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Sasaki M, Sato H, Uemura Y, Mikami A, Ichihara N, Fujitani S, Kondo M, Doi Y, Morino E, Tokita D, Ohmagari N, Sugiura W, Hirakawa A. How Much More Efficient Are Adaptive Platform Trials Than Multiple Stand-Alone Trials? A Comprehensive Simulation Study for Streamlining Drug Development During a Pandemic. Clin Pharmacol Ther 2024; 115:1372-1382. [PMID: 38441177 DOI: 10.1002/cpt.3224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/12/2024] [Indexed: 05/14/2024]
Abstract
With the coronavirus disease 2019 (COVID-19) pandemic, there is growing interest in utilizing adaptive platform clinical trials (APTs), in which multiple drugs are compared with a single common control group, such as a placebo or standard-of-care group. APTs evaluate several drugs for one disease and accept additions or exclusions of drugs as the trials progress; however, little is known about the efficiency of APTs over multiple stand-alone trials. In this study, we simulated the total development period, total sample size, and statistical operating characteristics of APTs and multiple stand-alone trials in drug development settings for hospitalized patients with COVID-19. Simulation studies using selected scenarios reconfirmed several findings regarding the efficiency of APTs. The APTs without staggered addition of drugs showed a shorter total development period than stand-alone trials, but the difference rapidly diminished if patient's enrollment was accelerated during the trials owing to the spread of infection. APTs with staggered addition of drugs still have the possibility of reducing the total development period compared with multiple stand-alone trials in some cases. Our study demonstrated that APTs could improve efficiency relative to multiple stand-alone trials regarding the total development period and total sample size without undermining statistical validity; however, this improvement varies depending on the speed of patient enrollment, sample size, presence/absence of family-wise error rate adjustment, allocation ratio between drug and placebo groups, and interval of staggered addition of drugs. Given the complexity of planning and implementing APT, the decision to implement APT during a pandemic must be made carefully.
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Affiliation(s)
- Masanao Sasaki
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Hiroyuki Sato
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Yukari Uemura
- Biostatistics Section, Department of Data Science, Center of Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Ayako Mikami
- Center for Clinical Research, National Center for Child Health and Development, Tokyo, Japan
| | - Nao Ichihara
- Department of Healthcare Quality Assessment, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shigeki Fujitani
- Department of Emergency and Critical Care Medicine, St. Marianna University School of Medicine, Kanagawa, Japan
| | - Masashi Kondo
- Center for Clinical Trial and Research Support, Fujita Health University School of Medicine, Aichi, Japan
- Department of Respiratory Medicine, Fujita Health University School of Medicine, Aichi, Japan
| | - Yohei Doi
- Departments of Microbiology and Infectious Diseases, Fujita Health University School of Medicine, Aichi, Japan
| | - Eriko Morino
- Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Daisuke Tokita
- Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Norio Ohmagari
- Disease Control and Prevention Center, National Center for Global Health and Medicine, Tokyo, Japan
| | - Wataru Sugiura
- Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Akihiro Hirakawa
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
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Pepić A, Stark M, Friede T, Kopp-Schneider A, Calderazzo S, Reichert M, Wolf M, Wirth U, Schopf S, Zapf A. A diagnostic phase III/IV seamless design to investigate the diagnostic accuracy and clinical effectiveness using the example of HEDOS and HEDOS II. Stat Methods Med Res 2024; 33:433-448. [PMID: 38327081 PMCID: PMC10981198 DOI: 10.1177/09622802241227951] [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: 02/09/2024]
Abstract
The development process of medical devices can be streamlined by combining different study phases. Here, for a diagnostic medical device, we present the combination of confirmation of diagnostic accuracy (phase III) and evaluation of clinical effectiveness regarding patient-relevant endpoints (phase IV) using a seamless design. This approach is used in the Thyroid HEmorrhage DetectOr Study (HEDOS & HEDOS II) investigating a post-operative hemorrhage detector named ISAR-M THYRO® in patients after thyroid surgery. Data from the phase III trial are reused as external controls in the control group of the phase IV trial. An unblinded interim analysis is planned between the two study stages which includes a recalculation of the sample size for the phase IV part after completion of the first stage of the seamless design. The study concept presented here is the first seamless design proposed in the field of diagnostic studies. Hence, the aim of this work is to emphasize the statistical methodology as well as feasibility of the proposed design in relation to the planning and implementation of the seamless design. Seamless designs can accelerate the overall trial duration and increase its efficiency in terms of sample size and recruitment. However, careful planning addressing numerous methodological and procedural challenges is necessary for successful implementation as well as agreement with regulatory bodies.
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Affiliation(s)
- Amra Pepić
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Maria Stark
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | | | - Silvia Calderazzo
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Michael Wolf
- CRI—The Clinical Research Institute, Munich, Germany
| | - Ulrich Wirth
- Clinic for General, Visceral and Transplant Surgery, Hospital of the Ludwig-Maximilians-University, Munich, Germany
| | - Stefan Schopf
- RoMed Klinik Bad Aibling, Academic University Hospital of the Technical University of Munich, Bad Aibling, Germany
| | - Antonia Zapf
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
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3
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Koenig F, Spiertz C, Millar D, Rodríguez-Navarro S, Machín N, Van Dessel A, Genescà J, Pericàs JM, Posch M. Current state-of-the-art and gaps in platform trials: 10 things you should know, insights from EU-PEARL. EClinicalMedicine 2024; 67:102384. [PMID: 38226342 PMCID: PMC10788209 DOI: 10.1016/j.eclinm.2023.102384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/22/2023] [Accepted: 12/04/2023] [Indexed: 01/17/2024] Open
Abstract
Platform trials bring the promise of making clinical research more efficient and more patient centric. While their use has become more widespread, including their prominent role during the COVID-19 pandemic response, broader adoption of platform trials has been limited by the lack of experience and tools to navigate the critical upfront planning required to launch such collaborative studies. The European Union-Patient-cEntric clinicAl tRial pLatform (EU-PEARL) initiative has produced new methodologies to expand the use of platform trials with an overarching infrastructure and services embedded into Integrated Research Platforms (IRPs), in collaboration with patient representatives and through consultation with U.S. Food and Drug Administration and European Medicines Agency stakeholders. In this narrative review, we discuss the outlook for platform trials in Europe, including challenges related to infrastructure, design, adaptations, data sharing and regulation. Documents derived from the EU-PEARL project, alongside a literature search including PubMed and relevant grey literature (e.g., guidance from regulatory agencies and health technology agencies) were used as sources for a multi-stage collaborative process through which the 10 more important points based on lessons drawn from the EU-PEARL project were developed and summarised as guidance for the setup of platform trials. We conclude that early involvement of critical stakeholder such as regulatory agencies or patients are critical steps in the implementation and later acceptance of platform trials. Addressing these gaps will be critical for attaining the full potential of platform trials for patients. Funding Innovative Medicines Initiative 2 Joint Undertaking with support from the European Union's Horizon 2020 research and innovation programme and EFPIA.
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Affiliation(s)
- Franz Koenig
- Medical University of Vienna, Center for Medical Data Science, Vienna, Austria
| | | | - Daniel Millar
- Former Employee, Janssen Research & Development, LLC, Raritan, NJ, USA
| | | | | | | | - Joan Genescà
- Vall d’Hebron Institute for Research, Barcelona, Spain
- Liver Unit, Vall d’Hebron University Hospital, Barcelona, Spain
- Spanish Network of Biomedical Research Centers, Digestive and Liver Diseases (CIBERehd), Madrid, Spain
- Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain
| | - Juan M. Pericàs
- Vall d’Hebron Institute for Research, Barcelona, Spain
- Liver Unit, Vall d’Hebron University Hospital, Barcelona, Spain
- Spanish Network of Biomedical Research Centers, Digestive and Liver Diseases (CIBERehd), Madrid, Spain
- Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain
| | - Martin Posch
- Medical University of Vienna, Center for Medical Data Science, Vienna, Austria
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Mahar RK, McGlothlin A, Dymock M, Lee TC, Lewis RJ, Lumley T, Mora J, Price DJ, Saville BR, Snelling T, Turner R, Webb SA, Davis JS, Tong SYC, Marsh JA. A blueprint for a multi-disease, multi-domain Bayesian adaptive platform trial incorporating adult and paediatric subgroups: the Staphylococcus aureus Network Adaptive Platform trial. Trials 2023; 24:795. [PMID: 38057927 PMCID: PMC10699085 DOI: 10.1186/s13063-023-07718-x] [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: 05/10/2023] [Accepted: 10/05/2023] [Indexed: 12/08/2023] Open
Abstract
The Staphylococcus aureus Network Adaptive Platform (SNAP) trial is a multifactorial Bayesian adaptive platform trial that aims to improve the way that S. aureus bloodstream infection, a globally common and severe infectious disease, is treated. In a world first, the SNAP trial will simultaneously investigate the effects of multiple intervention modalities within multiple groups of participants with different forms of S. aureus bloodstream infection. Here, we formalise the trial structure, modelling approach, and decision rules that will be used for the SNAP trial. By summarising the statistical principles governing the design, our hope is that the SNAP trial will serve as an adaptable template that can be used to improve comparative effectiveness research efficiency in other disease areas.Trial registration NCT05137119 . Registered on 30 November 2021.
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Affiliation(s)
- Robert K Mahar
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, Australia.
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.
| | | | - Michael Dymock
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Nedlands, Western Australia, Australia
| | - Todd C Lee
- Division of Infectious Diseases, Department of Medicine, McGill University, Montreal, Canada
| | - Roger J Lewis
- Berry Consultants LLC, Austin, Texas, USA
- Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, California, USA
| | - Thomas Lumley
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - Jocelyn Mora
- Department of Infectious Diseases, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - David J Price
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
- Department of Infectious Diseases, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - Benjamin R Saville
- Berry Consultants LLC, Austin, Texas, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Tom Snelling
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Nedlands, Western Australia, Australia
- Department of Infectious Diseases, Perth Children's Hospital, Perth, Western Australia, Australia
- Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Rebecca Turner
- Medical Research Council Clinical Trials Unit at University College London, London, United Kingdom
| | - Steven A Webb
- St John of God Healthcare, Perth, Western Australia, Australia
- Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Joshua S Davis
- Department of Infectious Diseases, John Hunter Hospital, Newcastle, New South Wales, Australia
- Menzies School of Health Research, Royal Darwin Hospital, Darwin, Northern Territory, Australia
| | - Steven Y C Tong
- Department of Infectious Diseases, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
- Victorian Infectious Diseases Service, Royal Melbourne Hospital at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Julie A Marsh
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Nedlands, Western Australia, Australia
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Marschner IC, Jones M, Totterdell JA, Mahar RK, Snelling TL, Tong SYC. Transparent reporting of adaptive clinical trials using concurrently randomised cohorts. BMJ MEDICINE 2023; 2:e000497. [PMID: 37736079 PMCID: PMC10510920 DOI: 10.1136/bmjmed-2023-000497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 09/01/2023] [Indexed: 09/23/2023]
Abstract
Adaptive clinical trials have designs that evolve over time because of changes to treatments or changes to the chance that participants will receive these treatments. These changes might introduce confounding that biases crude comparisons of the treatment arms and makes the results from standard reporting methods difficult to interpret for adaptive trials. To deal with this shortcoming, a reporting framework for adaptive trials was developed based on concurrently randomised cohort reporting. A concurrently randomised cohort is a subgroup of participants who all had the same treatments available and the same chance of receiving these treatments. The reporting of pre-randomisation characteristics and post-randomisation outcomes for each concurrently randomised cohort in the study is recommended. This approach provides a transparent and unbiased display of the degree of baseline balance and the randomised treatment comparisons for adaptive trials. The key concepts, terminology, and recommendations underlying concurrently randomised cohort reporting are presented, and its routine use in adaptive trial reporting is advocated.
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Affiliation(s)
- Ian C Marschner
- NHMRC Clinical Trials Centre, University of Sydney, Sydney, NSW, Australia
| | - Mark Jones
- School of Public Health, University of Sydney, Sydney, NSW, Australia
| | | | - Robert K Mahar
- Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Murdoch Children's Research Institute, Parkville, VIC, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
| | - Thomas L Snelling
- School of Public Health, University of Sydney, Sydney, NSW, Australia
- Telethon Kids Institute, University of Western Australia, Perth, WA, Australia
| | - Steven Y C Tong
- Department of Infectious Diseases, University of Melbourne, Melbourne, VIC, Australia
- Victorian Infectious Diseases Service, University of Melbourne, Melbourne, VIC, Australia
- Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
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Bofill Roig M, Burgwinkel C, Garczarek U, Koenig F, Posch M, Nguyen Q, Hees K. On the use of non-concurrent controls in platform trials: a scoping review. Trials 2023; 24:408. [PMID: 37322532 DOI: 10.1186/s13063-023-07398-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 05/19/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND Platform trials gained popularity during the last few years as they increase flexibility compared to multi-arm trials by allowing new experimental arms entering when the trial already started. Using a shared control group in platform trials increases the trial efficiency compared to separate trials. Because of the later entry of some of the experimental treatment arms, the shared control group includes concurrent and non-concurrent control data. For a given experimental arm, non-concurrent controls refer to patients allocated to the control arm before the arm enters the trial, while concurrent controls refer to control patients that are randomised concurrently to the experimental arm. Using non-concurrent controls can result in bias in the estimate in case of time trends if the appropriate methodology is not used and the assumptions are not met. METHODS We conducted two reviews on the use of non-concurrent controls in platform trials: one on statistical methodology and one on regulatory guidance. We broadened our searches to the use of external and historical control data. We conducted our review on the statistical methodology in 43 articles identified through a systematic search in PubMed and performed a review on regulatory guidance on the use of non-concurrent controls in 37 guidelines published on the EMA and FDA websites. RESULTS Only 7/43 of the methodological articles and 4/37 guidelines focused on platform trials. With respect to the statistical methodology, in 28/43 articles, a Bayesian approach was used to incorporate external/non-concurrent controls while 7/43 used a frequentist approach and 8/43 considered both. The majority of the articles considered a method that downweights the non-concurrent control in favour of concurrent control data (34/43), using for instance meta-analytic or propensity score approaches, and 11/43 considered a modelling-based approach, using regression models to incorporate non-concurrent control data. In regulatory guidelines, the use of non-concurrent control data was considered critical but was deemed acceptable for rare diseases in 12/37 guidelines or was accepted in specific indications (12/37). Non-comparability (30/37) and bias (16/37) were raised most often as the general concerns with non-concurrent controls. Indication specific guidelines were found to be most instructive. CONCLUSIONS Statistical methods for incorporating non-concurrent controls are available in the literature, either by means of methods originally proposed for the incorporation of external controls or non-concurrent controls in platform trials. Methods mainly differ with respect to how the concurrent and non-concurrent data are combined and temporary changes handled. Regulatory guidance for non-concurrent controls in platform trials are currently still limited.
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Affiliation(s)
- Marta Bofill Roig
- Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.
| | - Cora Burgwinkel
- Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
- Department of Biostatistics, Paul-Ehrlich Institut, Langen, Germany
| | | | - Franz Koenig
- Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Quynh Nguyen
- Department of Biostatistics, Paul-Ehrlich Institut, Langen, Germany
| | - Katharina Hees
- Department of Biostatistics, Paul-Ehrlich Institut, Langen, Germany.
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Liu J, Lu C, Jiang Z, Alemayehu D, Nie L, Chu H. Borrowing Concurrent Information from Non-Concurrent Control to Enhance Statistical Efficiency in Platform Trials. Curr Oncol 2023; 30:3964-3973. [PMID: 37185413 PMCID: PMC10137133 DOI: 10.3390/curroncol30040300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/29/2023] [Indexed: 04/03/2023] Open
Abstract
A platform trial is a trial involving an innovative adaptive design with a single master protocol to efficiently evaluate multiple interventions. It offers flexible features such as dropping interventions for futility and adding new interventions to be evaluated during the course of a trial. Although there is a consensus that platform trials can identify beneficial interventions with fewer patients, less time, and a higher probability of success than traditional trials, there remains debate on certain issues, one of which is whether (and how) the non-concurrent control (NCC) (i.e., patients in the control group recruited prior to the new interventions) can be combined with the current control (CC) in the analysis, especially if there is a change of standard of care during the trial. Methods: In this paper, considering time-to-event endpoints under the proportional hazard model assumption, we introduce a new concept of NCC concurrent observation time (NCC COT), and propose to borrow NCC COT through left truncation. This assumes that the NCC COT and CC are comparable. If the protocol does not prohibit NCC patients to change the standard of care while on study, NCC COT and CC likely will share the same standard of care. A simulated example is provided to demonstrate the approach. Results: Using exponential distributions, the simulated example assumes that NCC COT and CC have the same hazard, and the treatment group has a lower hazard. The estimated HR comparing treatment to the pooled control group is 0.744 (95% CI 0.575, 0.962), whereas the comparison to the CC group alone is 0.755 (95% CI 0.566, 1.008), with corresponding p-values of 0.024 versus 0.057, respectively. This suggests that borrowing NCC COT can improve statistical efficiency when the exchangeability assumption holds. Conclusion: This article proposes an innovative approach of borrowing NCC COT to enhance statistical inference in platform trials under appropriate scenarios.
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Roustit M, Demarcq O, Laporte S, Barthélémy P, Chassany O, Cucherat M, Demotes J, Diebolt V, Espérou H, Fouret C, Galaup A, Gambotti L, Gourio C, Guérin A, Labruyère C, Paoletti X, Porcher R, Simon T, Varoqueaux N. Les essais plateformes ☆. Therapie 2023; 78:19-28. [PMID: 36581520 PMCID: PMC9721267 DOI: 10.1016/j.therap.2022.11.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022]
Abstract
Les essais plateformes connaissent depuis quelques années un essor important, amplifié récemment par la pandémie de coronavirus disease 2019 (COVID-19). La mise en œuvre d’un essai plateforme s’avère particulièrement utile dans certaines pathologies, notamment lorsqu’il y a un nombre important de candidats médicaments à évaluer, une évolution rapide du traitement de référence ou dans les situations de besoin urgent d’évaluation, au cours desquelles la mutualisation des protocoles et des infrastructures permet d’optimiser le nombre de patients à inclure, les coûts et les délais de réalisation de l’investigation. Toutefois, la spécificité des essais plateformes soulève des problématiques méthodologiques, éthiques et règlementaires, qui ont fait l’objet de la table ronde et qui sont exposées dans cet article. La table ronde a également été l’occasion d’aborder la complexité de la promotion et de la gestion des données liée à la multiplicité des partenaires, le financement et la gouvernance de ces essais, et le niveau d’acceptabilité de leurs résultats par les autorités compétentes.
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Affiliation(s)
- Matthieu Roustit
- Inserm CIC1406, university Grenoble Alpes, CHU de Grenoble, 38000 Grenoble, France.
| | - Olivier Demarcq
- Pfizer, direction des affaires médicales, 75668 Paris, France
| | - Silvy Laporte
- Inserm, U 1059 Sainbiose, Mines Saint-Étienne, unité de recherche clinique, innovation, pharmacologie, université Jean Monnet, CHU de Saint-Étienne, 42023 Saint-Étienne, France
| | | | - Olivier Chassany
- Unité de recherche clinique en économie de la santé (URC-ECO), hôpital Hôtel-Dieu, AP-HP, 75004 Paris, France
| | - Michel Cucherat
- metaEvidence.org, service hospitalo-universitaire de pharmacologie et toxicologie, hospices civils de Lyon, 69000 Lyon, France
| | | | - Vincent Diebolt
- F-CRIN, UMS 015, Pavillon Leriche, hôpital Purpan/CHU de Toulouse, 31059 Toulouse, France
| | - Hélène Espérou
- Inserm, pôle de recherche clinique, Institut de santé publique, 75013 Paris, France
| | - Cécile Fouret
- Medtronic, direction des affaires scientifiques, 75014 Paris, France
| | | | - Laetitia Gambotti
- Département recherche clinique, Institut national du cancer, 92100 Boulogne-Billancourt, France
| | | | | | - Carine Labruyère
- Inserm, U 1059 Sainbiose, Mines Saint-Étienne, unité de recherche clinique, innovation, pharmacologie, université Jean Monnet, CHU de Saint-Étienne, 42023 Saint-Étienne, France
| | - Xavier Paoletti
- Inserm U900, équipe de statistique pour la médecine de précision (STAMPM), Institut Curie, université de Versailles St Quentin/Paris-Saclay, 92210 St-Cloud, France
| | - Raphael Porcher
- Inserm, Inra, centre d'épidémiologie clinique, université Paris Cité, METHODS Team, CRESS, Hôtel-Dieu, Assistance publique-Hôpitaux de Paris, 75004 Paris, France
| | - Tabassome Simon
- Service de pharmacologie, plateforme de recherche clinique de l'Est parisien, Sorbonne université, Assistance publique-Hôpitaux de Paris, 75012 Paris, France
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Roustit M, Demarcq O, Laporte S, Barthélémy P, Chassany O, Cucherat M, Demotes J, Diebolt V, Espérou H, Fouret C, Galaup A, Gambotti L, Gourio C, Guérin A, Labruyère C, Paoletti X, Porcher R, Simon T, Varoqueaux N. Platform trials. Therapie 2023; 78:29-38. [PMID: 36529559 PMCID: PMC9756081 DOI: 10.1016/j.therap.2022.12.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022]
Abstract
For the past few years, platform trials have experienced a significant increase, recently amplified by the COVID-19 pandemic. The implementation of a platform trial is particularly useful in certain pathologies, particularly when there is a significant number of drug candidates to be assessed, a rapid evolution of the standard of care or in situations of urgent need for evaluation, during which the pooling of protocols and infrastructure optimizes the number of patients to be enrolled, the costs, and the deadlines for carrying out the investigation. However, the specificity of platform trials raises methodological, ethical, and regulatory issues, which have been the subject of the round table and which are presented in this article. The round table was also an opportunity to discuss the complexity of sponsorship and data management related to the multiplicity of partners, funding, and governance of these trials, and the level of acceptability of their findings by the competent authorities.
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Affiliation(s)
- Matthieu Roustit
- Inserm CIC1406, university Grenoble Alpes, CHU de Grenoble, 38000 Grenoble, France,Corresponding author. Centre d’investigation clinique – Inserm CIC1406, CHU Grenoble Alpes, 38043 Grenoble cedex 09, France
| | - Olivier Demarcq
- Pfizer, direction des affaires médicales, 75668 Paris, France
| | - Silvy Laporte
- Inserm, U 1059 Sainbiose, Mines Saint-Étienne, unité de recherche clinique, innovation, pharmacologie, université Jean Monnet, CHU de Saint-Étienne, 42023 Saint-Étienne, France
| | | | - Olivier Chassany
- Unité de recherche clinique en économie de la santé (URC-ECO), hôpital Hôtel-Dieu, AP–HP, 75004 Paris, France
| | - Michel Cucherat
- metaEvidence.org, service hospitalo-universitaire de pharmacologie et toxicologie, hospices civils de Lyon, 69000 Lyon, France
| | | | - Vincent Diebolt
- F-CRIN, UMS 015, Pavillon Leriche, hôpital Purpan/CHU de Toulouse, 31059 Toulouse, France
| | - Hélène Espérou
- Inserm, pôle de recherche clinique, Institut de santé publique, 75013 Paris, France
| | - Cécile Fouret
- Medtronic, direction des affaires scientifiques, 75014 Paris, France
| | | | - Laetitia Gambotti
- Département recherche clinique, Institut national du cancer, 92100 Boulogne-Billancourt, France
| | | | | | - Carine Labruyère
- Inserm, U 1059 Sainbiose, Mines Saint-Étienne, unité de recherche clinique, innovation, pharmacologie, université Jean Monnet, CHU de Saint-Étienne, 42023 Saint-Étienne, France
| | - Xavier Paoletti
- Inserm U900, équipe de statistique pour la médecine de précision (STAMPM), Institut Curie, université de Versailles St Quentin/Paris-Saclay, 92210 St-Cloud, France
| | - Raphael Porcher
- Inserm, Inra, centre d’épidémiologie clinique, université Paris Cité, METHODS Team, CRESS, Hôtel-Dieu, Assistance publique–Hôpitaux de Paris, 75004 Paris, France
| | - Tabassome Simon
- Service de pharmacologie, plateforme de recherche clinique de l’Est parisien, Sorbonne université, Assistance publique–Hôpitaux de Paris, 75012 Paris, France
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Zehetmayer S, Posch M, Koenig F. Online control of the False Discovery Rate in group-sequential platform trials. Stat Methods Med Res 2022; 31:2470-2485. [PMID: 36189481 PMCID: PMC10130539 DOI: 10.1177/09622802221129051] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
When testing multiple hypotheses, a suitable error rate should be controlled even in exploratory trials. Conventional methods to control the False Discovery Rate assume that all p-values are available at the time point of test decision. In platform trials, however, treatment arms enter and leave the trial at different times during its conduct. Therefore, the actual number of treatments and hypothesis tests is not fixed in advance and hypotheses are not tested at once, but sequentially. Recently, for such a setting the concept of online control of the False Discovery Rate was introduced. We propose several heuristic variations of the LOND procedure (significance Levels based On Number of Discoveries) that incorporate interim analyses for platform trials, and study their online False Discovery Rate via simulations. To adjust for the interim looks spending functions are applied with O'Brien-Fleming or Pocock type group-sequential boundaries. The power depends on the prior distribution of effect sizes, for example, whether true alternatives are uniformly distributed over time or not. We consider the choice of design parameters for the LOND procedure to maximize the overall power and investigate the impact on the False Discovery Rate by including both concurrent and non-concurrent control data.
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Affiliation(s)
- Sonja Zehetmayer
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Franz Koenig
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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