1
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Innovations in Clinical Development in Rare Diseases of Children and Adults: Small Populations and/or Small Patients. Paediatr Drugs 2022; 24:657-669. [PMID: 36241954 DOI: 10.1007/s40272-022-00538-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/11/2022] [Indexed: 10/17/2022]
Abstract
Many of the afflictions of children are rare diseases. This creates numerous drug development challenges related to small populations, including limited information about the disease state, enrollment challenges, and diminished incentives for pediatric development of novel therapies by pharmaceutical and biotechnology sponsors. We review selected innovations in clinical development that may partially mitigate some of these difficulties, starting with the concept of development efficiency for individual clinical trials, clinical programs (involving multiple trials for a single drug), and clinical portfolios of multiple drugs, and decision analysis as a tool to optimize efficiency. Development efficiency is defined as the ability to reach equally rigorous or more rigorous conclusions in less time, with fewer trial participants, or with fewer resources. We go on to discuss efficient methods for matching targeted therapies to biomarker-defined subgroups, methods for eliminating or reducing the need for natural history data to guide rare disease development, the use of basket trials to enhance efficiency by grouping multiple similar disease applications in a single clinical trial, and the use of alternative data sources including historical controls to augment or replace concurrent controls in clinical studies. Greater understanding and broader application of these methods could lead to improved therapies and/or more widespread and rapid access to novel therapies for rare diseases in both children and adults.
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2
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Jackson H, Jaki T. An alternative to traditional sample size determination for small patient populations. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2107565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
- Holly Jackson
- Department of Mathematics and Statistics, Lancaster University, Lancaster, U.K
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, U.K
- MRC Biostatistics Unit, University of Cambridge, Cambridge, U.K
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3
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Kyr M, Svobodnik A, Stepanova R, Hejnova R. N-of-1 Trials in Pediatric Oncology: From a Population-Based Approach to Personalized Medicine-A Review. Cancers (Basel) 2021; 13:5428. [PMID: 34771590 PMCID: PMC8582573 DOI: 10.3390/cancers13215428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 10/11/2021] [Accepted: 10/27/2021] [Indexed: 12/02/2022] Open
Abstract
Pediatric oncology is a critical area where the more efficient development of new treatments is urgently needed. The speed of approval of new drugs is still limited by regulatory requirements and a lack of innovative designs appropriate for trials in children. Childhood cancers meet the criteria of rare diseases. Personalized medicine brings it even closer to the horizon of individual cases. Thus, not all the traditional research tools, such as large-scale RCTs, are always suitable or even applicable, mainly due to limited sample sizes. Small samples and traditional versus subject-specific evidence are both distinctive issues in personalized pediatric oncology. Modern analytical approaches and adaptations of the paradigms of evidence are warranted. We have reviewed innovative trial designs and analytical methods developed for small populations, together with individualized approaches, given their applicability to pediatric oncology. We discuss traditional population-based and individualized perspectives of inferences and evidence, and explain the possibilities of using various methods in pediatric personalized oncology. We find that specific derivatives of the original N-of-1 trial design adapted for pediatric personalized oncology may represent an optimal analytical tool for this area of medicine. We conclude that no particular N-of-1 strategy can provide a solution. Rather, a whole range of approaches is needed to satisfy the new inferential and analytical paradigms of modern medicine. We reveal a new view of cancer as continuum model and discuss the "evidence puzzle".
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Affiliation(s)
- Michal Kyr
- Department of Paediatric Oncology, University Hospital Brno and School of Medicine, Masaryk University, Cernopolni 9, 613 00 Brno, Czech Republic
- International Clinical Research Centre, St. Anne’s University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Adam Svobodnik
- Department of Pharmacology, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; (A.S.); (R.S.) (R.H.)
| | - Radka Stepanova
- Department of Pharmacology, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; (A.S.); (R.S.) (R.H.)
| | - Renata Hejnova
- Department of Pharmacology, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; (A.S.); (R.S.) (R.H.)
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4
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Wiklund SJ, Burman CF. Selection bias, investment decisions and treatment effect distributions. Pharm Stat 2021; 20:1168-1182. [PMID: 34002467 PMCID: PMC9290610 DOI: 10.1002/pst.2132] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 04/09/2021] [Accepted: 05/03/2021] [Indexed: 11/08/2022]
Abstract
When making decisions regarding the investment and design for a Phase 3 programme in the development of a new drug, the results from preceding Phase 2 trials are an important source of information. However, only projects in which the Phase 2 results show promising treatment effects will typically be considered for a Phase 3 investment decision. This implies that, for those projects where Phase 3 is pursued, the underlying Phase 2 estimates are subject to selection bias. We will in this article investigate the nature of this selection bias based on a selection of distributions for the treatment effect. We illustrate some properties of Bayesian estimates, providing shrinkage of the Phase 2 estimate to counteract the selection bias. We further give some empirical guidance regarding the choice of prior distribution and comment on the consequences for decision-making in investment and planning for Phase 3 programmes.
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5
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Michelet R, Ursino M, Boulet S, Franck S, Casilag F, Baldry M, Rolff J, van Dyk M, Wicha SG, Sirard JC, Comets E, Zohar S, Kloft C. The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia. Pharmaceutics 2021; 13:601. [PMID: 33922017 PMCID: PMC8143524 DOI: 10.3390/pharmaceutics13050601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 04/16/2021] [Accepted: 04/20/2021] [Indexed: 11/16/2022] Open
Abstract
The treatment of respiratory tract infections is threatened by the emergence of bacterial resistance. Immunomodulatory drugs, which enhance airway innate immune defenses, may improve therapeutic outcome. In this concept paper, we aim to highlight the utility of pharmacometrics and Bayesian inference in the development of immunomodulatory therapeutic agents as an adjunct to antibiotics in the context of pneumonia. For this, two case studies of translational modelling and simulation frameworks are introduced for these types of drugs up to clinical use. First, we evaluate the pharmacokinetic/pharmacodynamic relationship of an experimental combination of amoxicillin and a TLR4 agonist, monophosphoryl lipid A, by developing a pharmacometric model accounting for interaction and potential translation to humans. Capitalizing on this knowledge and associating clinical trial extrapolation and statistical modelling approaches, we then investigate the TLR5 agonist flagellin. The resulting workflow combines expert and prior knowledge on the compound with the in vitro and in vivo data generated during exploratory studies in order to construct high-dimensional models considering the pharmacokinetics and pharmacodynamics of the compound. This workflow can be used to refine preclinical experiments, estimate the best doses for human studies, and create an adaptive knowledge-based design for the next phases of clinical development.
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Affiliation(s)
- Robin Michelet
- Department of Clinical Pharmacy & Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, 12169 Berlin, Germany; (S.F.); (C.K.)
| | - Moreno Ursino
- Unit of Clinical Epidemiology, Assistance Publique-Hôpitaux de Paris, CHU Robert Debré, Université de Paris, Sorbonne Paris-Cité, Inserm U1123 and CIC-EC 1426, F-75019 Paris, France;
- INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, F-75006 Paris, France; (S.B.); (S.Z.)
| | - Sandrine Boulet
- INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, F-75006 Paris, France; (S.B.); (S.Z.)
- HeKA, Inria, F-75006 Paris, France
| | - Sebastian Franck
- Department of Clinical Pharmacy & Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, 12169 Berlin, Germany; (S.F.); (C.K.)
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, 20146 Hamburg, Germany;
| | - Fiordiligie Casilag
- CNRS, Inserm, CHU Lille, Institute Pasteur de Lille, U1019-UMR9017-CIIL-Centre for Infection and Immunity of Lille, Université de Lille, F-59000 Lille, France; (F.C.); (M.B.); (J.-C.S.)
| | - Mara Baldry
- CNRS, Inserm, CHU Lille, Institute Pasteur de Lille, U1019-UMR9017-CIIL-Centre for Infection and Immunity of Lille, Université de Lille, F-59000 Lille, France; (F.C.); (M.B.); (J.-C.S.)
| | - Jens Rolff
- Department of Evolutionary Biology, Institute of Biology, Freie Universitaet Berlin, 14195 Berlin, Germany;
| | - Madelé van Dyk
- Flinders Centre for Innovation in Cancer, College of Medicine and Public Health, Flinders University, Adelaide 5042, Australia;
| | - Sebastian G. Wicha
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, 20146 Hamburg, Germany;
| | - Jean-Claude Sirard
- CNRS, Inserm, CHU Lille, Institute Pasteur de Lille, U1019-UMR9017-CIIL-Centre for Infection and Immunity of Lille, Université de Lille, F-59000 Lille, France; (F.C.); (M.B.); (J.-C.S.)
| | - Emmanuelle Comets
- INSERM, University Rennes-1, CIC 1414, F-35000 Rennes, France;
- INSERM, IAME, Université de Paris, F-75006 Paris, France
| | - Sarah Zohar
- INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, F-75006 Paris, France; (S.B.); (S.Z.)
- HeKA, Inria, F-75006 Paris, France
| | - Charlotte Kloft
- Department of Clinical Pharmacy & Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, 12169 Berlin, Germany; (S.F.); (C.K.)
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6
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Collignon O, Burman CF, Posch M, Schiel A. Collaborative Platform Trials to Fight COVID-19: Methodological and Regulatory Considerations for a Better Societal Outcome. Clin Pharmacol Ther 2021; 110:311-320. [PMID: 33506495 PMCID: PMC8014457 DOI: 10.1002/cpt.2183] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 01/19/2021] [Indexed: 12/19/2022]
Abstract
For the development of coronavirus disease 2019 (COVID‐19) drugs during the ongoing pandemic, speed is of essence whereas quality of evidence is of paramount importance. Although thousands of COVID‐19 trials were rapidly started, many are unlikely to provide robust statistical evidence and meet regulatory standards (e.g., because of lack of randomization or insufficient power). This has led to an inefficient use of time and resources. With more coordination, the sheer number of patients in these trials might have generated convincing data for several investigational treatments. Collaborative platform trials, comparing several drugs to a shared control arm, are an attractive solution. Those trials can utilize a variety of adaptive design features in order to accelerate the finding of life‐saving treatments. In this paper, we discuss several possible designs, illustrate them via simulations, and also discuss challenges, such as the heterogeneity of the target population, time‐varying standard of care, and the potentially high number of false hypothesis rejections in phase II and phase III trials. We provide corresponding regulatory perspectives on approval and reimbursement, and note that the optimal design of a platform trial will differ with our societal objective and by stakeholder. Hasty approvals may delay the development of better alternatives, whereas searching relentlessly for the single most efficacious treatment may indirectly diminish the number of lives saved as time is lost. We point out the need for incentivizing developers to participate in collaborative evidence‐generation initiatives when a positive return on investment is not met.
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Affiliation(s)
| | - Carl-Fredrik Burman
- Statistical Innovation, Data Science, and Artificial Intelligence, AstraZeneca R&D, Gothenburg, Sweden
| | - Martin Posch
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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7
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Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18031022. [PMID: 33498915 PMCID: PMC7908592 DOI: 10.3390/ijerph18031022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 01/15/2021] [Accepted: 01/20/2021] [Indexed: 11/30/2022]
Abstract
The aim of this narrative review is to introduce the reader to Bayesian methods that, in our opinion, appear to be the most important in the context of rare diseases. A disease is defined as rare depending on the prevalence of the affected patients in the considered population, for example, about 1 in 1500 people in U.S.; about 1 in 2500 people in Japan; and fewer than 1 in 2000 people in Europe. There are between 6000 and 8000 rare diseases and the main issue in drug development is linked to the challenge of achieving robust evidence from clinical trials in small populations. A better use of all available information can help the development process and Bayesian statistics can provide a solid framework at the design stage, during the conduct of the trial, and at the analysis stage. The focus of this manuscript is to provide a review of Bayesian methods for sample size computation or reassessment during phase II or phase III trial, for response adaptive randomization and of for meta-analysis in rare disease. Challenges regarding prior distribution choice, computational burden and dissemination are also discussed.
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8
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Zhang P, Cao Y, Chen S, Shao L. Combination of Vinpocetine and Dexamethasone Alleviates Cognitive Impairment in Nasopharyngeal Carcinoma Patients following Radiation Injury. Pharmacology 2020; 106:37-44. [PMID: 32294652 DOI: 10.1159/000506777] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 02/24/2020] [Indexed: 11/19/2022]
Abstract
BACKGROUND Nasopharyngeal carcinoma (NPC) originates in the nasopharyngeal epithelium. The most common treatments for NPC rT1-4 are radiotherapy and surgery. The pathogenesis of radiation-induced cognitive impairment is complex and includes oxidative stress, mitochondrial dysfunction, neuro-inflammation, and even apoptosis and cell death. Principally, toll-like receptors (TLRs) could regulate the inflammatory/anti-inflammatory balance in patients with radiation-induced brain injury. Vinpocetine has an anti-inflammatory effect as shown in both animal and in vitro studies. Also, dexamethasone is a widely used anti-inflammatory drug. Thus, it is important to test whether addition of vinpocetine could improve the anti-inflammatory properties of dexamethasone for the treatment of NPC patients with radiation-induced brain injuries. METHODS A total of 60 NPC patients with radiation-related brain injury were recruited for this study. All subjects were randomly and blindly assigned to the following groups: the dexamethasone group (D group, n = 30) and the vinpocetine and dexamethasone group (VD group, n = 30). Both medicine treatments were uninterrupted for 14 days of administration. RESULTS Combined administration of vinpocetine and dexamethasone lowered the expression levels of serum inflammatory cytokines, including TLR2, TLR4, interleukin (IL)-20, IL-8, tumor necrosis factor-α, interferon-γ, monocyte chemoattractant protein 2, and interferon-induced protein 20, when compared to dexamethasone monotherapy. Notably, combination therapy increased antioxidants (superoxide dismutase, glutathione, glutathione peroxidase, and glutathione reductase) and decreased oxidants (thiobarbituric acid reactive substances). Furthermore, combination therapy significantly increased the Mini Mental State Examination score, when compared to dexamethasone monotherapy. CONCLUSION Administration of a combination of vinpocetine and dexamethasone may enhance the anti-inflammatory and anti-oxidative effects when compared to dexamethasone monotherapy, which leads to alleviated cognitive impairment in NPC patients with radiation injury.
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Affiliation(s)
- Ping Zhang
- Department of Neurology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, China.,Department of Geriatrics & Neurology, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, China
| | - Yungang Cao
- Department of Geriatrics & Neurology, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, China
| | - Songfang Chen
- Department of Geriatrics & Neurology, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, China
| | - Liang Shao
- Department of Cardiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, China,
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9
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Kýr M, Klement GL, Zdrazilova-Dubska L, Demlova R, Valik D, Slaby O, Slavc I, Sterba J. Editorial: Precision/Personalized Pediatric Oncology and Immune Therapies: Rather Customize Than Randomize. Front Oncol 2020; 10:377. [PMID: 32269965 PMCID: PMC7109439 DOI: 10.3389/fonc.2020.00377] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 03/04/2020] [Indexed: 11/16/2022] Open
Affiliation(s)
- Michal Kýr
- Department of Pediatric Oncology, University Hospital Brno, Brno, Czechia.,Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Giannoula Lakka Klement
- Department of Pediatric Oncology, University Hospital Brno, Brno, Czechia.,CSTS Health Care, Toronto, ON, Canada.,Floating Hospital for Children at Tufts Medical Center, Boston, MA, United States
| | - Lenka Zdrazilova-Dubska
- Department of Laboratory Medicine, Masaryk Memorial Cancer Institute, Brno, Czechia.,Department of Pharmacology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Regina Demlova
- Department of Pharmacology, Faculty of Medicine, Masaryk University, Brno, Czechia.,Department of Clinical Trials, Masaryk Memorial Cancer Institute, Brno, Czechia
| | - Dalibor Valik
- Department of Pediatric Oncology, University Hospital Brno, Brno, Czechia.,Department of Laboratory Medicine, Masaryk Memorial Cancer Institute, Brno, Czechia.,Department of Pharmacology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Ondrej Slaby
- Department of Pathology, University Hospital Brno, Brno, Czechia.,Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia
| | - Irene Slavc
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Jaroslav Sterba
- Department of Pediatric Oncology, University Hospital Brno, Brno, Czechia.,Faculty of Medicine, Masaryk University, Brno, Czechia
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10
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Rosner GL. Bayesian Methods in Regulatory Science. Stat Biopharm Res 2019; 12:130-136. [PMID: 32489520 PMCID: PMC7265656 DOI: 10.1080/19466315.2019.1668843] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 08/22/2019] [Accepted: 09/08/2019] [Indexed: 10/26/2022]
Abstract
Regulatory science comprises the tools, standards, and approaches that regulators use to assess safety, efficacy, quality, and performance of drugs and medical devices. A major focus of regulatory science is the design and analysis of clinical trials. Clinical trials are an essential part of clinical research programs that aim to improve therapies and reduce the burden of disease. These clinical experiments help us learn about what works clinically and what does not work. The results of clinical trials support therapeutic and policy decisions. When designing clinical trials, investigators make many decisions regarding various aspects of how they will carry out the study, such as the primary objective of the study, primary and secondary endpoints, methods of analysis, sample size, etc. This paper provides a brief review of the clinical development of new treatments and argues for the use of Bayesian methods and decision theory in clinical research.
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Affiliation(s)
- Gary L Rosner
- Division of Oncology Biostatistics & Bioinformatics, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore MD 21205
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11
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Cook JA, Julious SA, Sones W, Hampson LV, Hewitt C, Berlin JA, Ashby D, Emsley R, Fergusson DA, Walters SJ, Wilson EC, MacLennan G, Stallard N, Rothwell JC, Bland M, Brown L, Ramsay CR, Cook A, Armstrong D, Altman D, Vale LD. Practical help for specifying the target difference in sample size calculations for RCTs: the DELTA 2 five-stage study, including a workshop. Health Technol Assess 2019; 23:1-88. [PMID: 31661431 PMCID: PMC6843113 DOI: 10.3310/hta23600] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND The randomised controlled trial is widely considered to be the gold standard study for comparing the effectiveness of health interventions. Central to its design is a calculation of the number of participants needed (the sample size) for the trial. The sample size is typically calculated by specifying the magnitude of the difference in the primary outcome between the intervention effects for the population of interest. This difference is called the 'target difference' and should be appropriate for the principal estimand of interest and determined by the primary aim of the study. The target difference between treatments should be considered realistic and/or important by one or more key stakeholder groups. OBJECTIVE The objective of the report is to provide practical help on the choice of target difference used in the sample size calculation for a randomised controlled trial for researchers and funder representatives. METHODS The Difference ELicitation in TriAls2 (DELTA2) recommendations and advice were developed through a five-stage process, which included two literature reviews of existing funder guidance and recent methodological literature; a Delphi process to engage with a wider group of stakeholders; a 2-day workshop; and finalising the core document. RESULTS Advice is provided for definitive trials (Phase III/IV studies). Methods for choosing the target difference are reviewed. To aid those new to the topic, and to encourage better practice, 10 recommendations are made regarding choosing the target difference and undertaking a sample size calculation. Recommended reporting items for trial proposal, protocols and results papers under the conventional approach are also provided. Case studies reflecting different trial designs and covering different conditions are provided. Alternative trial designs and methods for choosing the sample size are also briefly considered. CONCLUSIONS Choosing an appropriate sample size is crucial if a study is to inform clinical practice. The number of patients recruited into the trial needs to be sufficient to answer the objectives; however, the number should not be higher than necessary to avoid unnecessary burden on patients and wasting precious resources. The choice of the target difference is a key part of this process under the conventional approach to sample size calculations. This document provides advice and recommendations to improve practice and reporting regarding this aspect of trial design. Future work could extend the work to address other less common approaches to the sample size calculations, particularly in terms of appropriate reporting items. FUNDING Funded by the Medical Research Council (MRC) UK and the National Institute for Health Research as part of the MRC-National Institute for Health Research Methodology Research programme.
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Affiliation(s)
- Jonathan A Cook
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Steven A Julious
- Medical Statistics Group, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - William Sones
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Lisa V Hampson
- Statistical Methodology and Consulting, Novartis Pharma AG, Basel, Switzerland
| | - Catherine Hewitt
- York Trials Unit, Department of Health Sciences, University of York, York, UK
| | | | - Deborah Ashby
- Imperial Clinical Trials Unit, Imperial College London, London, UK
| | - Richard Emsley
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Dean A Fergusson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Stephen J Walters
- Medical Statistics Group, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Edward Cf Wilson
- Cambridge Centre for Health Services Research, Cambridge Clinical Trials Unit University of Cambridge, Cambridge, UK
- Health Economics Group, Norwich Medical School, University of East Anglia, Norwich, UK
| | - Graeme MacLennan
- Centre for Healthcare Randomised Trials, University of Aberdeen, Aberdeen, UK
| | - Nigel Stallard
- Warwick Medical School, Statistics and Epidemiology, University of Warwick, Coventry, UK
| | - Joanne C Rothwell
- Medical Statistics Group, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Martin Bland
- Department of Health Sciences, University of York, York, UK
| | - Louise Brown
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Craig R Ramsay
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK
| | - Andrew Cook
- Wessex Institute, University of Southampton, Southampton, UK
| | - David Armstrong
- School of Population Health and Environmental Sciences, King's College London, London, UK
| | - Douglas Altman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Luke D Vale
- Health Economics Group, Institute of Health & Society, Newcastle University, Newcastle upon Tyne, UK
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12
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Lee KM, Wason J, Stallard N. To add or not to add a new treatment arm to a multiarm study: A decision-theoretic framework. Stat Med 2019; 38:3305-3321. [PMID: 31115078 PMCID: PMC6619445 DOI: 10.1002/sim.8194] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 03/05/2019] [Accepted: 04/17/2019] [Indexed: 12/16/2022]
Abstract
Multiarm clinical trials, which compare several experimental treatments against control, are frequently recommended due to their efficiency gain. In practise, all potential treatments may not be ready to be tested in a phase II/III trial at the same time. It has become appealing to allow new treatment arms to be added into on‐going clinical trials using a “platform” trial approach. To the best of our knowledge, many aspects of when to add arms to an existing trial have not been explored in the literature. Most works on adding arm(s) assume that a new arm is opened whenever a new treatment becomes available. This strategy may prolong the overall duration of a study or cause reduction in marginal power for each hypothesis if the adaptation is not well accommodated. Within a two‐stage trial setting, we propose a decision‐theoretic framework to investigate when to add or not to add a new treatment arm based on the observed stage one treatment responses. To account for different prospect of multiarm studies, we define utility in two different ways; one for a trial that aims to maximise the number of rejected hypotheses; the other for a trial that would declare a success when at least one hypothesis is rejected from the study. Our framework shows that it is not always optimal to add a new treatment arm to an existing trial. We illustrate a case study by considering a completed trial on knee osteoarthritis.
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Affiliation(s)
- Kim May Lee
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - James Wason
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.,Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK
| | - Nigel Stallard
- WMS - Statistics and Epidemiology, University of Warwick, Coventry, UK
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13
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Rufo MJ, Martín J, Pérez CJ, Paniagua S. A Bayesian decision analysis approach to assess voice disorder risks by using acoustic features. Biom J 2018; 61:503-513. [PMID: 30408226 DOI: 10.1002/bimj.201700233] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 06/30/2018] [Accepted: 08/23/2018] [Indexed: 11/09/2022]
Abstract
Vocal fold nodules are recognized as an occupational disease for all collective of workers performing activities for which maintained and continued use of voice is required. Computer-aided systems based on features extracted from voice recordings have been considered as potential noninvasive and low cost tools to diagnose some voice-related diseases. A Bayesian decision analysis approach has been proposed to classify university lectures in three levels of risk: low, medium, and high, based on the information provided by acoustic features extracted from healthy controls and people suffering from vocal fold nodules. The proposed risk groups are associated with different treatments. The approach is based on the calculation of posterior probabilities of developing vocal fold nodules and considers utility functions that include the financial cost and the probability of recovery for the corresponding treatment. Maximization of the expected utilities is considered. By using this approach, the risk of having vocal fold nodules is identified for each university lecturer, so he/she can be properly assigned to the right treatment. The approach has been applied to university lecturers according to the Disease Prevention Program of the University of Extremadura. However, it can also be applied to other voice professionals (singers, speakers, coaches, actors…).
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Affiliation(s)
- María J Rufo
- Department of Mathematics, School of Technology, University of Extremadura, Cáceres, Spain
| | - Jacinto Martín
- Department of Mathematics, Faculty of Science and ICCAEx, University of Extremadura, Badajoz, Spain
| | - Carlos J Pérez
- Department of Mathematics, Faculty of Veterinary, University of Extremadura, Cáceres, Spain
| | - Sandra Paniagua
- Department of Nursing, Faculty of Nursing, University of Extremadura, Mérida, Spain
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14
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Cook JA, Julious SA, Sones W, Hampson LV, Hewitt C, Berlin JA, Ashby D, Emsley R, Fergusson DA, Walters SJ, Wilson ECF, Maclennan G, Stallard N, Rothwell JC, Bland M, Brown L, Ramsay CR, Cook A, Armstrong D, Altman D, Vale LD. DELTA 2 guidance on choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial. Trials 2018; 19:606. [PMID: 30400926 PMCID: PMC6218987 DOI: 10.1186/s13063-018-2884-0] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 08/29/2018] [Indexed: 12/29/2022] Open
Abstract
Background A key step in the design of a RCT is the estimation of the number of participants needed in the study. The most common approach is to specify a target difference between the treatments for the primary outcome and then calculate the required sample size. The sample size is chosen to ensure that the trial will have a high probability (adequate statistical power) of detecting a target difference between the treatments should one exist. The sample size has many implications for the conduct and interpretation of the study. Despite the critical role that the target difference has in the design of a RCT, the way in which it is determined has received little attention. In this article, we summarise the key considerations and messages from new guidance for researchers and funders on specifying the target difference, and undertaking and reporting a RCT sample size calculation. This article on choosing the target difference for a randomised controlled trial (RCT) and undertaking and reporting the sample size calculation has been dual published in the BMJ and BMC Trials journals Methods The DELTA2 (Difference ELicitation in TriAls) project comprised five major components: systematic literature reviews of recent methodological developments (stage 1) and existing funder guidance (stage 2); a Delphi study (stage 3); a two-day consensus meeting bringing together researchers, funders and patient representatives (stage 4); and the preparation and dissemination of a guidance document (stage 5). Results and Discussion The key messages from the DELTA2 guidance on determining the target difference and sample size calculation for a randomised caontrolled trial are presented. Recommendations for the subsequent reporting of the sample size calculation are also provided.
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Affiliation(s)
- Jonathan A Cook
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Nuffield Orthopaedic Centre, Windmill Rd, Oxford, OX3 7LD, UK.
| | - Steven A Julious
- Medical Statistics Group, ScHARR, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | - William Sones
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Nuffield Orthopaedic Centre, Windmill Rd, Oxford, OX3 7LD, UK
| | - Lisa V Hampson
- Statistical Methodology and Consulting, Novartis, Basel, Switzerland.,Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, UK
| | - Catherine Hewitt
- Department of Health Sciences, Seebohm Rowntree Building, University of York, Heslington, York, YO10 5DD, UK
| | - Jesse A Berlin
- Johnson & Johnson, 1125 Trenton-Harbourton Road, Titusville, NJ, 08933, USA
| | - Deborah Ashby
- Imperial Clinical Trials Unit, School of Public Health, Imperial College London, Stadium House, 68 Wood Lane, London, W12 7RH, UK
| | - Richard Emsley
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Dean A Fergusson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Stephen J Walters
- Medical Statistics Group, ScHARR, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Edward C F Wilson
- Cambridge Centre for Health Services Research & Cambridge Clinical Trials Unit, University of Cambridge, Institute of Public Health, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK
| | - Graeme Maclennan
- The Centre for Healthcare Randomised Trials (CHaRT), Health Sciences Building, University of Aberdeen, Foresterhill, Aberdeen, AB25 2D, UK
| | - Nigel Stallard
- Warwick Medical School - Statistics and Epidemiology, University of Warwick, Coventry, CV4 7AL, UK
| | - Joanne C Rothwell
- Medical Statistics Group, ScHARR, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Martin Bland
- Department of Health Sciences, Seebohm Rowntree Building, University of York, Heslington, York, YO10 5DD, UK
| | - Louise Brown
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, 2nd Floor 90 High Holborn, London, WC1V 6LJ, UK
| | - Craig R Ramsay
- Health Services Research Unit, University of Aberdeen, Health Sciences Building Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Andrew Cook
- Public Health Medicine and Fellow in Health Technology Assessment, Wessex Institute, University of Southampton, Alpha House, Enterprise Road, Southampton, SO16 7NS, UK
| | - David Armstrong
- School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, Kings College London, Addison House, Guy's Campus, London, SE1 1UL, UK
| | - Doug Altman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Nuffield Orthopaedic Centre, Windmill Rd, Oxford, OX3 7LD, UK
| | - Luke D Vale
- Health Economics Group, Institute of Health & Society, Newcastle University, Newcastle upon Tyne, NE2 4AX, UK
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15
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Cook JA, Julious SA, Sones W, Hampson LV, Hewitt C, Berlin JA, Ashby D, Emsley R, Fergusson DA, Walters SJ, Wilson ECF, MacLennan G, Stallard N, Rothwell JC, Bland M, Brown L, Ramsay CR, Cook A, Armstrong D, Altman D, Vale LD. DELTA 2 guidance on choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial. BMJ 2018; 363:k3750. [PMID: 30560792 PMCID: PMC6216070 DOI: 10.1136/bmj.k3750] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/09/2018] [Indexed: 11/17/2022]
Affiliation(s)
- Jonathan A Cook
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Nuffield Orthopaedic Centre, Oxford OX3 7LD, UK
| | - Steven A Julious
- Medical Statistics Group, ScHARR, University of Sheffield, Sheffield, UK
| | - William Sones
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Nuffield Orthopaedic Centre, Oxford OX3 7LD, UK
| | - Lisa V Hampson
- Statistical Methodology and Consulting, Novartis, Basel, Switzerland
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Catherine Hewitt
- Department of Health Sciences, University of York, Heslington, York, UK
| | | | - Deborah Ashby
- Imperial Clinical Trials Unit, School of Public Health, Imperial College London, London, UK
| | - Richard Emsley
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Dean A Fergusson
- Clinical Epidemiology Programme, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Stephen J Walters
- Medical Statistics Group, ScHARR, University of Sheffield, Sheffield, UK
| | - Edward C F Wilson
- Cambridge Centre for Health Services Research and Cambridge Clinical Trials Unit, University of Cambridge, Institute of Public Health, Cambridge, UK
| | - Graeme MacLennan
- Centre for Healthcare Randomised Trials (CHaRT), University of Aberdeen, Aberdeen, UK
| | - Nigel Stallard
- Warwick Medical School-Statistics and Epidemiology, University of Warwick, Coventry, UK
| | - Joanne C Rothwell
- Medical Statistics Group, ScHARR, University of Sheffield, Sheffield, UK
| | - Martin Bland
- Department of Health Sciences, University of York, Heslington, York, UK
| | - Louise Brown
- MRC Clinical Trials Unit at University College London, Institute of Clinical Trials and Methodology, London, UK
| | - Craig R Ramsay
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK
| | - Andrew Cook
- Wessex Institute, University of Southampton, Southampton, UK
| | - David Armstrong
- School of Population Health and Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Doug Altman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Nuffield Orthopaedic Centre, Oxford OX3 7LD, UK
| | - Luke D Vale
- Health Economics Group, Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK
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16
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Cook JA, Julious SA, Sones W, Hampson LV, Hewitt C, Berlin JA, Ashby D, Emsley R, Fergusson DA, Walters SJ, Wilson ECF, Maclennan G, Stallard N, Rothwell JC, Bland M, Brown L, Ramsay CR, Cook A, Armstrong D, Altman D, Vale LD. DELTA 2 guidance on choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial. Trials 2018. [PMID: 30400926 DOI: 10.1186/s13063‐018‐2884‐0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A key step in the design of a RCT is the estimation of the number of participants needed in the study. The most common approach is to specify a target difference between the treatments for the primary outcome and then calculate the required sample size. The sample size is chosen to ensure that the trial will have a high probability (adequate statistical power) of detecting a target difference between the treatments should one exist. The sample size has many implications for the conduct and interpretation of the study. Despite the critical role that the target difference has in the design of a RCT, the way in which it is determined has received little attention. In this article, we summarise the key considerations and messages from new guidance for researchers and funders on specifying the target difference, and undertaking and reporting a RCT sample size calculation. This article on choosing the target difference for a randomised controlled trial (RCT) and undertaking and reporting the sample size calculation has been dual published in the BMJ and BMC Trials journals METHODS: The DELTA2 (Difference ELicitation in TriAls) project comprised five major components: systematic literature reviews of recent methodological developments (stage 1) and existing funder guidance (stage 2); a Delphi study (stage 3); a two-day consensus meeting bringing together researchers, funders and patient representatives (stage 4); and the preparation and dissemination of a guidance document (stage 5). RESULTS AND DISCUSSION The key messages from the DELTA2 guidance on determining the target difference and sample size calculation for a randomised caontrolled trial are presented. Recommendations for the subsequent reporting of the sample size calculation are also provided.
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Affiliation(s)
- Jonathan A Cook
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Nuffield Orthopaedic Centre, Windmill Rd, Oxford, OX3 7LD, UK.
| | - Steven A Julious
- Medical Statistics Group, ScHARR, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | - William Sones
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Nuffield Orthopaedic Centre, Windmill Rd, Oxford, OX3 7LD, UK
| | - Lisa V Hampson
- Statistical Methodology and Consulting, Novartis, Basel, Switzerland.,Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, UK
| | - Catherine Hewitt
- Department of Health Sciences, Seebohm Rowntree Building, University of York, Heslington, York, YO10 5DD, UK
| | - Jesse A Berlin
- Johnson & Johnson, 1125 Trenton-Harbourton Road, Titusville, NJ, 08933, USA
| | - Deborah Ashby
- Imperial Clinical Trials Unit, School of Public Health, Imperial College London, Stadium House, 68 Wood Lane, London, W12 7RH, UK
| | - Richard Emsley
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Dean A Fergusson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Stephen J Walters
- Medical Statistics Group, ScHARR, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Edward C F Wilson
- Cambridge Centre for Health Services Research & Cambridge Clinical Trials Unit, University of Cambridge, Institute of Public Health, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK
| | - Graeme Maclennan
- The Centre for Healthcare Randomised Trials (CHaRT), Health Sciences Building, University of Aberdeen, Foresterhill, Aberdeen, AB25 2D, UK
| | - Nigel Stallard
- Warwick Medical School - Statistics and Epidemiology, University of Warwick, Coventry, CV4 7AL, UK
| | - Joanne C Rothwell
- Medical Statistics Group, ScHARR, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Martin Bland
- Department of Health Sciences, Seebohm Rowntree Building, University of York, Heslington, York, YO10 5DD, UK
| | - Louise Brown
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, 2nd Floor 90 High Holborn, London, WC1V 6LJ, UK
| | - Craig R Ramsay
- Health Services Research Unit, University of Aberdeen, Health Sciences Building Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Andrew Cook
- Public Health Medicine and Fellow in Health Technology Assessment, Wessex Institute, University of Southampton, Alpha House, Enterprise Road, Southampton, SO16 7NS, UK
| | - David Armstrong
- School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, Kings College London, Addison House, Guy's Campus, London, SE1 1UL, UK
| | - Doug Altman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Nuffield Orthopaedic Centre, Windmill Rd, Oxford, OX3 7LD, UK
| | - Luke D Vale
- Health Economics Group, Institute of Health & Society, Newcastle University, Newcastle upon Tyne, NE2 4AX, UK
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17
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Friede T, Posch M, Zohar S, Alberti C, Benda N, Comets E, Day S, Dmitrienko A, Graf A, Günhan BK, Hee SW, Lentz F, Madan J, Miller F, Ondra T, Pearce M, Röver C, Toumazi A, Unkel S, Ursino M, Wassmer G, Stallard N. Recent advances in methodology for clinical trials in small populations: the InSPiRe project. Orphanet J Rare Dis 2018; 13:186. [PMID: 30359266 PMCID: PMC6203217 DOI: 10.1186/s13023-018-0919-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 09/24/2018] [Indexed: 12/16/2022] Open
Abstract
Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017.The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods.
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Affiliation(s)
| | - Martin Posch
- Section of Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | - Sarah Zohar
- INSERM, U1138, team 22, Centre de Recherche des Cordeliers, Université Paris 5, Université Paris 6, Paris, France
| | - Corinne Alberti
- INSERM, Hôpital Robert-Debré, APHP, University Paris 7, Paris, France
| | | | - Emmanuelle Comets
- INSERM, IAME, UMR 1137, Univ Paris Diderot, Sorbonne Paris Cité, Paris, France.,INSERM, CIC 1414, Univ Rennes-1, Rennes, France
| | - Simon Day
- Clinical Trials Consulting and Training Limited, Buckingham, UK
| | | | - Alexandra Graf
- Section of Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | | | - Siew Wan Hee
- Warwick Medical School, University of Warwick, Coventry, UK
| | | | - Jason Madan
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Frank Miller
- Department of Statistics, Stockholm University, Stockholm, Sweden
| | - Thomas Ondra
- Section of Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | | | | | - Artemis Toumazi
- INSERM, Hôpital Robert-Debré, APHP, University Paris 7, Paris, France
| | | | - Moreno Ursino
- INSERM, U1138, team 22, Centre de Recherche des Cordeliers, Université Paris 5, Université Paris 6, Paris, France
| | - Gernot Wassmer
- Section of Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | - Nigel Stallard
- Warwick Medical School, University of Warwick, Coventry, UK.
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18
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Ping Z, Xiaomu W, Xufang X, Liang S. Vinpocetine regulates levels of circulating TLRs in Parkinson's disease patients. Neurol Sci 2018; 40:113-120. [PMID: 30315378 DOI: 10.1007/s10072-018-3592-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 09/26/2018] [Indexed: 12/30/2022]
Abstract
BACKGROUND The pathogenesis of Parkinson's disease (PD) is complex; it includes mitochondrial dysfunction, oxidative stress, and neuroinflammation. Notably, Toll-like receptors (TLRs) may activate inflammatory or anti-inflammatory responses in Parkinson's disease. Vinpocetine has been tested as an anti-inflammatory in both animal and in vitro research. Thus, it is important to test whether the anti-inflammatory properties of vinpocetine may have a protective effect in PD patients. METHODS Eighty-nine Parkinson's disease patients and 42 healthy controls were recruited for this study. All patients were randomly assigned to either the traditional therapy group (T PD group, n = 46) or the vinpocetine group (V PD group, n = 43), in a blinded manner. Both treatments were administered for 14 days. RESULTS Administration of vinpocetine reduced mRNA levels of TLR2/4, as well as protein levels of the downstream signalling molecules, MyD88 and NF-κB; moreover, it lowered the expression levels of serum inflammatory cytokines, TNF-α and MCP-1. Notably, vinpocetine increased TLR3 mRNA levels, as well as protein levels of the downstream signalling molecules TRIF-β and IRF-3, and serum levels of the anti-inflammatory cytokines IL-10 and IL-8. Furthermore, vinpocetine produced a robust increase in the Mini Mental State Examination score, compared to that achieved by using levodopa therapy. CONCLUSION Vinpocetine treatment may exhibit anti-inflammatory activity and alleviate cognitive impairment.
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Affiliation(s)
- Zhang Ping
- Department of Neurology, Jiangxi Provincial People's Hospital, No, 92 Aiguo Road, Donghu District, Nanchang, 330006, Jiangxi, China.
| | - Wu Xiaomu
- Department of Neurology, Jiangxi Provincial People's Hospital, No, 92 Aiguo Road, Donghu District, Nanchang, 330006, Jiangxi, China
| | - Xie Xufang
- Department of Neurology, Jiangxi Provincial People's Hospital, No, 92 Aiguo Road, Donghu District, Nanchang, 330006, Jiangxi, China
| | - Shao Liang
- Department of Cardiology, Jiangxi Provincial People's Hospital, No, 92 Aiguo Road, Donghu District, Nanchang, 330006, Jiangxi, China.
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19
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Griffin LR, Thamm DH, Selmic LE, Ehrhart E, Randall E. Pilot study utilizing Fluorine-18 fluorodeoxyglucose-positron emission tomography/computed tomography for glycolytic phenotyping of canine mast cell tumors. Vet Radiol Ultrasound 2018; 59:461-468. [DOI: 10.1111/vru.12612] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 10/10/2017] [Accepted: 10/19/2017] [Indexed: 10/17/2022] Open
Affiliation(s)
- Lynn R. Griffin
- Department of Environmental and Radiological Health Sciences (ERHS); Colorado State University; Fort Collins CO 80523
| | - Doug H. Thamm
- Flint Animal Cancer Center (FACC); Colorado State University; Fort Collins CO 80523
| | - Laura E. Selmic
- Department of Veterinary Clinical Medicine; University of Illinois; Urbana IL 61802
| | - E.J. Ehrhart
- Flint Animal Cancer Center (FACC); Colorado State University; Fort Collins CO 80523
| | - Elissa Randall
- Department of Environmental and Radiological Health Sciences (ERHS); Colorado State University; Fort Collins CO 80523
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20
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Jobjörnsson S, Christensen S. Anscombe’s model for sequential clinical trials revisited. Seq Anal 2018. [DOI: 10.1080/07474946.2018.1427982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Sebastian Jobjörnsson
- Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
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21
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Pearce M, Hee SW, Madan J, Posch M, Day S, Miller F, Zohar S, Stallard N. Value of information methods to design a clinical trial in a small population to optimise a health economic utility function. BMC Med Res Methodol 2018; 18:20. [PMID: 29422021 PMCID: PMC5806391 DOI: 10.1186/s12874-018-0475-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 01/14/2018] [Indexed: 01/20/2023] Open
Abstract
Background Most confirmatory randomised controlled clinical trials (RCTs) are designed with specified power, usually 80% or 90%, for a hypothesis test conducted at a given significance level, usually 2.5% for a one-sided test. Approval of the experimental treatment by regulatory agencies is then based on the result of such a significance test with other information to balance the risk of adverse events against the benefit of the treatment to future patients. In the setting of a rare disease, recruiting sufficient patients to achieve conventional error rates for clinically reasonable effect sizes may be infeasible, suggesting that the decision-making process should reflect the size of the target population. Methods We considered the use of a decision-theoretic value of information (VOI) method to obtain the optimal sample size and significance level for confirmatory RCTs in a range of settings. We assume the decision maker represents society. For simplicity we assume the primary endpoint to be normally distributed with unknown mean following some normal prior distribution representing information on the anticipated effectiveness of the therapy available before the trial. The method is illustrated by an application in an RCT in haemophilia A. We explicitly specify the utility in terms of improvement in primary outcome and compare this with the costs of treating patients, both financial and in terms of potential harm, during the trial and in the future. Results The optimal sample size for the clinical trial decreases as the size of the population decreases. For non-zero cost of treating future patients, either monetary or in terms of potential harmful effects, stronger evidence is required for approval as the population size increases, though this is not the case if the costs of treating future patients are ignored. Conclusions Decision-theoretic VOI methods offer a flexible approach with both type I error rate and power (or equivalently trial sample size) depending on the size of the future population for whom the treatment under investigation is intended. This might be particularly suitable for small populations when there is considerable information about the patient population. Electronic supplementary material The online version of this article (10.1186/s12874-018-0475-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Siew Wan Hee
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Jason Madan
- Warwick Clinical Trials Unit, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Martin Posch
- Section of Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | - Simon Day
- Clinical Trials Consulting and Training Limited, Buckingham, UK
| | - Frank Miller
- Department of Statistics, Stockholm University, Stockholm, Sweden
| | - Sarah Zohar
- INSERM, U1138, team 22, Centre de Recherche des Cordeliers, Université Paris 5, Université Paris 6, Paris, France
| | - Nigel Stallard
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.
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22
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Miller F, Zohar S, Stallard N, Madan J, Posch M, Hee SW, Pearce M, Vågerö M, Day S. Approaches to sample size calculation for clinical trials in rare diseases. Pharm Stat 2018; 17:214-230. [PMID: 29322632 DOI: 10.1002/pst.1848] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 09/05/2017] [Accepted: 12/08/2017] [Indexed: 01/27/2023]
Abstract
We discuss 3 alternative approaches to sample size calculation: traditional sample size calculation based on power to show a statistically significant effect, sample size calculation based on assurance, and sample size based on a decision-theoretic approach. These approaches are compared head-to-head for clinical trial situations in rare diseases. Specifically, we consider 3 case studies of rare diseases (Lyell disease, adult-onset Still disease, and cystic fibrosis) with the aim to plan the sample size for an upcoming clinical trial. We outline in detail the reasonable choice of parameters for these approaches for each of the 3 case studies and calculate sample sizes. We stress that the influence of the input parameters needs to be investigated in all approaches and recommend investigating different sample size approaches before deciding finally on the trial size. Highly influencing for the sample size are choice of treatment effect parameter in all approaches and the parameter for the additional cost of the new treatment in the decision-theoretic approach. These should therefore be discussed extensively.
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Affiliation(s)
- Frank Miller
- Department of Statistics, Stockholm University, Stockholm, Sweden
| | - Sarah Zohar
- INSERM, U1138, Team 22, Centre de Recherche des Cordeliers, Université Paris 5, Université Paris 6, Paris, France
| | - Nigel Stallard
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Jason Madan
- Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK
| | - Martin Posch
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Siew Wan Hee
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | | | | | - Simon Day
- Clinical Trials Consulting and Training Limited, Buckingham, UK
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23
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Miller F, Burman CF. A decision theoretical modeling for Phase III investments and drug licensing. J Biopharm Stat 2017; 28:698-721. [PMID: 28920757 DOI: 10.1080/10543406.2017.1377729] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
For a new candidate drug to become an approved medicine, several decision points have to be passed. In this article, we focus on two of them: First, based on Phase II data, the commercial sponsor decides to invest (or not) in Phase III. Second, based on the outcome of Phase III, the regulator determines whether the drug should be granted market access. Assuming a population of candidate drugs with a distribution of true efficacy, we optimize the two stakeholders' decisions and study the interdependence between them. The regulator is assumed to seek to optimize the total public health benefit resulting from the efficacy of the drug and a safety penalty. In optimizing the regulatory rules, in terms of minimal required sample size and the Type I error in Phase III, we have to consider how these rules will modify the commercial optimization made by the sponsor. The results indicate that different Type I errors should be used depending on the rarity of the disease.
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Affiliation(s)
- Frank Miller
- a Department of Statistics , Stockholm University , Stockholm , Sweden
| | - Carl-Fredrik Burman
- b Biometrics & Information Science , AstraZeneca R&D , Mölndal , Sweden.,c Department of Mathematical Sciences , Chalmers University of Technology and Göteborg University , Gothenburg , Sweden
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24
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Cook JA, Julious SA, Sones W, Rothwell JC, Ramsay CR, Hampson LV, Emsley R, Walters SJ, Hewitt C, Bland M, Fergusson DA, Berlin JA, Altman D, Vale LD. Choosing the target difference ('effect size') for a randomised controlled trial - DELTA 2 guidance protocol. Trials 2017; 18:271. [PMID: 28606102 PMCID: PMC5469157 DOI: 10.1186/s13063-017-1969-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Accepted: 05/04/2017] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND A key step in the design of a randomised controlled trial (RCT) is the estimation of the number of participants needed. By far the most common approach is to specify a target difference and then estimate the corresponding sample size; this sample size is chosen to provide reassurance that the trial will have high statistical power to detect such a difference between the randomised groups (at the planned statistical significance level). The sample size has many implications for the conduct of the study, as well as carrying scientific and ethical aspects to its choice. Despite the critical role of the target difference for the primary outcome in the design of an RCT, the manner in which it is determined has received little attention. This article reports the protocol of the Difference ELicitation in TriAls (DELTA2) project, which will produce guidance on the specification and reporting of the target difference for the primary outcome in a sample size calculation for RCTs. METHODS/DESIGN The DELTA2 project has five components: systematic literature reviews of recent methodological developments (stage 1) and existing funder guidance (stage 2); a Delphi study (stage 3); a 2-day consensus meeting bringing together researchers, funders and patient representatives, as well as one-off engagement sessions at relevant stakeholder meetings (stage 4); and the preparation and dissemination of a guidance document (stage 5). DISCUSSION Specification of the target difference for the primary outcome is a key component of the design of an RCT. There is a need for better guidance for researchers and funders regarding specification and reporting of this aspect of trial design. The aim of this project is to produce consensus based guidance for researchers and funders.
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Affiliation(s)
- Jonathan A. Cook
- Centre for Statistics in Medicine, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Nuffield Orthopaedic Centre, Windmill Road, Oxford, OX3 7LD UK
| | - Steven A. Julious
- Medical Statistics Group, School of Health and Related Research (ScHARR), The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA UK
| | - William Sones
- Centre for Statistics in Medicine, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Nuffield Orthopaedic Centre, Windmill Road, Oxford, OX3 7LD UK
| | - Joanne C. Rothwell
- Medical Statistics Group, School of Health and Related Research (ScHARR), The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA UK
| | - Craig R. Ramsay
- Health Services Research Unit, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen, AB25 2ZD UK
| | - Lisa V. Hampson
- Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF UK
- Statistical Innovation Group, Advanced Analytics Centre, AstraZeneca, Riverside Building, Granta Park, Cambridge, CB21 6GH UK
| | - Richard Emsley
- Centre for Biostatistics, School of Health Sciences, The University of Manchester, Jean McFarlane Building, Oxford Road, Manchester, M13 9PL UK
| | - Stephen J. Walters
- Medical Statistics Group, School of Health and Related Research (ScHARR), The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA UK
| | - Catherine Hewitt
- Department of Health Sciences, University of York, Heslington, Seebohm Rowntree Building, York, YO10 5DD UK
| | - Martin Bland
- Department of Health Sciences, University of York, Heslington, Seebohm Rowntree Building, York, YO10 5DD UK
| | - Dean A. Fergusson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa Hospital, 501 Smyth Road, Ottawa, ON K1H 8L6 Canada
| | - Jesse A. Berlin
- Johnson & Johnson, 1125 Trenton-Harbourton Road, MS TE3-15, PO Box 200, Titusville, NJ 08560 USA
| | - Doug Altman
- Centre for Statistics in Medicine, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Nuffield Orthopaedic Centre, Windmill Road, Oxford, OX3 7LD UK
| | - Luke D. Vale
- Institute of Health and Society, Newcastle University, The Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX UK
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