventions are similar. Practical issues are (7) recruitment is not harmed by testing many interventions; (8) each intervention and associated toxicities is unlikely to reduce either adherence to the other intervention or overall follow-up; (9) blinding is easy to implement or not required. Statistical issues are (10) a suitable scale of analysis can be identified; (11) adjustment for multiplicity is not required; (12) early stopping for efficacy or lack of benefit can be done effectively. External issues are (13) adequate funding is available and (14) the trial is not intended for licensing purposes. An overarching issue (15) is that factorial design should give a lower sample size requirement than alternative designs. Across designs with varying non-adherence, retention, intervention effects and interaction effects, 2×2 factorial designs require lower sample size than a three-arm alternative when one intervention effect is reduced by no more than 24%-48% in the presence of the other intervention compared with in the absence of the other intervention. CONCLUSIONS Factorial designs are not widely used and should be considered more often using our issues to consider. Low potential for at most small to modest interaction is key, for example, where the interventions have different mechanisms of action or target different aspects of the disease being studied.
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Affiliation(s)
- Ian R White
- Ian R White, MRC Clinical Trials Unit at UCL, 2nd Floor, 90 High Holborn, London WC1V 6LJ, UK.
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Kahan BC, Hall SS, Beller EM, Birchenall M, Chan AW, Elbourne D, Little P, Fletcher J, Golub RM, Goulao B, Hopewell S, Islam N, Zwarenstein M, Juszczak E, Montgomery AA. Reporting of Factorial Randomized Trials: Extension of the CONSORT 2010 Statement. JAMA 2023; 330:2106-2114. [PMID: 38051324 DOI: 10.1001/jama.2023.19793] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Importance Transparent reporting of randomized trials is essential to facilitate critical appraisal and interpretation of results. Factorial trials, in which 2 or more interventions are assessed in the same set of participants, have unique methodological considerations. However, reporting of factorial trials is suboptimal. Objective To develop a consensus-based extension to the Consolidated Standards of Reporting Trials (CONSORT) 2010 Statement for factorial trials. Design Using the Enhancing the Quality and Transparency of Health Research (EQUATOR) methodological framework, the CONSORT extension for factorial trials was developed by (1) generating a list of reporting recommendations for factorial trials using a scoping review of methodological articles identified using a MEDLINE search (from inception to May 2019) and supplemented with relevant articles from the personal collections of the authors; (2) a 3-round Delphi survey between January and June 2022 to identify additional items and assess the importance of each item, completed by 104 panelists from 14 countries; and (3) a hybrid consensus meeting attended by 15 panelists to finalize the selection and wording of items for the checklist. Findings This CONSORT extension for factorial trials modifies 16 of the 37 items in the CONSORT 2010 checklist and adds 1 new item. The rationale for the importance of each item is provided. Key recommendations are (1) the reason for using a factorial design should be reported, including whether an interaction is hypothesized, (2) the treatment groups that form the main comparisons should be clearly identified, and (3) for each main comparison, the estimated interaction effect and its precision should be reported. Conclusions and Relevance This extension of the CONSORT 2010 Statement provides guidance on the reporting of factorial randomized trials and should facilitate greater understanding of and transparency in their reporting.
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Affiliation(s)
| | - Sophie S Hall
- Nottingham Clinical Trials Unit, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Elaine M Beller
- Institute for Evidence-Based Healthcare, Bond University, Queensland, Australia
| | - Megan Birchenall
- Nottingham Clinical Trials Unit, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - An-Wen Chan
- Women's College Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Diana Elbourne
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Paul Little
- Primary Care Research Centre, School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - John Fletcher
- The BMJ, BMA House, Tavistock Square, London, United Kingdom
| | - Robert M Golub
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Beatriz Goulao
- Health Services Research Unit, University of Aberdeen, Aberdeen, Scotland
| | - Sally Hopewell
- Oxford Clinical Trials Research Unit, University of Oxford, Oxford, United Kingdom
| | - Nazrul Islam
- Primary Care Research Centre, School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- The BMJ, BMA House, Tavistock Square, London, United Kingdom
| | - Merrick Zwarenstein
- Centre For Studies in Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Edmund Juszczak
- Nottingham Clinical Trials Unit, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Alan A Montgomery
- Nottingham Clinical Trials Unit, School of Medicine, University of Nottingham, Nottingham, United Kingdom
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Kahan BC, Hall SS, Beller EM, Birchenall M, Elbourne D, Juszczak E, Little P, Fletcher J, Golub RM, Goulao B, Hopewell S, Islam N, Zwarenstein M, Chan AW, Montgomery AA. Consensus Statement for Protocols of Factorial Randomized Trials: Extension of the SPIRIT 2013 Statement. JAMA Netw Open 2023; 6:e2346121. [PMID: 38051535 DOI: 10.1001/jamanetworkopen.2023.46121] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/07/2023] Open
Abstract
Importance Trial protocols outline a trial's objectives as well as the methods (design, conduct, and analysis) that will be used to meet those objectives, and transparent reporting of trial protocols ensures objectives are clear and facilitates appraisal regarding the suitability of study methods. Factorial trials, in which 2 or more interventions are assessed in the same set of participants, have unique methodological considerations. However, no extension of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 Statement, which provides guidance on reporting of trial protocols, for factorial trials is available. Objective To develop a consensus-based extension to the SPIRIT 2013 Statement for factorial trials. Evidence Review The SPIRIT extension for factorial trials was developed using the Enhancing the Quality and Transparency of Health Research (EQUATOR) methodological framework. First, a list of reporting recommendations was generated using a scoping review of methodological articles identified using a MEDLINE search (inception to May 2019), which was supplemented with relevant articles from the personal collections of the authors. Second, a 3-round Delphi survey (January to June 2022, completed by 104 panelists from 14 countries) was conducted to assess the importance of items and identify additional recommendations. Third, a hybrid consensus meeting was held, attended by 15 panelists to finalize selection and wording of the checklist. Findings This SPIRIT extension for factorial trials modified 9 of the 33 items in the SPIRIT 2013 checklist. Key reporting recommendations were that the rationale for using a factorial design should be provided, including whether an interaction is hypothesized; the treatment groups that will form the main comparisons should be identified; and statistical methods for each main comparison should be provided, including how interactions will be assessed. Conclusions and Relevance In this consensus statement, 9 factorial-specific items were provided that should be addressed in all protocols of factorial trials to increase the trial's utility and transparency.
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Affiliation(s)
| | - Sophie S Hall
- Nottingham Clinical Trials Unit, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Elaine M Beller
- Institute for Evidence-Based Healthcare, Bond University, Robina, Australia
| | - Megan Birchenall
- Nottingham Clinical Trials Unit, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Diana Elbourne
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Edmund Juszczak
- Nottingham Clinical Trials Unit, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Paul Little
- Primary Care Research Centre, School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | | | - Robert M Golub
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Beatriz Goulao
- Health Services Research Unit, University of Aberdeen, Aberdeen, Scotland
| | - Sally Hopewell
- Oxford Clinical Trials Research Unit, University of Oxford, Oxford, United Kingdom
| | - Nazrul Islam
- Primary Care Research Centre, School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- The BMJ, London, United Kingdom
| | - Merrick Zwarenstein
- Centre For Studies in Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - An-Wen Chan
- Women's College Research Institute, University of Toronto, Toronto, Canada
| | - Alan A Montgomery
- Nottingham Clinical Trials Unit, School of Medicine, University of Nottingham, Nottingham, United Kingdom
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