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Orsso CE, Ford KL, Kiss N, Trujillo EB, Spees CK, Hamilton-Reeves JM, Prado CM. Optimizing clinical nutrition research: the role of adaptive and pragmatic trials. Eur J Clin Nutr 2023; 77:1130-1142. [PMID: 37715007 PMCID: PMC10861156 DOI: 10.1038/s41430-023-01330-7] [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: 05/19/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 09/17/2023]
Abstract
Evidence-based nutritional recommendations address the health impact of suboptimal nutritional status. Efficacy randomized controlled trials (RCTs) have traditionally been the preferred method for determining the effects of nutritional interventions on health outcomes. Nevertheless, obtaining a holistic understanding of intervention efficacy and effectiveness in real-world settings is stymied by inherent constraints of efficacy RCTs. These limitations are further compounded by the complexity of nutritional interventions and the intricacies of the clinical context. Herein, we explore the advantages and limitations of alternative study designs (e.g., adaptive and pragmatic trials), which can be incorporated into RCTs to optimize the efficacy or effectiveness of interventions in clinical nutrition research. Efficacy RCTs often lack external validity due to their fixed design and restrictive eligibility criteria, leading to efficacy-effectiveness and evidence-practice gaps. Adaptive trials improve the evaluation of nutritional intervention efficacy through planned study modifications, such as recalculating sample sizes or discontinuing a study arm. Pragmatic trials are embedded within clinical practice or conducted in settings that resemble standard of care, enabling a more comprehensive assessment of intervention effectiveness. Pragmatic trials often rely on patient-oriented primary outcomes, acquire outcome data from electronic health records, and employ broader eligibility criteria. Consequently, adaptive and pragmatic trials facilitate the prompt implementation of evidence-based nutritional recommendations into clinical practice. Recognizing the limitations of efficacy RCTs and the potential advantages of alternative trial designs is essential for bridging efficacy-effectiveness and evidence-practice gaps. Ultimately, this awareness will lead to a greater number of patients benefiting from evidence-based nutritional recommendations.
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Affiliation(s)
- Camila E Orsso
- Human Nutrition Research Unit, Department of Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Katherine L Ford
- Human Nutrition Research Unit, Department of Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada
- Department of Kinesiology & Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Nicole Kiss
- Institute for Physical Activity and Nutrition, Deakin University, Geelong, VIC, Australia
| | - Elaine B Trujillo
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Colleen K Spees
- Divison of Medical Dietetics, School of Health and Rehabilitation Sciences, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Jill M Hamilton-Reeves
- Department of Urology, University of Kansas Medical Center, Kansas City, KS, USA
- Department of Dietetics and Nutrition, University of Kansas Medical Center, Kansas City, KS, USA
| | - Carla M Prado
- Human Nutrition Research Unit, Department of Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada.
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Mano H, Tanaka Y, Orihara S, Moriya J. Application of sample size re-estimation in clinical trials: A systematic review. Contemp Clin Trials Commun 2023; 36:101210. [PMID: 37842317 PMCID: PMC10568275 DOI: 10.1016/j.conctc.2023.101210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 08/03/2023] [Accepted: 09/15/2023] [Indexed: 10/17/2023] Open
Abstract
Background Sample size re-estimation (SSR) is a method used to recalculate sample size during clinical trial conduct to address a lack of adequate information and can have a significant impact on study size, duration, resources, and cost. Few studies to date have summarized the conditions and circumstances under which SSR is applied. We therefore performed a systematic review of the literature related to SSR to better understand its application in clinical trial settings. Methods PubMed was used as the primary search source, supplemented with information from ClinicalTrials.gov where necessary details were lacking from PubMed. A systematic review was performed according to a pre-specified search strategy to identify clinical trials using SSR. Features of SSR, such as study phase and study start year, were summarized. Results In total, 253 publications met the pre-specified search criteria and 27 clinical trials were subsequently determined as relevant in SSR usage. Among trials where the study phase was provided, 2 (7.4%) trials were Phase I, 5 (18.5%) trials were Phase II, 11 (40.7%) trials were Phase III, and 2 (7.4%) trials were Phase IV. Conclusion Our results showed that SSR is also used in Phase I and II, which involve earlier decision making. We expect that SSR will continue to be used in early-phase trials where sufficient prior information may not be available. Furthermore, no major trends were observed in relation to therapy area or type of SSR, meaning that SSR may become a feasible and widely applied method in the future.
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Affiliation(s)
- Hirotaka Mano
- Biostatistics Group, Biometrics Department, Development Unit, R&D Division, Kyowa Kirin Co., Ltd., Otemachi Financial City Grand Cube, 1-9-2 Otemachi, Chiyoda-ku, Tokyo, Japan
| | - Yuji Tanaka
- Biostatistics Group, Biometrics Department, Development Unit, R&D Division, Kyowa Kirin Co., Ltd., Otemachi Financial City Grand Cube, 1-9-2 Otemachi, Chiyoda-ku, Tokyo, Japan
| | - Shunichiro Orihara
- Biostatistics Group, Biometrics Department, Development Unit, R&D Division, Kyowa Kirin Co., Ltd., Otemachi Financial City Grand Cube, 1-9-2 Otemachi, Chiyoda-ku, Tokyo, Japan
| | - Junji Moriya
- Biostatistics Group, Biometrics Department, Development Unit, R&D Division, Kyowa Kirin Co., Ltd., Otemachi Financial City Grand Cube, 1-9-2 Otemachi, Chiyoda-ku, Tokyo, Japan
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D’Amico F, Danese S, Peyrin-Biroulet L. Adaptive Designs: Lessons for Inflammatory Bowel Disease Trials. J Clin Med 2020; 9:jcm9082350. [PMID: 32717997 PMCID: PMC7464489 DOI: 10.3390/jcm9082350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 07/10/2020] [Accepted: 07/22/2020] [Indexed: 11/16/2022] Open
Abstract
In recent decades, scientific research has considerably evolved in the field of inflammatory bowel diseases (IBD) and clinical studies have become increasingly complex, including new outcomes, different study populations, and additional techniques of re-randomization and centralized control. In this context, randomized clinical trials are the gold standard for new drugs’ development. However, traditional study designs are time-consuming, expensive, and only a small percentage of the tested therapies are approved. For this reason, a new study design called “adaptive design” has been introduced, allowing to accumulate data during the study and to make predefined adjustments based on the results of scheduled interim analysis. Our aim is to clarify the advantages and drawbacks of adaptive designs in order to properly interpret study results and to identify their role in upcoming IBD trials.
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Affiliation(s)
- Ferdinando D’Amico
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy; (F.D.); (S.D.)
- Department of Gastroenterology and Inserm NGERE U1256, Nancy University Hospital, University of Lorraine, 54500 Vandoeuvre-lès-Nancy, France
| | - Silvio Danese
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy; (F.D.); (S.D.)
- Department of Gastroenterology, Humanitas Clinical and Research Center-IRCCS, Rozzano, 20089 Milan, Italy
| | - Laurent Peyrin-Biroulet
- Department of Gastroenterology and Inserm NGERE U1256, Nancy University Hospital, University of Lorraine, 54500 Vandoeuvre-lès-Nancy, France
- Correspondence: ; Tel.: +33-3831-53661
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Dimairo M, Pallmann P, Wason J, Todd S, Jaki T, Julious SA, Mander AP, Weir CJ, Koenig F, Walton MK, Nicholl JP, Coates E, Biggs K, Hamasaki T, Proschan MA, Scott JA, Ando Y, Hind D, Altman DG. The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. Trials 2020; 21:528. [PMID: 32546273 PMCID: PMC7298968 DOI: 10.1186/s13063-020-04334-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits. In order to encourage its wide dissemination this article is freely accessible on the BMJ and Trials journal websites."To maximise the benefit to society, you need to not just do research but do it well" Douglas G Altman.
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Affiliation(s)
- Munyaradzi Dimairo
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK.
| | | | - James Wason
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Institute of Health and Society, Newcastle University, Newcastle, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, Reading, UK
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Adrian P Mander
- Centre for Trials Research, Cardiff University, Cardiff, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Franz Koenig
- Centre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Marc K Walton
- Janssen Pharmaceuticals, Titusville, New Jersey, USA
| | - Jon P Nicholl
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Elizabeth Coates
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Katie Biggs
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | | | - Michael A Proschan
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | - John A Scott
- Division of Biostatistics in the Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, USA
| | - Yuki Ando
- Pharmaceuticals and Medical Devices Agency, Tokyo, Japan
| | - Daniel Hind
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Douglas G Altman
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
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5
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Dimairo M, Pallmann P, Wason J, Todd S, Jaki T, Julious SA, Mander AP, Weir CJ, Koenig F, Walton MK, Nicholl JP, Coates E, Biggs K, Hamasaki T, Proschan MA, Scott JA, Ando Y, Hind D, Altman DG. The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. BMJ 2020; 369:m115. [PMID: 32554564 PMCID: PMC7298567 DOI: 10.1136/bmj.m115] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/19/2019] [Indexed: 12/11/2022]
Abstract
Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits.
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Affiliation(s)
- Munyaradzi Dimairo
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | | | - James Wason
- MRC Biostatistics Unit, University of Cambridge, UK
- Institute of Health and Society, Newcastle University, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, UK
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, UK
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Adrian P Mander
- Centre for Trials Research, Cardiff University, UK
- MRC Biostatistics Unit, University of Cambridge, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, UK
| | - Franz Koenig
- Centre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Austria
| | | | - Jon P Nicholl
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Elizabeth Coates
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Katie Biggs
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | | | - Michael A Proschan
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, USA
| | - John A Scott
- Division of Biostatistics in the Center for Biologics Evaluation and Research, Food and Drug Administration, USA
| | - Yuki Ando
- Pharmaceuticals and Medical Devices Agency, Japan
| | - Daniel Hind
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
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Pallmann P, Bedding AW, Choodari-Oskooei B, Dimairo M, Flight L, Hampson LV, Holmes J, Mander AP, Odondi L, Sydes MR, Villar SS, Wason JMS, Weir CJ, Wheeler GM, Yap C, Jaki T. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med 2018; 16:29. [PMID: 29490655 PMCID: PMC5830330 DOI: 10.1186/s12916-018-1017-7] [Citation(s) in RCA: 361] [Impact Index Per Article: 60.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 01/30/2018] [Indexed: 12/11/2022] Open
Abstract
Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.
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Affiliation(s)
- Philip Pallmann
- Department of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF UK
| | | | - Babak Choodari-Oskooei
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | | | - Laura Flight
- Medical Statistics Group, University of Sheffield, Sheffield, UK
| | - Lisa V. Hampson
- Department of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF UK
- Statistical Innovation Group, Advanced Analytics Centre, AstraZeneca, Cambridge, UK
| | - Jane Holmes
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | | | - Lang’o Odondi
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Matthew R. Sydes
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Sofía S. Villar
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - James M. S. Wason
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Institute of Health and Society, Newcastle University, Newcastle, UK
| | - Christopher J. Weir
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Graham M. Wheeler
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cancer Research UK & UCL Cancer Trials Centre, University College London, London, UK
| | - Christina Yap
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, UK
| | - Thomas Jaki
- Department of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF UK
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Efficiency Considerations for Group Sequential Designs with Adaptive Unblinded Sample Size Re-assessment. STATISTICS IN BIOSCIENCES 2017. [DOI: 10.1007/s12561-017-9188-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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