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Bofill Roig M, Glimm E, Mielke T, Posch M. Optimal allocation strategies in platform trials with continuous endpoints. Stat Methods Med Res 2024; 33:858-874. [PMID: 38505941 DOI: 10.1177/09622802241239008] [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] [Indexed: 03/21/2024]
Abstract
Platform trials are randomized clinical trials that allow simultaneous comparison of multiple interventions, usually against a common control. Arms to test experimental interventions may enter and leave the platform over time. This implies that the number of experimental intervention arms in the trial may change as the trial progresses. Determining optimal allocation rates to allocate patients to the treatment and control arms in platform trials is challenging because the optimal allocation depends on the number of arms in the platform and the latter typically varies over time. In addition, the optimal allocation depends on the analysis strategy used and the optimality criteria considered. In this article, we derive optimal treatment allocation rates for platform trials with shared controls, assuming that a stratified estimation and a testing procedure based on a regression model are used to adjust for time trends. We consider both, analysis using concurrent controls only as well as analysis methods using concurrent and non-concurrent controls and assume that the total sample size is fixed. The objective function to be minimized is the maximum of the variances of the effect estimators. We show that the optimal solution depends on the entry time of the arms in the trial and, in general, does not correspond to the square root of k allocation rule used in classical multi-arm trials. We illustrate the optimal allocation and evaluate the power and type 1 error rate compared to trials using one-to-one and square root of k allocations by means of a case study.
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Affiliation(s)
- Marta Bofill Roig
- Section for Medical Statistics, Center for Medical Data Science, Medical University of Vienna, Wien, Austria
| | - Ekkehard Glimm
- Advanced Methodology and Data Science, Novartis Campus, Novartis Pharma AG, Basel, Switzerland
| | - Tobias Mielke
- Statistics and Decision Sciences, Janssen-Cilag GmbH, Neuss, Nordrhein-Westfalen, Germany
| | - Martin Posch
- Section for Medical Statistics, Center for Medical Data Science, Medical University of Vienna, Wien, Austria
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Huang TJ, Luedtke A. Improved efficiency for cross-arm comparisons via platform designs. Biostatistics 2023; 24:1106-1124. [PMID: 35939566 PMCID: PMC10583724 DOI: 10.1093/biostatistics/kxac030] [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: 12/18/2021] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 11/12/2022] Open
Abstract
Though platform trials have been touted for their flexibility and streamlined use of trial resources, their statistical efficiency is not well understood. We fill this gap by establishing their greater efficiency for comparing the relative efficacy of multiple interventions over using several separate, 2-arm trials, where the relative efficacy of an arbitrary pair of interventions is evaluated by contrasting their relative risks as compared to control. In theoretical and numerical studies, we demonstrate that the inference of such a contrast using data from a platform trial enjoys identical or better precision than using data from separate trials, even when the former enrolls substantially fewer participants. This benefit is attributed to the sharing of controls among interventions under contemporaneous randomization. We further provide a novel procedure for establishing the noninferiority of a given intervention relative to the most efficacious of the other interventions under evaluation, where this procedure is adaptive in the sense that it need not be a priori known which of these other interventions is most efficacious. Our numerical studies show that this testing procedure can attain substantially better power when the data arise from a platform trial rather than multiple separate trials. Our results are illustrated using data from two monoclonal antibody trials for the prevention of HIV.
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Affiliation(s)
- Tzu-Jung Huang
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Alex Luedtke
- Department of Statistics, University of Washington, Seattle, WA 98195, USA and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
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Collignon O, Schiel A, Burman C, Rufibach K, Posch M, Bretz F. Estimands and Complex Innovative Designs. Clin Pharmacol Ther 2022; 112:1183-1190. [PMID: 35253205 PMCID: PMC9790227 DOI: 10.1002/cpt.2575] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/01/2022] [Indexed: 01/31/2023]
Abstract
Since the release of the ICH E9(R1) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials) document in 2019, the estimand framework has become a fundamental part of clinical trial protocols. In parallel, complex innovative designs have gained increased popularity in drug development, in particular in early development phases or in difficult experimental situations. While the estimand framework is relevant to any study in which a treatment effect is estimated, experience is lacking as regards its application to these designs. In a basket trial for example, should a different estimand be specified for each subpopulation of interest, defined, for example, by cancer site? Or can a single estimand focusing on the general population (defined, for example, by the positivity to a certain biomarker) be used? In the case of platform trials, should a different estimand be proposed for each drug investigated? In this work we discuss possible ways of implementing the estimand framework for different types of complex innovative designs. We consider trials that allow adding or selecting experimental treatment arms, modifying the control arm or the standard of care, and selecting or pooling populations. We also address the potentially data-driven, adaptive selection of estimands in an ongoing trial and disentangle certain statistical issues that pertain to estimation rather than to estimands, such as the borrowing of nonconcurrent information. We hope this discussion will facilitate the implementation of the estimand framework and its description in the study protocol when the objectives of the trial require complex innovative designs.
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Affiliation(s)
| | | | - Carl‐Fredrik Burman
- Statistical Innovation, Data Science & Artificial IntelligenceAstraZeneca Research & DevelopmentGothenburgSweden
| | - Kaspar Rufibach
- Methods, Collaboration, and Outreach Group, Product Development Data SciencesF.Hoffmann‐La RocheBaselSwitzerland
| | - Martin Posch
- Section for Medical StatisticsCenter for Medical Statistics Informatics, and Intelligent SystemsMedical University of ViennaViennaAustria
| | - Frank Bretz
- Section for Medical StatisticsCenter for Medical Statistics Informatics, and Intelligent SystemsMedical University of ViennaViennaAustria,NovartisBaselSwitzerland
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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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Bofill Roig M, Krotka P, Burman CF, Glimm E, Gold SM, Hees K, Jacko P, Koenig F, Magirr D, Mesenbrink P, Viele K, Posch M. On model-based time trend adjustments in platform trials with non-concurrent controls. BMC Med Res Methodol 2022; 22:228. [PMID: 35971069 PMCID: PMC9380382 DOI: 10.1186/s12874-022-01683-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 07/12/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Platform trials can evaluate the efficacy of several experimental treatments compared to a control. The number of experimental treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel group trials because of using shared control groups. However, for a treatment entering the trial at a later time point, the control group is divided into concurrent controls, consisting of patients randomised to control when that treatment arm is in the platform, and non-concurrent controls, patients randomised before. Using non-concurrent controls in addition to concurrent controls can improve the trial's efficiency by increasing power and reducing the required sample size, but can introduce bias due to time trends. METHODS We focus on a platform trial with two treatment arms and a common control arm. Assuming that the second treatment arm is added at a later time, we assess the robustness of recently proposed model-based approaches to adjust for time trends when utilizing non-concurrent controls. In particular, we consider approaches where time trends are modeled either as linear in time or as a step function, with steps at time points where treatments enter or leave the platform trial. For trials with continuous or binary outcomes, we investigate the type 1 error rate and power of testing the efficacy of the newly added arm, as well as the bias and root mean squared error of treatment effect estimates under a range of scenarios. In addition to scenarios where time trends are equal across arms, we investigate settings with different time trends or time trends that are not additive in the scale of the model. RESULTS A step function model, fitted on data from all treatment arms, gives increased power while controlling the type 1 error, as long as the time trends are equal for the different arms and additive on the model scale. This holds even if the shape of the time trend deviates from a step function when patients are allocated to arms by block randomisation. However, if time trends differ between arms or are not additive to treatment effects in the scale of the model, the type 1 error rate may be inflated. CONCLUSIONS The efficiency gained by using step function models to incorporate non-concurrent controls can outweigh potential risks of biases, especially in settings with small sample sizes. Such biases may arise if the model assumptions of equality and additivity of time trends are not satisfied. However, the specifics of the trial, scientific plausibility of different time trends, and robustness of results should be carefully considered.
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Affiliation(s)
- Marta Bofill Roig
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Pavla Krotka
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Carl-Fredrik Burman
- Statistical Innovation, Data Science & Artificial Intelligence, AstraZeneca, Gothenburg, Sweden
| | - Ekkehard Glimm
- Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland
- Institute of Biometry and Medical Informatics, University of Magdeburg, Magdeburg, Germany
| | - Stefan M Gold
- Klinik für Psychiatrie und Psychotherapie, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Medizinische Klinik m.S. Psychosomatik, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Zentrum für Molekulare Neurobiologie, Universitätsklinikum Hamburg Eppendorf, Hamburg, Germany
| | - Katharina Hees
- Section of Biostatistics, Paul-Ehrlich-Institut, Langen, Germany
| | - Peter Jacko
- Berry Consultants, Abingdon, UK
- Lancaster University, Lancaster, UK
| | - Franz Koenig
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Dominic Magirr
- Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland
| | - Peter Mesenbrink
- Analytics Global Drug Development, Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA
| | | | - Martin Posch
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
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Fernández A, Noce G, Del Percio C, Pinal D, Díaz F, Lojo-Seoane C, Zurrón M, Babiloni C. Resting state electroencephalographic rhythms are affected by immediately preceding memory demands in cognitively unimpaired elderly and patients with mild cognitive impairment. Front Aging Neurosci 2022; 14:907130. [PMID: 36062151 PMCID: PMC9435320 DOI: 10.3389/fnagi.2022.907130] [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: 03/29/2022] [Accepted: 07/18/2022] [Indexed: 11/23/2022] Open
Abstract
Experiments on event-related electroencephalographic oscillations in aged people typically include blocks of cognitive tasks with a few minutes of interval between them. The present exploratory study tested the effect of being engaged on cognitive tasks over the resting state cortical arousal after task completion, and whether it differs according to the level of the participant’s cognitive decline. To investigate this issue, we used a local database including data in 30 healthy cognitively unimpaired (CU) persons and 40 matched patients with amnestic mild cognitive impairment (aMCI). They had been involved in 2 memory tasks for about 40 min and underwent resting-state electroencephalographic (rsEEG) recording after 5 min from the task end. eLORETA freeware estimated rsEEG alpha source activity as an index of general cortical arousal. In the CU but not aMCI group, there was a negative correlation between memory tasks performance and posterior rsEEG alpha source activity. The better the memory tasks performance, the lower the posterior alpha activity (i.e., higher cortical arousal). There was also a negative correlation between neuropsychological test scores of global cognitive status and alpha source activity. These results suggest that engagement in memory tasks may perturb background brain arousal for more than 5 min after the tasks end, and that this effect are dependent on participants global cognitive status. Future studies in CU and aMCI groups may cross-validate and extend these results with experiments including (1) rsEEG recordings before memory tasks and (2) post-tasks rsEEG recordings after 5, 15, and 30 min.
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Affiliation(s)
- Alba Fernández
- Departamento de Psicoloxía Clínica e Psicobioloxía, Facultade de Psicoloxía, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- *Correspondence: Alba Fernández,
| | | | - Claudio Del Percio
- Department of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome, Rome, Italy
| | - Diego Pinal
- Psychological Neuroscience Lab, Escola de Psicologia, Universidade do Minho, Braga, Portugal
| | - Fernando Díaz
- Departamento de Psicoloxía Clínica e Psicobioloxía, Facultade de Psicoloxía, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Cristina Lojo-Seoane
- Departamento de Psicoloxía Evolutiva e da Educación, Facultade de Psicoloxía, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Montserrat Zurrón
- Departamento de Psicoloxía Clínica e Psicobioloxía, Facultade de Psicoloxía, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome, Rome, Italy
- San Raffaele Cassino, Cassino, Italy
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White IR, Choodari-Oskooei B, Sydes MR, Kahan BC, McCabe L, Turkova A, Esmail H, Gibb DM, Ford D. Combining factorial and multi-arm multi-stage platform designs to evaluate multiple interventions efficiently. Clin Trials 2022; 19:432-441. [PMID: 35579066 PMCID: PMC9373200 DOI: 10.1177/17407745221093577] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Factorial-MAMS design platform designs have many advantages, but the practical advantages and disadvantages of combining the two designs have not been explored. METHODS We propose practical methods for a combined design within the platform trial paradigm where some interventions are not expected to interact and could be given together. RESULTS We describe the combined design and suggest diagrams that can be used to represent it. Many properties are common both to standard factorial designs, including the need to consider interactions between interventions and the impact of intervention efficacy on power of other comparisons, and to standard multi-arm multi-stage designs, including the need to pre-specify procedures for starting and stopping intervention comparisons. We also identify some specific features of the factorial-MAMS design: timing of interim and final analyses should be determined by calendar time or total observed events; some non-factorial modifications may be useful; eligibility criteria should be broad enough to include any patient eligible for any part of the randomisation; stratified randomisation may conveniently be performed sequentially; and analysis requires special care to use only concurrent controls. CONCLUSION A combined factorial-MAMS design can combine the efficiencies of factorial trials and multi-arm multi-stage platform trials. It allows us to address multiple research questions under one protocol and to test multiple new treatment options, which is particularly important when facing a new emergent infection such as COVID-19.
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Noor NM, Love SB, Isaacs T, Kaplan R, Parmar MKB, Sydes MR. Uptake of the multi-arm multi-stage (MAMS) adaptive platform approach: a trial-registry review of late-phase randomised clinical trials. BMJ Open 2022; 12:e055615. [PMID: 35273052 PMCID: PMC8915371 DOI: 10.1136/bmjopen-2021-055615] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND For medical conditions with numerous interventions worthy of investigation, there are many advantages of a multi-arm multi-stage (MAMS) platform trial approach. However, there is currently limited knowledge on uptake of the MAMS design, especially in the late-phase setting. We sought to examine uptake and characteristics of late-phase MAMS platform trials, to enable better planning for teams considering future use of this approach. DESIGN We examined uptake of registered, late-phase MAMS platforms in the EU clinical trials register, Australian New Zealand Clinical Trials Registry, International Standard Randomised Controlled Trial Number registry, Pan African Clinical Trials Registry, WHO International Clinical Trial Registry Platform and databases: PubMed, Medline, Cochrane Library, Global Health Library and EMBASE. Searching was performed and review data frozen on 1 April 2021. MAMS platforms were defined as requiring two or more comparison arms, with two or more trial stages, with an interim analysis allowing for stopping of recruitment to arms and typically the ability to add new intervention arms. RESULTS 62 late-phase clinical trials using an MAMS approach were included. Overall, the number of late-phase trials using the MAMS design has been increasing since 2001 and been accelerated by COVID-19. The majority of current MAMS platforms were either targeting infectious diseases (52%) or cancers (29%) and all identified trials were for treatment interventions. 89% (55/62) of MAMS platforms were evaluating medications, with 45% (28/62) of the MAMS platforms having at least one or more repurposed medication as a comparison arm. CONCLUSIONS Historically, late-phase trials have adhered to long-established standard (two-arm) designs. However, the number of late-phase MAMS platform trials is increasing, across a range of different disease areas. This study highlights the potential scope of MAMS platform trials and may assist research teams considering use of this approach in the late-phase randomised clinical trial setting. PROSPERO REGISTRATION NUMBER CRD42019153910.
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Affiliation(s)
| | | | - Talia Isaacs
- Institute of Education, University College London, London, UK
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VanBuren JM, Casper TC, Nishijima DK, Kuppermann N, Lewis RJ, Dean JM, McGlothlin A. The design of a Bayesian adaptive clinical trial of tranexamic acid in severely injured children. Trials 2021; 22:769. [PMID: 34736498 PMCID: PMC8567588 DOI: 10.1186/s13063-021-05737-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 10/20/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Trauma is the leading cause of death and disability in children in the USA. Tranexamic acid (TXA) reduces the blood transfusion requirements in adults and children during surgery. Several studies have evaluated TXA in adults with hemorrhagic trauma, but no randomized controlled trials have occurred in children with trauma. We propose a Bayesian adaptive clinical trial to investigate TXA in children with brain and/or torso hemorrhagic trauma. METHODS/DESIGN We designed a double-blind, Bayesian adaptive clinical trial that will enroll up to 2000 patients. We extend the traditional Emax dose-response model to incorporate a hierarchical structure so multiple doses of TXA can be evaluated in different injury populations (isolated head injury, isolated torso injury, or both head and torso injury). Up to 3 doses of TXA (15 mg/kg, 30 mg/kg, and 45 mg/kg bolus doses) will be compared to placebo. Equal allocation between placebo, 15 mg/kg, and 30 mg/kg will be used for an initial period within each injury group. Depending on the dose-response curve, the 45 mg/kg arm may open in an injury group if there is a trend towards increasing efficacy based on the observed relationship using the data from the lower doses. Response-adaptive randomization allows each injury group to differ in allocation proportions of TXA so an optimal dose can be identified for each injury group. Frequent interim stopping periods are included to evaluate efficacy and futility. The statistical design is evaluated through extensive simulations to determine the operating characteristics in several plausible scenarios. This trial achieves adequate power in each injury group. DISCUSSION This trial design evaluating TXA in pediatric hemorrhagic trauma allows for three separate injury populations to be analyzed and compared within a single study framework. Individual conclusions regarding optimal dosing of TXA can be made within each injury group. Identifying the optimal dose of TXA, if any, for various injury types in childhood may reduce death and disability.
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Affiliation(s)
- John M. VanBuren
- Department of Pediatrics, University of Utah School of Medicine, 295 Chipeta Way, Salt Lake City, UT 84108 USA
| | - T. Charles Casper
- Department of Pediatrics, University of Utah School of Medicine, 295 Chipeta Way, Salt Lake City, UT 84108 USA
| | - Daniel K. Nishijima
- Department of Emergency Medicine, University of California, Davis School of Medicine, Sacramento, CA 95817 USA
| | - Nathan Kuppermann
- Department of Emergency Medicine, University of California, Davis School of Medicine, Sacramento, CA 95817 USA
- Department of Pediatrics, University of California, Davis School of Medicine, Sacramento, CA 95817 USA
| | - Roger J. Lewis
- Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, CA 90509 USA
- Berry Consultants, LLC, Austin, TX 78746 USA
| | - J. Michael Dean
- Department of Pediatrics, University of Utah School of Medicine, 295 Chipeta Way, Salt Lake City, UT 84108 USA
| | | | - For the TIC-TOC Collaborators of the Pediatric Emergency Care Applied Research Network (PECARN)
- Department of Pediatrics, University of Utah School of Medicine, 295 Chipeta Way, Salt Lake City, UT 84108 USA
- Department of Emergency Medicine, University of California, Davis School of Medicine, Sacramento, CA 95817 USA
- Department of Pediatrics, University of California, Davis School of Medicine, Sacramento, CA 95817 USA
- Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, CA 90509 USA
- Berry Consultants, LLC, Austin, TX 78746 USA
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Park JJH, Dron L, Mills EJ. Moving forward in clinical research with master protocols. Contemp Clin Trials 2021; 106:106438. [PMID: 34000408 PMCID: PMC8120789 DOI: 10.1016/j.cct.2021.106438] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/15/2022]
Abstract
With billions of dollars in research and development (R&D) funding continuing to be invested, the novel coronavirus disease 2019 (COVID-19) has become into a singular focus for the scientific community. However, the collective response from the scientific communities have seen poor return on investment, particularly for therapeutic research for COVID-19, revealing the existing weaknesses and inefficiencies of the clinical trial enterprise. In this article, we argue for the importance of structural changes to existing research programs for clinical trials in light of the lessons learned from COVID-19.
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Affiliation(s)
- Jay J H Park
- Experimental Medicine, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Louis Dron
- Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Edward J Mills
- Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
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