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Alyami RA, Simpson R, Oliver P, Julious SA. Evaluation of the impact of letters to GP practices to promote asthma prescription uptake in school-age children during summer (TRAINS study): a pragmatic cluster-randomised controlled trial. Lancet 2023; 402 Suppl 1:S22. [PMID: 37997062 DOI: 10.1016/s0140-6736(23)02089-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 09/07/2023] [Accepted: 09/22/2023] [Indexed: 11/25/2023]
Abstract
BACKGROUND Asthma exacerbations peak in school-aged children after the return to school in September. Previous studies have shown a decline in collections of asthma prescriptions during August. The PLEASANT trial demonstrated that sending a reminder letter to parents increased prescription uptake; reduced unscheduled care, and was cost saving to the health service. We aimed to assess whether informing general practitioner (GP) practices about the PLEASANT trial and its results could lead to its implementation in routine practice. METHODS The trial to assess implementation of new research in a primary care setting (TRAINS) was a pragmatic cluster-randomised (1:1) trial conducted in England involving GP practices contributing to the Clinical Practice Research Datalink (CPRD). The intervention was a letter informing the GP practice of the PLEASANT trial results with recommendations for implementation. GP practices in the control group continued with usual care without receiving any letters about PLEASANT trial. The intervention was distributed via CPRD by both mail and email in June 2021. The trial received both University of Sheffield Ethics approval and Independent Scientific Advisory Committee (ISAC) approval. The primary outcome was the proportion of children with asthma (aged 4-15 years) who had a prescription for a preventer between Aug 1 and Sept 30, 2021. This trial is registered with ClinicalTrials.gov, NCT05226091. FINDINGS A total of 1326 GP practices, including 90 583 children with asthma, were included in the study. These practices were randomly allocated to the intervention group (664 practices, 44 708 children) or the control group (662 practices, 45 875 children). In assessing the impact of the intervention on the proportion of children collecting a preventer prescription, 15 716 (35·3%) of 44 708 children from the intervention group and 16 001 (35·1%) of 45 559 children from the control group picked up a prescription. There was no statistically significant difference observed (odds ratio [OR] 1·01, 95% CI 0·97-1·05), indicating that the intervention had no effect. INTERPRETATION The study findings suggest that passive intervention of providing a letter to GPs did not achieve the intended outcomes. To bridge the gap between evidence and practice, alternative, more proactive strategies could be explored to address the identified issues. FUNDING Jazan University.
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Affiliation(s)
- Rami A Alyami
- School of Medicine and Population Health, University of Sheffield, Sheffield, UK; Respiratory Therapy Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia.
| | - Rebecca Simpson
- School of Medicine and Population Health, University of Sheffield, Sheffield, UK
| | - Phillip Oliver
- School of Medicine and Population Health, University of Sheffield, Sheffield, UK; Academic Unit of Primary Medical Care, Samuel Fox House Northern General Hospital, Sheffield, UK
| | - Steven A Julious
- School of Medicine and Population Health, University of Sheffield, Sheffield, UK
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Totton N, Julious SA, Coates E, Hughes DA, Cook JA, Biggs K, Hewitt C, Day S, Cook A. Appropriate design and reporting of superiority, equivalence and non-inferiority clinical trials incorporating a benefit-risk assessment: the BRAINS study including expert workshop. Health Technol Assess 2023; 27:1-58. [PMID: 37982521 PMCID: PMC11017151 DOI: 10.3310/bhqz7691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023] Open
Abstract
Background Randomised controlled trials are designed to assess the superiority, equivalence or non-inferiority of a new health technology, but which trial design should be used is not always obvious in practice. In particular, when using equivalence or non-inferiority designs, multiple outcomes of interest may be important for the success of a trial, despite the fact that usually only a single primary outcome is used to design the trial. Benefit-risk methods are used in the regulatory clinical trial setting to assess multiple outcomes and consider the trade-off of the benefits against the risks, but are not regularly implemented in publicly funded trials. Objectives The aim of the project is to aid the design of clinical trials with multiple outcomes of interest by defining when each trial design is appropriate to use and identifying when to use benefit-risk methods to assess outcome trade-offs (qualitatively or quantitatively) in a publicly funded trial setting. Methods A range of methods was used to elicit expert opinion to answer the project objectives, including a web-based survey of relevant researchers, a rapid review of current literature and a 2-day consensus workshop of experts (in 2019). Results We created a list of 19 factors to aid researchers in selecting the most appropriate trial design, containing the following overarching sections: population, intervention, comparator, outcomes, feasibility and perspectives. Six key reasons that indicate a benefit-risk method should be considered within a trial were identified: (1) when the success of the trial depends on more than one outcome; (2) when important outcomes within the trial are in competing directions (i.e. a health technology is better for one outcome, but worse for another); (3) to allow patient preferences to be included and directly influence trial results; (4) to provide transparency on subjective recommendations from a trial; (5) to provide consistency in the approach to presenting results from a trial; and (6) to synthesise multiple outcomes into a single metric. Further information was provided to support the use of benefit-risk methods in appropriate circumstances, including the following: methods identified from the review were collated into different groupings and described to aid the selection of a method; potential implementation of methods throughout the trial process were provided and discussed (with examples); and general considerations were described for those using benefit-risk methods. Finally, a checklist of five pieces of information that should be present when reporting benefit-risk methods was defined, with two additional items specifically for reporting the results. Conclusions These recommendations will assist research teams in selecting which trial design to use and deciding whether or not a benefit-risk method could be included to ensure research questions are answered appropriately. Additional information is provided to support consistent use and clear reporting of benefit-risk methods in the future. The recommendations can also be used by funding committees to confirm that appropriate considerations of the trial design have been made. Limitations This research was limited in scope and should be considered in conjunction with other trial design methodologies to assess appropriateness. In addition, further research is needed to provide concrete information about which benefit-risk methods are best to use in publicly funded trials, along with recommendations that are specific to each method. Study registration The rapid review is registered as PROSPERO CRD42019144882. Funding Funded by the Medical Research Council UK and the National Institute for Health and Care Research as part of the Medical Research Council-National Institute for Health and Care Research Methodology Research programme.
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Affiliation(s)
- Nikki Totton
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Elizabeth Coates
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Dyfrig A Hughes
- Centre for Health Economics and Medicines Evaluation, Bangor University, Bangor, UK
| | - Jonathan A Cook
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Katie Biggs
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Catherine Hewitt
- York Trials Unit, Department of Health Sciences, University of York, York, UK
| | - Simon Day
- Clinical Trials Consulting & Training Limited, Buckingham, UK
| | - Andrew Cook
- Wessex Institute, University of Southampton, Southampton, UK
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Junqueira DR, Zorzela L, Golder S, Loke Y, Gagnier JJ, Julious SA, Li T, Mayo-Wilson E, Pham B, Phillips R, Santaguida P, Scherer RW, Gøtzsche PC, Moher D, Ioannidis JPA, Vohra S. CONSORT Harms 2022 statement, explanation, and elaboration: updated guideline for the reporting of harms in randomized trials. J Clin Epidemiol 2023; 158:149-165. [PMID: 37100738 DOI: 10.1016/j.jclinepi.2023.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/21/2023] [Indexed: 04/28/2023]
Abstract
Randomized controlled trials remain the reference standard for healthcare research on effects of interventions, and the need to report both benefits and harms is essential. The Consolidated Standards of Reporting Trials (the main CONSORT) statement includes one item on reporting harms (i.e., all important harms or unintended effects in each group). In 2004, the CONSORT group developed the CONSORT Harms extension; however, it has not been consistently applied and needs to be updated. Here, we describe CONSORT Harms 2022, which replaces the CONSORT Harms 2004 checklist, and shows how CONSORT Harms 2022 items could be incorporated into the main CONSORT checklist. Thirteen items from the main CONSORT were modified to improve harms reporting. Three new items were added. In this article, we describe CONSORT Harms 2022 and how it was integrated into the main CONSORT checklist and elaborate on each item relevant to complete reporting of harms in randomized controlled trials. Until future work from the CONSORT group produces an updated checklist, authors, journal reviewers, and editors of randomized controlled trials should use the integrated checklist presented in this paper.
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Affiliation(s)
- Daniela R Junqueira
- Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Liliane Zorzela
- Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Susan Golder
- Department of Health Sciences, University of York, York, UK
| | - Yoon Loke
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Joel J Gagnier
- Department of Epidemiology and Biostatistics, Department of Surgery, Western University, London, Ontario, Canada
| | - Steven A Julious
- Design, Trials and Statistics, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Tianjing Li
- Department of Ophthalmology, School of Medicine, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Evan Mayo-Wilson
- Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC, USA
| | - Ba Pham
- Knowledge Translation Programme, Unity Health Toronto, Toronto, Ontario, Canada
| | - Rachel Phillips
- Faculty of Medicine, School of Public Health, Imperial College London, London, UK
| | - Pasqualina Santaguida
- Department of Health Research Methods, Evidence and Impact (HEI), McMaster University, Hamilton, Ontario, Canada
| | | | | | - David Moher
- Centre for Journalology, Clinical Epidemiology Programme, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - John P A Ioannidis
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, CA, USA
| | - Sunita Vohra
- Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada.
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Junqueira DR, Zorzela L, Golder S, Loke Y, Gagnier JJ, Julious SA, Li T, Mayo-Wilson E, Pham B, Phillips R, Santaguida P, Scherer RW, Gøtzsche PC, Moher D, Ioannidis JPA, Vohra S. CONSORT Harms 2022 statement, explanation, and elaboration: updated guideline for the reporting of harms in randomised trials. BMJ 2023; 381:e073725. [PMID: 37094878 DOI: 10.1136/bmj-2022-073725] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Affiliation(s)
- Daniela R Junqueira
- Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Liliane Zorzela
- Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Susan Golder
- Department of Health Sciences, University of York, York, UK
| | - Yoon Loke
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Joel J Gagnier
- Department of Epidemiology and Biostatistics, Department of Surgery, Western University, London, ON, Canada
| | - Steven A Julious
- Design, Trials and Statistics, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Tianjing Li
- Department of Ophthalmology, School of Medicine, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Evan Mayo-Wilson
- Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC, USA
| | - Ba Pham
- Knowledge Translation Programme, Unity Health Toronto, Toronto, ON, Canada
| | - Rachel Phillips
- Faculty of Medicine, School of Public Health, Imperial College London, London, UK
| | - Pasqualina Santaguida
- Department of Health Research Methods, Evidence and Impact (HEI), McMaster University, Hamilton, ON, Canada
| | | | | | - David Moher
- Centre for Journalology, Clinical Epidemiology Programme, Ottawa Hospital Research Institute, Ottawa, ON, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - John P A Ioannidis
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, CA, USA
| | - Sunita Vohra
- Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
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Edwards JM, Walters SJ, Julious SA. A retrospective analysis of conditional power assumptions in clinical trials with continuous or binary endpoints. Trials 2023; 24:215. [PMID: 36949524 PMCID: PMC10035140 DOI: 10.1186/s13063-023-07202-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 02/25/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND Adaptive clinical trials may use conditional power (CP) to make decisions at interim analyses, requiring assumptions about the treatment effect for remaining patients. It is critical that these assumptions are understood by those using CP in decision-making, as well as timings of these decisions. METHODS Data for 21 outcomes from 14 published clinical trials were made available for re-analysis. CP curves for accruing outcome information were calculated using and compared with a pre-specified objective criteria for original and transformed versions of the trial data using four future treatment effect assumptions: (i) observed current trend, (ii) hypothesised effect, (iii) 80% optimistic confidence limit, (iv) 90% optimistic confidence limit. RESULTS The hypothesised effect assumption met objective criteria when the true effect was close to that planned, but not when smaller than planned. The opposite was seen using the current trend assumption. Optimistic confidence limit assumptions appeared to offer a compromise between the two, performing well against objective criteria when the end observed effect was as planned or smaller. CONCLUSION The current trend assumption could be the preferable assumption when there is a wish to stop early for futility. Interim analyses could be undertaken as early as 30% of patients have data available. Optimistic confidence limit assumptions should be considered when using CP to make trial decisions, although later interim timings should be considered where logistically feasible.
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Affiliation(s)
- Julia M Edwards
- School of Health and Related Research, The University of Sheffield, 30 Regent Street, Sheffield, S1 4DA, UK.
- Intensive Care National Audit and Research Centre (ICNARC), 24 High Holborn, London, WC1V 6AZ, UK.
| | - Stephen J Walters
- School of Health and Related Research, The University of Sheffield, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Steven A Julious
- School of Health and Related Research, The University of Sheffield, 30 Regent Street, Sheffield, S1 4DA, UK
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Iflaifel M, Sprange K, Bell J, Cook A, Gamble C, Julious SA, Juszczak E, Linsell L, Montgomery A, Partlett C. Developing guidance for a risk-proportionate approach to blinding statisticians within clinical trials: a mixed methods study. Trials 2023; 24:71. [PMID: 36721215 PMCID: PMC9887916 DOI: 10.1186/s13063-022-06992-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 12/07/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Existing guidelines recommend statisticians remain blinded to treatment allocation prior to the final analysis and that any interim analyses should be conducted by a separate team from the one undertaking the final analysis. However, there remains substantial variation in practice between UK Clinical Trials Units (CTUs) when it comes to blinding statisticians. Therefore, the aim of this study was to develop guidance to advise CTUs on a risk-proportionate approach to blinding statisticians within clinical trials. METHODS This study employed a mixed methods approach involving three stages: (I) a quantitative study using a cohort of 200 studies (from a major UK funder published between 2016 and 2020) to assess the impact of blinding statisticians on the proportion of trials reporting a statistically significant finding for the primary outcome(s); (II) a qualitative study using focus groups to determine the perspectives of key stakeholders on the practice of blinding trial statisticians; and (III) combining the results of stages I and II, along with a stakeholder meeting, to develop guidance for UK CTUs. RESULTS After screening abstracts, 179 trials were included for review. The results of the primary analysis showed no evidence that involvement of an unblinded trial statistician was associated with the likelihood of statistically significant findings being reported, odds ratio (OR) 1.02 (95% confidence interval (CI) 0.49 to 2.13). Six focus groups were conducted, with 37 participants. The triangulation between stages I and II resulted in developing 40 provisional statements. These were rated independently by the stakeholder group prior to the meeting. Ten statements reached agreement with no agreement on 30 statements. At the meeting, various factors were identified that could influence the decision of blinding the statistician, including timing, study design, types of intervention and practicalities. Guidance including 21 recommendations/considerations was developed alongside a Risk Assessment Tool to provide CTUs with a framework for assessing the risks associated with blinding/not blinding statisticians and for identifying appropriate mitigation strategies. CONCLUSIONS This is the first study to develop a guidance document to enhance the understanding of blinding statisticians and to provide a framework for the decision-making process. The key finding was that the decision to blind statisticians should be based on the benefits and risks associated with a particular trial.
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Affiliation(s)
- Mais Iflaifel
- Nottingham Clinical Trials Unit, University of Nottingham, Nottingham, UK
| | - Kirsty Sprange
- Nottingham Clinical Trials Unit, University of Nottingham, Nottingham, UK
| | - Jennifer Bell
- National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Carrol Gamble
- Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK
| | - Steven A Julious
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Edmund Juszczak
- Nottingham Clinical Trials Unit, University of Nottingham, Nottingham, UK
| | - Louise Linsell
- National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Alan Montgomery
- Nottingham Clinical Trials Unit, University of Nottingham, Nottingham, UK
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Alyami RA, Simpson R, Oliver P, Julious SA. TRial to Assess Implementation of New research in a primary care Setting (TRAINS): study protocol for a pragmatic cluster randomised controlled trial of an educational intervention to promote asthma prescription uptake in general practitioner practices. Trials 2022; 23:947. [PMCID: PMC9670052 DOI: 10.1186/s13063-022-06864-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 10/25/2022] [Indexed: 11/19/2022] Open
Abstract
Background There is a marked increase in unscheduled care visits in school-aged children with asthma after returning to school in September. This is potentially associated with children not taking their asthma preventer medication during the school summer holidays. A cluster randomised controlled trial (PLEASANT) was undertaken with 1279 school-age children in 141 general practices (71 on intervention and 70 on control) in England and Wales. It found that a simple letter sent from the family doctor during the school holidays to a parent with a child with asthma, informing them of the importance of taking asthma preventer medication during the summer relatively increased prescriptions by 30% in August and reduced medical contacts in the period September to December. Also, it is estimated there was a cost-saving of £36.07 per patient over the year. We aim to conduct a randomised trial to assess if informing GP practices of an evidence-based intervention improves the implementation of that intervention. Methods/design The TRAINS study—TRial to Assess Implementation of New research in a primary care Setting—is a pragmatic cluster randomised implementation trial using routine data. A total of 1389 general practitioner (GP) practices in England will be included into the trial; 694 GP practices will be randomised to the intervention group and 695 control group of usual care. The Clinical Practice Research Datalink (CPRD) will send the intervention and obtain all data for the study, including prescription and primary care contacts data. The intervention will be sent in June 2021 by postal and email to the asthma lead and/or practice manager. The intervention is a letter to GPs informing them of the PLEASANT study findings with recommendations. It will come with an information leaflet about PLEASANT and a suggested reminder letter and SMS text template. Discussion The trial will assess if informing GP practices of the PLEASANT trial results will increase prescription uptake before the start of the school year. The hope is that the intervention will increase the implementation of PLEASANT work and then increase prescription uptake during the summer holiday prior to the start of school. Trial registration ClinicalTrials.gov ID: NCT05226091 Supplementary Information The online version contains supplementary material available at 10.1186/s13063-022-06864-y.
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Affiliation(s)
- Rami A. Alyami
- grid.11835.3e0000 0004 1936 9262School of Health and Related Research (ScHARR), University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA UK ,grid.411831.e0000 0004 0398 1027Respiratory Therapy Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Rebecca Simpson
- grid.11835.3e0000 0004 1936 9262School of Health and Related Research (ScHARR), University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA UK
| | - Phillip Oliver
- grid.11835.3e0000 0004 1936 9262School of Health and Related Research (ScHARR), University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA UK ,grid.412937.a0000 0004 0641 5987Academic Unit of Primary Medical Care, Samuel Fox House Northern General Hospital, Herries Road, Sheffield, S5 7AU UK
| | - Steven A. Julious
- grid.11835.3e0000 0004 1936 9262School of Health and Related Research (ScHARR), University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA UK
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Tesfaye S, Sloan G, Petrie J, White D, Bradburn M, Young T, Rajbhandari S, Sharma S, Rayman G, Gouni R, Alam U, Julious SA, Cooper C, Loban A, Sutherland K, Glover R, Waterhouse S, Turton E, Horspool M, Gandhi R, Maguire D, Jude E, Ahmed SH, Vas P, Hariman C, McDougall C, Devers M, Tsatlidis V, Johnson M, Bouhassira D, Bennett DL, Selvarajah D. Optimal pharmacotherapy pathway in adults with diabetic peripheral neuropathic pain: the OPTION-DM RCT. Health Technol Assess 2022; 26:1-100. [PMID: 36259684 PMCID: PMC9589396 DOI: 10.3310/rxuo6757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND The mainstay of treatment for diabetic peripheral neuropathic pain is pharmacotherapy, but the current National Institute for Health and Care Excellence guideline is not based on robust evidence, as the treatments and their combinations have not been directly compared. OBJECTIVES To determine the most clinically beneficial, cost-effective and tolerated treatment pathway for diabetic peripheral neuropathic pain. DESIGN A randomised crossover trial with health economic analysis. SETTING Twenty-one secondary care centres in the UK. PARTICIPANTS Adults with diabetic peripheral neuropathic pain with a 7-day average self-rated pain score of ≥ 4 points (Numeric Rating Scale 0-10). INTERVENTIONS Participants were randomised to three commonly used treatment pathways: (1) amitriptyline supplemented with pregabalin, (2) duloxetine supplemented with pregabalin and (3) pregabalin supplemented with amitriptyline. Participants and research teams were blinded to treatment allocation, using over-encapsulated capsules and matching placebos. Site pharmacists were unblinded. OUTCOMES The primary outcome was the difference in 7-day average 24-hour Numeric Rating Scale score between pathways, measured during the final week of each pathway. Secondary end points included 7-day average daily Numeric Rating Scale pain score at week 6 between monotherapies, quality of life (Short Form questionnaire-36 items), Hospital Anxiety and Depression Scale score, the proportion of patients achieving 30% and 50% pain reduction, Brief Pain Inventory - Modified Short Form items scores, Insomnia Severity Index score, Neuropathic Pain Symptom Inventory score, tolerability (scale 0-10), Patient Global Impression of Change score at week 16 and patients' preferred treatment pathway at week 50. Adverse events and serious adverse events were recorded. A within-trial cost-utility analysis was carried out to compare treatment pathways using incremental costs per quality-adjusted life-years from an NHS and social care perspective. RESULTS A total of 140 participants were randomised from 13 UK centres, 130 of whom were included in the analyses. Pain score at week 16 was similar between the arms, with a mean difference of -0.1 points (98.3% confidence interval -0.5 to 0.3 points) for duloxetine supplemented with pregabalin compared with amitriptyline supplemented with pregabalin, a mean difference of -0.1 points (98.3% confidence interval -0.5 to 0.3 points) for pregabalin supplemented with amitriptyline compared with amitriptyline supplemented with pregabalin and a mean difference of 0.0 points (98.3% confidence interval -0.4 to 0.4 points) for pregabalin supplemented with amitriptyline compared with duloxetine supplemented with pregabalin. Results for tolerability, discontinuation and quality of life were similar. The adverse events were predictable for each drug. Combination therapy (weeks 6-16) was associated with a further reduction in Numeric Rating Scale pain score (mean 1.0 points, 98.3% confidence interval 0.6 to 1.3 points) compared with those who remained on monotherapy (mean 0.2 points, 98.3% confidence interval -0.1 to 0.5 points). The pregabalin supplemented with amitriptyline pathway had the fewest monotherapy discontinuations due to treatment-emergent adverse events and was most commonly preferred (most commonly preferred by participants: amitriptyline supplemented with pregabalin, 24%; duloxetine supplemented with pregabalin, 33%; pregabalin supplemented with amitriptyline, 43%; p = 0.26). No single pathway was superior in cost-effectiveness. The incremental gains in quality-adjusted life-years were small for each pathway comparison [amitriptyline supplemented with pregabalin compared with duloxetine supplemented with pregabalin -0.002 (95% confidence interval -0.011 to 0.007) quality-adjusted life-years, amitriptyline supplemented with pregabalin compared with pregabalin supplemented with amitriptyline -0.006 (95% confidence interval -0.002 to 0.014) quality-adjusted life-years and duloxetine supplemented with pregabalin compared with pregabalin supplemented with amitriptyline 0.007 (95% confidence interval 0.0002 to 0.015) quality-adjusted life-years] and incremental costs over 16 weeks were similar [amitriptyline supplemented with pregabalin compared with duloxetine supplemented with pregabalin -£113 (95% confidence interval -£381 to £90), amitriptyline supplemented with pregabalin compared with pregabalin supplemented with amitriptyline £155 (95% confidence interval -£37 to £625) and duloxetine supplemented with pregabalin compared with pregabalin supplemented with amitriptyline £141 (95% confidence interval -£13 to £398)]. LIMITATIONS Although there was no placebo arm, there is strong evidence for the use of each study medication from randomised placebo-controlled trials. The addition of a placebo arm would have increased the duration of this already long and demanding trial and it was not felt to be ethically justifiable. FUTURE WORK Future research should explore (1) variations in diabetic peripheral neuropathic pain management at the practice level, (2) how OPTION-DM (Optimal Pathway for TreatIng neurOpathic paiN in Diabetes Mellitus) trial findings can be best implemented, (3) why some patients respond to a particular drug and others do not and (4) what options there are for further treatments for those patients on combination treatment with inadequate pain relief. CONCLUSIONS The three treatment pathways appear to give comparable patient outcomes at similar costs, suggesting that the optimal treatment may depend on patients' preference in terms of side effects. TRIAL REGISTRATION The trial is registered as ISRCTN17545443 and EudraCT 2016-003146-89. FUNDING This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme, and will be published in full in Health Technology Assessment; Vol. 26, No. 39. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Solomon Tesfaye
- Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
- Department of Oncology and Human Metabolism, Medical School, University of Sheffield, Sheffield, UK
| | - Gordon Sloan
- Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Jennifer Petrie
- Clinical Trials Research Unit, University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, UK
| | - David White
- Clinical Trials Research Unit, University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, UK
| | - Mike Bradburn
- Clinical Trials Research Unit, University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, UK
| | - Tracey Young
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | | | - Sanjeev Sharma
- East Suffolk and North Essex NHS Foundation Trust, Ipswich, UK
| | - Gerry Rayman
- East Suffolk and North Essex NHS Foundation Trust, Ipswich, UK
| | | | - Uazman Alam
- University of Liverpool, Liverpool, UK
- Liverpool University Hospital NHS Foundation Trust, Liverpool, UK
| | - Steven A Julious
- Medical Statistics Group, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Cindy Cooper
- Clinical Trials Research Unit, University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, UK
| | - Amanda Loban
- Clinical Trials Research Unit, University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, UK
| | - Katie Sutherland
- Clinical Trials Research Unit, University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, UK
| | - Rachel Glover
- Clinical Trials Research Unit, University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, UK
| | - Simon Waterhouse
- Clinical Trials Research Unit, University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, UK
| | - Emily Turton
- Clinical Trials Research Unit, University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, UK
| | | | - Rajiv Gandhi
- Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | | | - Edward Jude
- Tameside and Glossop Integrated Care NHS Foundation Trust, Ashton under Lyne, UK
- University of Manchester, Manchester, UK
| | - Syed Haris Ahmed
- University of Liverpool, Liverpool, UK
- Countess of Chester Hospital NHS Foundation Trust, Chester, UK
| | - Prashanth Vas
- King's College Hospital NHS Foundation Trust, London, UK
| | | | | | | | | | | | | | - David L Bennett
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Dinesh Selvarajah
- Department of Oncology and Human Metabolism, Medical School, University of Sheffield, Sheffield, UK
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9
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Flight L, Julious SA. Practical guide to sample size calculations: Installation of the app SampSize. Pharm Stat 2022; 21:1109-1110. [PMID: 35535737 DOI: 10.1002/pst.2215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 03/31/2022] [Accepted: 04/10/2022] [Indexed: 11/12/2022]
Abstract
In 2016 we published three articles in Pharmaceutical Statistics that gave a practical guide to sample size calculations. In each of the articles there were instructions on how to obtain the App SampSize. This short communication updates these instructions and highlights the updates and added functionality to the App.
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Affiliation(s)
- Laura Flight
- School of Health and Related Research (ScHARR), The University of Sheffield, Sheffield, UK
| | - Steven A Julious
- School of Health and Related Research (ScHARR), The University of Sheffield, Sheffield, UK
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10
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Rothwell JC, Julious SA, Cooper CL. Adjusting for bias in the mean for primary and secondary outcomes when trials are in sequence. Pharm Stat 2021; 21:460-475. [PMID: 34860471 DOI: 10.1002/pst.2180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 11/10/2021] [Accepted: 11/18/2021] [Indexed: 11/11/2022]
Abstract
When designing a clinical trial, one key aspect of the design is the sample size calculation. The sample size calculation tends to rely on a target or expected difference. The expected difference can be based on the observed data from previous studies, which results in bias. It has been reported that large treatment effects observed in trials are often not replicated in subsequent trials. If these values are used to design subsequent studies, the sample sizes may be biased which results in an unethical study. Regression to the mean (RTM) is one explanation for this. If only health technologies which meet a particular continuation criterion (such as p < 0.05 in the first study) are progressed to a second confirmatory trial, it is highly likely that the observed effect in the second trial will be lower than that observed in the first trial. It will be shown how when moving from one trial to the next, a truncated normal distribution is inherently imposed on the first study. This results in a lower observed effect size in the second trial. A simple adjustment method is proposed based on the mathematical properties of the truncated normal distribution. This adjustment method was confirmed using simulations in R and compared with other previous adjustments. The method can be applied to the observed effect in a trial, which is being used in the design of a second confirmatory trial, resulting in a more stable estimate for the 'true' treatment effect. The adjustment accounts for the bias in the primary and secondary endpoints in the first trial with the bias being affected by the power of that study. Tables of results have been provided to aid implementation, along with a worked example. In summary, there is a bias introduced when the point estimate from one trial is used to assist the design of a second trial. It is recommended that any observed point estimates be used with caution and the adjustment method developed in this article be implemented to significantly reduce this bias.
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Affiliation(s)
- Joanne C Rothwell
- Biostatistics, Parexel International, Sheffield, UK.,Design Trials and Statistics, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Steven A Julious
- Design Trials and Statistics, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Cindy L Cooper
- Sheffield Clinical Trials Unit, ScHARR, University of Sheffield, Sheffield, UK
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11
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Lewis J, Julious SA. Sample sizes for cluster-randomised trials with continuous outcomes: Accounting for uncertainty in a single intra-cluster correlation estimate. Stat Methods Med Res 2021; 30:2459-2470. [PMID: 34477455 PMCID: PMC8649444 DOI: 10.1177/09622802211037073] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Sample size calculations for cluster-randomised trials require inclusion of an
inflation factor taking into account the intra-cluster correlation coefficient.
Often, estimates of the intra-cluster correlation coefficient are taken from
pilot trials, which are known to have uncertainty about their estimation. Given
that the value of the intra-cluster correlation coefficient has a considerable
influence on the calculated sample size for a main trial, the uncertainty in the
estimate can have a large impact on the ultimate sample size and consequently,
the power of a main trial. As such, it is important to account for the
uncertainty in the estimate of the intra-cluster correlation coefficient. While
a commonly adopted approach is to utilise the upper confidence limit in the
sample size calculation, this is a largely inefficient method which can result
in overpowered main trials. In this paper, we present a method of estimating the
sample size for a main cluster-randomised trial with a continuous outcome, using
numerical methods to account for the uncertainty in the intra-cluster
correlation coefficient estimate. Despite limitations with this initial study,
the findings and recommendations in this paper can help to improve sample size
estimations for cluster randomised controlled trials by accounting for
uncertainty in the estimate of the intra-cluster correlation coefficient. We
recommend this approach be applied to all trials where there is uncertainty in
the intra-cluster correlation coefficient estimate, in conjunction with
additional sources of information to guide the estimation of the intra-cluster
correlation coefficient.
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Affiliation(s)
- Jen Lewis
- Design, Trials and Statistics, School of Health and Related Research (ScHARR), 7315University of Sheffield, UK
| | - Steven A Julious
- Design, Trials and Statistics, School of Health and Related Research (ScHARR), 7315University of Sheffield, UK
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12
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Abstract
Introduction Sample size calculations require assumptions regarding treatment response and variability. Incorrect assumptions can result in under- or overpowered trials, posing ethical concerns. Sample size re-estimation (SSR) methods investigate the validity of these assumptions and increase the sample size if necessary. The “promising zone” (Mehta and Pocock, Stat Med 30:3267–3284, 2011) concept is appealing to researchers for its design simplicity. However, it is still relatively new in the application and has been a source of controversy. Objectives This research aims to synthesise current approaches and practical implementation of the promising zone design. Methods This systematic review comprehensively identifies the reporting of methodological research and of clinical trials using promising zone. Databases were searched according to a pre-specified search strategy, and pearl growing techniques implemented. Results The combined search methods resulted in 270 unique records identified; 171 were included in the review, of which 30 were trials. The median time to the interim analysis was 60% of the original target sample size (IQR 41–73%). Of the 15 completed trials, 7 increased their sample size. Only 21 studies reported the maximum sample size that would be considered, for which the median increase was 50% (IQR 35–100%). Conclusions Promising zone is being implemented in a range of trials worldwide, albeit in low numbers. Identifying trials using promising zone was difficult due to the lack of reporting of SSR methodology. Even when SSR methodology was reported, some had key interim analysis details missing, and only eight papers provided promising zone ranges.
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Affiliation(s)
- Julia M Edwards
- School of Health and Related Research, The University of Sheffield, Sheffield, UK.
| | - Stephen J Walters
- School of Health and Related Research, The University of Sheffield, Sheffield, UK
| | - Cornelia Kunz
- Boehringer Ingelheim, Biberach an der Riss, Biberach, Germany
| | - Steven A Julious
- School of Health and Related Research, The University of Sheffield, Sheffield, UK
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13
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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14
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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: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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15
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Sones W, Julious SA, Rothwell JC, Ramsay CR, Hampson LV, Emsley R, Walters SJ, Hewitt C, Bland M, Fergusson DA, Berlin JA, Altman D, Vale LD, Cook JA. Correction to: Choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial - the development of the DELTA2 guidance. Trials 2019; 20:602. [PMID: 31651373 PMCID: PMC6813096 DOI: 10.1186/s13063-019-3809-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- 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
| | - Steven A Julious
- Medical Statistics Group, ScHARR, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Joanne C Rothwell
- Medical Statistics Group, ScHARR, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Craig Robert 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 Methodology and Consulting, Novartis Pharma AG, Basel, Switzerland
| | - 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
| | - Stephen J Walters
- Medical Statistics Group, ScHARR, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Catherine Hewitt
- Department of Health Sciences, Seebohm Rowntree Building, University of York, Heslington, York, YO10 5DD, UK
| | - Martin Bland
- Department of Health Sciences, Seebohm Rowntree Building, University of York, Heslington, York, YO10 5DD, UK
| | - Dean A Fergusson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, 501 Smyth Road, Box 201B, Ottawa, ON, K1H 8L6, Canada
| | - Jesse A Berlin
- Johnson & Johnson, One J&J Plaza, New Brunswick, NJ, 08933, USA
| | - 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 David Vale
- Health Economics Group, Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK
| | - Jonathan Alistair 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.
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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 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] [What about the content of this article? (0)] [Affiliation(s)] [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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17
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Herbert E, Julious SA, Goodacre S. Progression criteria in trials with an internal pilot: an audit of publicly funded randomised controlled trials. Trials 2019; 20:493. [PMID: 31399148 PMCID: PMC6688224 DOI: 10.1186/s13063-019-3578-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 07/16/2019] [Indexed: 11/22/2022] Open
Abstract
Background With millions of pounds spent annually on medical research in the UK, it is important that studies are spending funds wisely. Internal pilots offer the chance to stop a trial early if it becomes apparent that the study will not be able to recruit enough patients to show whether an intervention is clinically effective. This study aims to assess the use of internal pilots in individually randomised controlled trials funded by the Health Technology Assessment (HTA) programme and to summarise the progression criteria chosen in these trials. Methods Studies were identified from reports of the HTA committees’ funding decisions from 2012 to 2016. In total, 242 trials were identified of which 134 were eligible to be included in the audit. Protocols for the eligible studies were located on the NIHR Journals website, and if protocols were not available online then study managers were contacted to provide information. Results Over two-thirds (72.4%) of studies said in their protocol that they would include an internal pilot phase for their study and 37.8% of studies without an internal pilot had done an external pilot study to assess the feasibility of the full study. A typical study with an internal pilot has a target sample size of 510 over 24 months and aims to recruit one-fifth of their total target sample size within the first one-third of their recruitment time. There has been an increase in studies adopting a three-tiered structure for their progression rules in recent years, with 61.5% (16/26) of studies using the system in 2016 compared to just 11.8% (2/17) in 2015. There was also a rise in the number of studies giving a target recruitment rate in their progression criteria: 42.3% (11/26) in 2016 compared to 35.3% (6/17) in 2015. Conclusions Progression criteria for an internal pilot are usually well specified but targets vary widely. For the actual criteria, red/amber/green systems have increased in popularity in recent years. Trials should justify the targets they have set, especially where targets are low.
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Affiliation(s)
- Esther Herbert
- School of Health and Related Research, University of Sheffield, Regent Court, Regent Street, Sheffield, S1 4DA, UK.
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Regent Court, Regent Street, Sheffield, S1 4DA, UK
| | - Steve Goodacre
- School of Health and Related Research, University of Sheffield, Regent Court, Regent Street, Sheffield, S1 4DA, UK
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Dimairo M, Coates E, Pallmann P, Todd S, Julious SA, Jaki T, Wason J, Mander AP, Weir CJ, Koenig F, Walton MK, Biggs K, Nicholl J, Hamasaki T, Proschan MA, Scott JA, Ando Y, Hind D, Altman DG. Development process of a consensus-driven CONSORT extension for randomised trials using an adaptive design. BMC Med 2018; 16:210. [PMID: 30442137 PMCID: PMC6238302 DOI: 10.1186/s12916-018-1196-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 10/23/2018] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Adequate reporting of adaptive designs (ADs) maximises their potential benefits in the conduct of clinical trials. Transparent reporting can help address some obstacles and concerns relating to the use of ADs. Currently, there are deficiencies in the reporting of AD trials. To overcome this, we have developed a consensus-driven extension to the CONSORT statement for randomised trials using an AD. This paper describes the processes and methods used to develop this extension rather than detailed explanation of the guideline. METHODS We developed the guideline in seven overlapping stages: 1) Building on prior research to inform the need for a guideline; 2) A scoping literature review to inform future stages; 3) Drafting the first checklist version involving an External Expert Panel; 4) A two-round Delphi process involving international, multidisciplinary, and cross-sector key stakeholders; 5) A consensus meeting to advise which reporting items to retain through voting, and to discuss the structure of what to include in the supporting explanation and elaboration (E&E) document; 6) Refining and finalising the checklist; and 7) Writing-up and dissemination of the E&E document. The CONSORT Executive Group oversaw the entire development process. RESULTS Delphi survey response rates were 94/143 (66%), 114/156 (73%), and 79/143 (55%) in rounds 1, 2, and across both rounds, respectively. Twenty-seven delegates from Europe, the USA, and Asia attended the consensus meeting. The main checklist has seven new and nine modified items and six unchanged items with expanded E&E text to clarify further considerations for ADs. The abstract checklist has one new and one modified item together with an unchanged item with expanded E&E text. The E&E document will describe the scope of the guideline, the definition of an AD, and some types of ADs and trial adaptations and explain each reporting item in detail including case studies. CONCLUSIONS We hope that making the development processes, methods, and all supporting information that aided decision-making transparent will enhance the acceptability and quick uptake of the guideline. This will also help other groups when developing similar CONSORT extensions. The guideline is applicable to all randomised trials with an AD and contains minimum reporting requirements.
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Affiliation(s)
- Munyaradzi Dimairo
- School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK.
| | - Elizabeth Coates
- School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | | | | | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | | | - James Wason
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK
| | - Adrian P Mander
- MRC Biostatistics Unit, University of Cambridge, Cambridge, 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
| | - Katie Biggs
- School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Jon Nicholl
- School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, 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, White Oak, USA
| | - Yuki Ando
- Pharmaceuticals and Medical Devices Agency, Tokyo, Japan
| | - Daniel Hind
- School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
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19
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Julious SA. Calculation of confidence intervals for a finite population size. Pharm Stat 2018; 18:115-122. [PMID: 30411472 DOI: 10.1002/pst.1901] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 04/11/2018] [Accepted: 07/31/2018] [Indexed: 11/09/2022]
Abstract
For any estimate of response, confidence intervals are important as they help quantify a plausible range of values for the population response. However, there may be instances in clinical research when the population size is finite, but we wish to take a sample from the population and make inference from this sample. Instances where you can have a fixed population size include when undertaking a clinical audit of patient records or in a clinical trial a researcher could be checking for transcription errors against patient notes. In this paper, we describe how confidence interval calculations can be calculated for a finite population. These confidence intervals are narrower than confidence intervals from population samples. For the extreme case of when a 100% sample from the population is taken, there is no error and the calculation is the population response. The methods in the paper are described using a case study from clinical data management.
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Affiliation(s)
- Steven A Julious
- Medical Statistics Group, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, S1 4DA, UK
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20
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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: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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21
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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22
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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23
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Sones W, Julious SA, Rothwell JC, Ramsay CR, Hampson LV, Emsley R, Walters SJ, Hewitt C, Bland M, Fergusson DA, Berlin JA, Altman D, Vale LD, Cook JA. Choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial - the development of the DELTA 2 guidance. Trials 2018; 19:542. [PMID: 30305155 PMCID: PMC6180499 DOI: 10.1186/s13063-018-2887-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 08/29/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A key step in the design of a randomised controlled trial is the estimation of the number of participants needed. The most common approach is to specify a target difference in the primary outcome between the randomised groups and then estimate the corresponding sample size. The sample size is chosen to provide reassurance that the trial will have high statistical power to detect the target difference at the planned statistical significance level. Alternative approaches are also available, though most still require specification of a target difference. The sample size has many implications for the conduct of the study, as well as incurring scientific and ethical aspects. Despite the critical role of the target difference for the primary outcome in the design of a randomised controlled trial (RCT), the manner in which it is determined has received little attention. This article reports the development of the DELTA2 guidance on the specification and reporting of the target difference for the primary outcome in a sample size calculation for a RCT. METHODS The DELTA2 (Difference ELicitation in TriAls) project has five components comprising 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 (stage 4), and the preparation and dissemination of a guidance document (stage 5). RESULTS The project started in April 2016. The literature search identified 28 articles of methodological developments relevant to a method for specifying a target difference. A Delphi study involving 69 participants, along with a 2-day consensus meeting were conducted. In addition, further engagement sessions were held at two international conferences. The main guidance text was finalised on April 18, 2018, after revision informed by feedback gathered from stages 2 and 3 and from funder representatives. DISCUSSION The DELTA2 Delphi study identified a number of areas (such as practical recommendations and examples, greater coverage of different trial designs and statistical approaches) of particular interest amongst stakeholders which new guidance was desired to meet. New relevant references were identified by the review. Such findings influenced the scope, drafting and revision of the guidance. While not all suggestions could be accommodated, it is hoped that the process has led to a more useful and practical document.
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Affiliation(s)
- 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
| | - Steven A Julious
- Medical Statistics Group, ScHARR, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Joanne C Rothwell
- Medical Statistics Group, ScHARR, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Craig Robert 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 Methodology and Consulting, Novartis Pharma AG, Basel, Switzerland
| | - 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
| | - Stephen J Walters
- Medical Statistics Group, ScHARR, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Catherine Hewitt
- Department of Health Sciences, Seebohm Rowntree Building, University of York, Heslington, York, YO10 5DD, UK
| | - Martin Bland
- Department of Health Sciences, Seebohm Rowntree Building, University of York, Heslington, York, YO10 5DD, UK
| | - Dean A Fergusson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, 501 Smyth Road, Box 201B, Ottawa, ON, K1H 8L6, Canada
| | - Jesse A Berlin
- Johnson & Johnson, One J&J Plaza, New Brunswick, NJ, 08933, USA
| | - 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 David Vale
- Health Economics Group, Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK
| | - Jonathan Alistair 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.
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Rothwell JC, Julious SA, Cooper CL. A study of target effect sizes in randomised controlled trials published in the Health Technology Assessment journal. Trials 2018; 19:544. [PMID: 30305146 PMCID: PMC6180439 DOI: 10.1186/s13063-018-2886-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 08/29/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND When designing a randomised controlled trial (RCT), an important consideration is the sample size required. This is calculated from several components; one of which is the target difference. This study aims to review the currently reported methods of elicitation of the target difference as well as to quantify the target differences used in Health Technology Assessment (HTA)-funded trials. METHODS Trials were identified from the National Institute of Health Research Health Technology Assessment journal. A total of 177 RCTs published between 2006 and 2016 were assessed for eligibility. Eligibility was established by the design of the trial and the quality of data available. The trial designs were parallel-group, superiority RCTs with a continuous primary endpoint. Data were extracted and the standardised anticipated and observed effect size estimates were calculated. Exclusion criteria was based on trials not providing enough detail in the sample size calculation and results, and trials not being of parallel-group, superiority design. RESULTS A total of 107 RCTs were included in the study from 102 reports. The most commonly reported method for effect size derivation was a review of evidence and use of previous research (52.3%). This was common across all clinical areas. The median standardised target effect size was 0.30 (interquartile range: 0.20-0.38), with the median standardised observed effect size 0.11 (IQR 0.05-0.29). The maximum anticipated and observed effect sizes were 0.76 and 1.18, respectively. Only two trials had anticipated target values above 0.60. CONCLUSION The most commonly reported method of elicitation of the target effect size is previous published research. The average target effect size was 0.3. A clear distinction between the target difference and the minimum clinically important difference is recommended when designing a trial. Transparent explanation of target difference elicitation is advised, with multiple methods including a review of evidence and opinion-seeking advised as the more optimal methods for effect size quantification.
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Affiliation(s)
- Joanne C. Rothwell
- School of Health and Related Research, University of Sheffield, Sheffield, UK
- The Medical Statistics Group, School of Health and Related Research (ScHARR), University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA UK
| | - Steven A. Julious
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Cindy L. Cooper
- Sheffield Clinical Trials Unit, University of Sheffield, Sheffield, UK
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25
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Julious SA, Horspool MJ, Davis S, Franklin M, Smithson WH, Norman P, Simpson RM, Elphick H, Bortolami O, Cooper C. Open-label, cluster randomised controlled trial and economic evaluation of a brief letter from a GP on unscheduled medical contacts associated with the start of the school year: the PLEASANT trial. BMJ Open 2018; 8:e017367. [PMID: 29678962 PMCID: PMC5914776 DOI: 10.1136/bmjopen-2017-017367] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Asthma is seasonal with peaks in exacerbation rates in school-age children associated with the return to school following the summer vacation. A drop in prescription collection in August is associated with an increase in the number of unscheduled contacts after the school return. OBJECTIVE To assess whether a public health intervention delivered in general practice reduced unscheduled medical contacts in children with asthma. DESIGN Cluster randomised trial with trial-based economic evaluation. Randomisation was at general practice level, stratified by size of practice. The intervention group received a letter from their general practitioner (GP) in late July outlining the importance of (re)taking asthma medication before the return to school. The control group was usual care. SETTING General practices in England and Wales. PARTICIPANTS 12 179 school-age children in 142 general practices (70 randomised to intervention). MAIN OUTCOME Proportion of children aged 5-16 years who had an unscheduled contact in September. Secondary endpoints included collection of prescriptions in August and medical contacts over 12 months (September-August). Economic endpoints were quality-adjusted life-years gained and health service costs. RESULTS There was no evidence of effect (OR 1.09; 95% CI 0.96 to 1.25 against treatment) on unscheduled contacts in September. The intervention increased the proportion of children collecting a prescription in August by 4% (OR 1.43; 95% CI 1.24 to 1.64). The intervention also reduced the total number of medical contacts between September-August by 5% (incidence ratio 0.95; 95% CI 0.91 to 0.99).The mean reduction in medical contacts informed the health economics analyses. The intervention was estimated to save £36.07 per patient, with a high probability (96.3%) of being cost-saving. CONCLUSIONS The intervention succeeded in increasing children collecting prescriptions. It did not reduce unscheduled care in September (the primary outcome), but in the year following the intervention, it reduced the total number of medical contacts. TRIAL REGISTRATION NUMBER ISRCTN03000938; Results.
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Affiliation(s)
- Steven A Julious
- Medical Statistics Group, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Michelle J Horspool
- Medical Statistics Group, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Sarah Davis
- Health Economics and Decision Sciences, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Matthew Franklin
- Health Economics and Decision Sciences, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - W Henry Smithson
- Department of General Practice, University of Cork, Cork, Ireland
| | - Paul Norman
- Department of Psychology, University of Sheffield, Sheffield, UK
| | - Rebecca M Simpson
- Medical Statistics Group, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Heather Elphick
- Respiratory Department, Sheffield Children's Hospital, Sheffield, UK
| | - Oscar Bortolami
- Medical Statistics Group, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Cindy Cooper
- Clinical Trials Research Unit, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
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Cooper CL, Whitehead A, Pottrill E, Julious SA, Walters SJ. Are pilot trials useful for predicting randomisation and attrition rates in definitive studies: A review of publicly funded trials. Clin Trials 2018; 15:189-196. [PMID: 29361833 PMCID: PMC5894808 DOI: 10.1177/1740774517752113] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
BACKGROUND/AIMS External pilot trials are recommended for testing the feasibility of main or confirmatory trials. However, there is little evidence that progress in external pilot trials actually predicts randomisation and attrition rates in the main trial. To assess the use of external pilot trials in trial design, we compared randomisation and attrition rates in publicly funded randomised controlled trials with rates in their pilots. METHODS Randomised controlled trials for which there was an external pilot trial were identified from reports published between 2004 and 2013 in the Health Technology Assessment Journal. Data were extracted from published papers, protocols and reports. Bland-Altman plots and descriptive statistics were used to investigate the agreement of randomisation and attrition rates between the full and external pilot trials. RESULTS Of 561 reports, 41 were randomised controlled trials with pilot trials and 16 met criteria for a pilot trial with sufficient data. Mean attrition and randomisation rates were 21.1% and 50.4%, respectively, in the pilot trials and 16.8% and 65.2% in the main. There was minimal bias in the pilot trial when predicting the main trial attrition and randomisation rate. However, the variation was large: the mean difference in the attrition rate between the pilot and main trial was -4.4% with limits of agreement of -37.1% to 28.2%. Limits of agreement for randomisation rates were -47.8% to 77.5%. CONCLUSION Results from external pilot trials to estimate randomisation and attrition rates should be used with caution as comparison of the difference in the rates between pilots and their associated full trial demonstrates high variability. We suggest using internal pilot trials wherever appropriate.
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Affiliation(s)
- Cindy L Cooper
- Clinical Trials Research Unit, School of Health and Related Research (ScHARR), The University of Sheffield, Sheffield, UK
| | - Amy Whitehead
- School of Health and Related Research (ScHARR), The University of Sheffield, Sheffield, UK
- University of Southampton, Southampton, UK
| | - Edward Pottrill
- School of Health and Related Research (ScHARR), The University of Sheffield, Sheffield, UK
| | - Steven A Julious
- School of Health and Related Research (ScHARR), The University of Sheffield, Sheffield, UK
| | - Stephen J Walters
- School of Health and Related Research (ScHARR), The University of Sheffield, Sheffield, UK
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Julious SA, Horspool MJ, Davis S, Bradburn M, Norman P, Shephard N, Cooper CL, Smithson WH, Boote J, Elphick H, Loban A, Franklin M, Kua WS, May R, Campbell J, Williams R, Rex S, Bortolami O. PLEASANT: Preventing and Lessening Exacerbations of Asthma in School-age children Associated with a New Term - a cluster randomised controlled trial and economic evaluation. Health Technol Assess 2018; 20:1-154. [PMID: 28005003 DOI: 10.3310/hta20930] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Asthma episodes and deaths are known to be seasonal. A number of reports have shown peaks in asthma episodes in school-aged children associated with the return to school following the summer vacation. A fall in prescription collection in the month of August has been observed, and was associated with an increase in the number of unscheduled contacts after the return to school in September. OBJECTIVE The primary objective of the study was to assess whether or not a NHS-delivered public health intervention reduces the September peak in unscheduled medical contacts. DESIGN Cluster randomised trial, with the unit of randomisation being 142 NHS general practices, and trial-based economic evaluation. SETTING Primary care. INTERVENTION A letter sent (n = 70 practices) in July from their general practitioner (GP) to parents/carers of school-aged children with asthma to remind them of the importance of taking their medication, and to ensure that they have sufficient medication prior to the start of the new school year in September. The control group received usual care. MAIN OUTCOME MEASURES The primary outcome measure was the proportion of children aged 5-16 years who had an unscheduled medical contact in September 2013. Supporting end points included the proportion of children who collected prescriptions in August 2013 and unscheduled contacts through the following 12 months. Economic end points were quality-adjusted life-years (QALYs) gained and costs from an NHS and Personal Social Services perspective. RESULTS There is no evidence of effect in terms of unscheduled contacts in September. Among children aged 5-16 years, the odds ratio (OR) was 1.09 [95% confidence interval (CI) 0.96 to 1.25] against the intervention. The intervention did increase the proportion of children collecting a prescription in August (OR 1.43, 95% CI 1.24 to 1.64) as well as scheduled contacts in the same month (OR 1.13, 95% CI 0.84 to 1.52). For the wider time intervals (September-December 2013 and September-August 2014), there is weak evidence of the intervention reducing unscheduled contacts. The intervention did not reduce unscheduled care in September, although it succeeded in increasing the proportion of children collecting prescriptions in August as well as having scheduled contacts in the same month. These unscheduled contacts in September could be a result of the intervention, as GPs may have wanted to see patients before issuing a prescription. The economic analysis estimated a high probability that the intervention was cost-saving, for baseline-adjusted costs, across both base-case and sensitivity analyses. There was no increase in QALYs. LIMITATION The use of routine data led to uncertainty in the coding of medical contacts. The uncertainty was mitigated by advice from a GP adjudication panel. CONCLUSIONS The intervention did not reduce unscheduled care in September, although it succeeded in increasing the proportion of children both collecting prescriptions and having scheduled contacts in August. After September there is weak evidence in favour of the intervention. The intervention had a favourable impact on costs but did not demonstrate any impact on QALYs. The results of the trial indicate that further work is required on assessing and understanding adherence, both in terms of using routine data to make quantitative assessments, and through additional qualitative interviews with key stakeholders such as practice nurses, GPs and a wider group of children with asthma. TRIAL REGISTRATION Current Controlled Trials ISRCTN03000938. FUNDING DETAILS This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 20, No. 93. See the HTA programme website for further project information.
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Affiliation(s)
- Steven A Julious
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Michelle J Horspool
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Sarah Davis
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Mike Bradburn
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Paul Norman
- Department of Psychology, University of Sheffield, Sheffield, UK
| | - Neil Shephard
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Cindy L Cooper
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - W Henry Smithson
- Department of Clinical Practice, University of Cork, Cork, Ireland
| | - Jonathan Boote
- Centre for Research in Primary and Community Care, University of Hertfordshire, Hatfield, UK
| | - Heather Elphick
- Respiratory Department, Sheffield Children's Hospital, Sheffield, UK
| | - Amanda Loban
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Matthew Franklin
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Wei Sun Kua
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Robin May
- Clinical Practice Research Datalink, London, UK
| | | | | | - Saleema Rex
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Oscar Bortolami
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
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Abstract
Background A pilot study can be an important step in the assessment of an intervention by providing information to design the future definitive trial. Pilot studies can be used to estimate the recruitment and retention rates and population variance and to provide preliminary evidence of efficacy potential. However, estimation is poor because pilot studies are small, so sensitivity analyses for the main trial’s sample size calculations should be undertaken. Methods We demonstrate how to carry out easy-to-perform sensitivity analysis for designing trials based on pilot data using an example. Furthermore, we introduce rules of thumb for the size of the pilot study so that the overall sample size, for both pilot and main trials, is minimized. Results The example illustrates how sample size estimates for the main trial can alter dramatically by plausibly varying assumptions. Required sample size for 90% power varied from 392 to 692 depending on assumptions. Some scenarios were not feasible based on the pilot study recruitment and retention rates. Conclusion Pilot studies can be used to help design the main trial, but caution should be exercised. We recommend the use of sensitivity analyses to assess the robustness of the design assumptions for a main trial.
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Affiliation(s)
- Melanie L Bell
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Amy L Whitehead
- Medical Statistics Group, Design, Trials and Statistics, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Steven A Julious
- Medical Statistics Group, Design, Trials and Statistics, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
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Liu F, Walters SJ, Julious SA. Design considerations and analysis planning of a phase 2a proof of concept study in rheumatoid arthritis in the presence of possible non-monotonicity. BMC Med Res Methodol 2017; 17:149. [PMID: 28969588 PMCID: PMC5625783 DOI: 10.1186/s12874-017-0416-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 08/31/2017] [Indexed: 05/10/2023] Open
Abstract
Background It is important to quantify the dose response for a drug in phase 2a clinical trials so the optimal doses can then be selected for subsequent late phase trials. In a phase 2a clinical trial of new lead drug being developed for the treatment of rheumatoid arthritis (RA), a U-shaped dose response curve was observed. In the light of this result further research was undertaken to design an efficient phase 2a proof of concept (PoC) trial for a follow-on compound using the lessons learnt from the lead compound. Methods The planned analysis for the Phase 2a trial for GSK123456 was a Bayesian Emax model which assumes the dose-response relationship follows a monotonic sigmoid “S” shaped curve. This model was found to be suboptimal to model the U-shaped dose response observed in the data from this trial and alternatives approaches were needed to be considered for the next compound for which a Normal dynamic linear model (NDLM) is proposed. This paper compares the statistical properties of the Bayesian Emax model and NDLM model and both models are evaluated using simulation in the context of adaptive Phase 2a PoC design under a variety of assumed dose response curves: linear, Emax model, U-shaped model, and flat response. Results It is shown that the NDLM method is flexible and can handle a wide variety of dose-responses, including monotonic and non-monotonic relationships. In comparison to the NDLM model the Emax model excelled with higher probability of selecting ED90 and smaller average sample size, when the true dose response followed Emax like curve. In addition, the type I error, probability of incorrectly concluding a drug may work when it does not, is inflated with the Bayesian NDLM model in all scenarios which would represent a development risk to pharmaceutical company. The bias, which is the difference between the estimated effect from the Emax and NDLM models and the simulated value, is comparable if the true dose response follows a placebo like curve, an Emax like curve, or log linear shape curve under fixed dose allocation, no adaptive allocation, half adaptive and adaptive scenarios. The bias though is significantly increased for the Emax model if the true dose response follows a U-shaped curve. Conclusions In most cases the Bayesian Emax model works effectively and efficiently, with low bias and good probability of success in case of monotonic dose response. However, if there is a belief that the dose response could be non-monotonic then the NDLM is the superior model to assess the dose response. Electronic supplementary material The online version of this article (10.1186/s12874-017-0416-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Feng Liu
- GlaxoSmithKline, Inc, 1250 South Collegeville Road, PO Box 5089, Collegeville, PA, 19426-0989, USA. .,Medical Statistics Group, University of Sheffield, Sheffield, UK.
| | | | - Steven A Julious
- Medical Statistics Group, University of Sheffield, Sheffield, UK
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Walters SJ, Bonacho Dos Anjos Henriques-Cadby I, Bortolami O, Flight L, Hind D, Jacques RM, Knox C, Nadin B, Rothwell J, Surtees M, Julious SA. Recruitment and retention of participants in randomised controlled trials: a review of trials funded and published by the United Kingdom Health Technology Assessment Programme. BMJ Open 2017; 7:e015276. [PMID: 28320800 PMCID: PMC5372123 DOI: 10.1136/bmjopen-2016-015276] [Citation(s) in RCA: 287] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Substantial amounts of public funds are invested in health research worldwide. Publicly funded randomised controlled trials (RCTs) often recruit participants at a slower than anticipated rate. Many trials fail to reach their planned sample size within the envisaged trial timescale and trial funding envelope. OBJECTIVES To review the consent, recruitment and retention rates for single and multicentre randomised control trials funded and published by the UK's National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme. DATA SOURCES AND STUDY SELECTION HTA reports of individually randomised single or multicentre RCTs published from the start of 2004 to the end of April 2016 were reviewed. DATA EXTRACTION Information was extracted, relating to the trial characteristics, sample size, recruitment and retention by two independent reviewers. MAIN OUTCOME MEASURES Target sample size and whether it was achieved; recruitment rates (number of participants recruited per centre per month) and retention rates (randomised participants retained and assessed with valid primary outcome data). RESULTS This review identified 151 individually RCTs from 787 NIHR HTA reports. The final recruitment target sample size was achieved in 56% (85/151) of the RCTs and more than 80% of the final target sample size was achieved for 79% of the RCTs (119/151). The median recruitment rate (participants per centre per month) was found to be 0.92 (IQR 0.43-2.79) and the median retention rate (proportion of participants with valid primary outcome data at follow-up) was estimated at 89% (IQR 79-97%). CONCLUSIONS There is considerable variation in the consent, recruitment and retention rates in publicly funded RCTs. Investigators should bear this in mind at the planning stage of their study and not be overly optimistic about their recruitment projections.
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Affiliation(s)
- Stephen J Walters
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | | | - Oscar Bortolami
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Laura Flight
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Daniel Hind
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Richard M Jacques
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Christopher Knox
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Ben Nadin
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Joanne Rothwell
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Michael Surtees
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Sheffield, UK
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Posadzki P, Mastellos N, Ryan R, Gunn LH, Felix LM, Pappas Y, Gagnon M, Julious SA, Xiang L, Oldenburg B, Car J. Automated telephone communication systems for preventive healthcare and management of long-term conditions. Cochrane Database Syst Rev 2016; 12:CD009921. [PMID: 27960229 PMCID: PMC6463821 DOI: 10.1002/14651858.cd009921.pub2] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
BACKGROUND Automated telephone communication systems (ATCS) can deliver voice messages and collect health-related information from patients using either their telephone's touch-tone keypad or voice recognition software. ATCS can supplement or replace telephone contact between health professionals and patients. There are four different types of ATCS: unidirectional (one-way, non-interactive voice communication), interactive voice response (IVR) systems, ATCS with additional functions such as access to an expert to request advice (ATCS Plus) and multimodal ATCS, where the calls are delivered as part of a multicomponent intervention. OBJECTIVES To assess the effects of ATCS for preventing disease and managing long-term conditions on behavioural change, clinical, process, cognitive, patient-centred and adverse outcomes. SEARCH METHODS We searched 10 electronic databases (the Cochrane Central Register of Controlled Trials; MEDLINE; Embase; PsycINFO; CINAHL; Global Health; WHOLIS; LILACS; Web of Science; and ASSIA); three grey literature sources (Dissertation Abstracts, Index to Theses, Australasian Digital Theses); and two trial registries (www.controlled-trials.com; www.clinicaltrials.gov) for papers published between 1980 and June 2015. SELECTION CRITERIA Randomised, cluster- and quasi-randomised trials, interrupted time series and controlled before-and-after studies comparing ATCS interventions, with any control or another ATCS type were eligible for inclusion. Studies in all settings, for all consumers/carers, in any preventive healthcare or long term condition management role were eligible. DATA COLLECTION AND ANALYSIS We used standard Cochrane methods to select and extract data and to appraise eligible studies. MAIN RESULTS We included 132 trials (N = 4,669,689). Studies spanned across several clinical areas, assessing many comparisons based on evaluation of different ATCS types and variable comparison groups. Forty-one studies evaluated ATCS for delivering preventive healthcare, 84 for managing long-term conditions, and seven studies for appointment reminders. We downgraded our certainty in the evidence primarily because of the risk of bias for many outcomes. We judged the risk of bias arising from allocation processes to be low for just over half the studies and unclear for the remainder. We considered most studies to be at unclear risk of performance or detection bias due to blinding, while only 16% of studies were at low risk. We generally judged the risk of bias due to missing data and selective outcome reporting to be unclear.For preventive healthcare, ATCS (ATCS Plus, IVR, unidirectional) probably increase immunisation uptake in children (risk ratio (RR) 1.25, 95% confidence interval (CI) 1.18 to 1.32; 5 studies, N = 10,454; moderate certainty) and to a lesser extent in adolescents (RR 1.06, 95% CI 1.02 to 1.11; 2 studies, N = 5725; moderate certainty). The effects of ATCS in adults are unclear (RR 2.18, 95% CI 0.53 to 9.02; 2 studies, N = 1743; very low certainty).For screening, multimodal ATCS increase uptake of screening for breast cancer (RR 2.17, 95% CI 1.55 to 3.04; 2 studies, N = 462; high certainty) and colorectal cancer (CRC) (RR 2.19, 95% CI 1.88 to 2.55; 3 studies, N = 1013; high certainty) versus usual care. It may also increase osteoporosis screening. ATCS Plus interventions probably slightly increase cervical cancer screening (moderate certainty), but effects on osteoporosis screening are uncertain. IVR systems probably increase CRC screening at 6 months (RR 1.36, 95% CI 1.25 to 1.48; 2 studies, N = 16,915; moderate certainty) but not at 9 to 12 months, with probably little or no effect of IVR (RR 1.05, 95% CI 0.99, 1.11; 2 studies, 2599 participants; moderate certainty) or unidirectional ATCS on breast cancer screening.Appointment reminders delivered through IVR or unidirectional ATCS may improve attendance rates compared with no calls (low certainty). For long-term management, medication or laboratory test adherence provided the most general evidence across conditions (25 studies, data not combined). Multimodal ATCS versus usual care showed conflicting effects (positive and uncertain) on medication adherence. ATCS Plus probably slightly (versus control; moderate certainty) or probably (versus usual care; moderate certainty) improves medication adherence but may have little effect on adherence to tests (versus control). IVR probably slightly improves medication adherence versus control (moderate certainty). Compared with usual care, IVR probably improves test adherence and slightly increases medication adherence up to six months but has little or no effect at longer time points (moderate certainty). Unidirectional ATCS, compared with control, may have little effect or slightly improve medication adherence (low certainty). The evidence suggested little or no consistent effect of any ATCS type on clinical outcomes (blood pressure control, blood lipids, asthma control, therapeutic coverage) related to adherence, but only a small number of studies contributed clinical outcome data.The above results focus on areas with the most general findings across conditions. In condition-specific areas, the effects of ATCS varied, including by the type of ATCS intervention in use.Multimodal ATCS probably decrease both cancer pain and chronic pain as well as depression (moderate certainty), but other ATCS types were less effective. Depending on the type of intervention, ATCS may have small effects on outcomes for physical activity, weight management, alcohol consumption, and diabetes mellitus. ATCS have little or no effect on outcomes related to heart failure, hypertension, mental health or smoking cessation, and there is insufficient evidence to determine their effects for preventing alcohol/substance misuse or managing illicit drug addiction, asthma, chronic obstructive pulmonary disease, HIV/AIDS, hypercholesterolaemia, obstructive sleep apnoea, spinal cord dysfunction or psychological stress in carers.Only four trials (3%) reported adverse events, and it was unclear whether these were related to the interventions. AUTHORS' CONCLUSIONS ATCS interventions can change patients' health behaviours, improve clinical outcomes and increase healthcare uptake with positive effects in several important areas including immunisation, screening, appointment attendance, and adherence to medications or tests. The decision to integrate ATCS interventions in routine healthcare delivery should reflect variations in the certainty of the evidence available and the size of effects across different conditions, together with the varied nature of ATCS interventions assessed. Future research should investigate both the content of ATCS interventions and the mode of delivery; users' experiences, particularly with regard to acceptability; and clarify which ATCS types are most effective and cost-effective.
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Affiliation(s)
- Pawel Posadzki
- Lee Kong Chian School of Medicine, Nanyang Technological UniversityCentre for Population Health Sciences (CePHaS)3 Fusionopolis Link, #06‐13Nexus@one‐northSingaporeSingapore138543
| | - Nikolaos Mastellos
- Imperial College LondonGlobal eHealth Unit, Department of Primary Care and Public Health, School of Public HealthSt Dunstans RoadLondonHammersmithUKW6 8RP
| | - Rebecca Ryan
- La Trobe UniversityCentre for Health Communication and Participation, School of Psychology and Public HealthBundooraVICAustralia3086
| | - Laura H Gunn
- Stetson UniversityPublic Health Program421 N Woodland BlvdDeLandFloridaUSA32723
| | - Lambert M Felix
- Edge Hill UniversityFaculty of Health and Social CareSt Helens RoadOrmskirkLancashireUKL39 4QP
| | - Yannis Pappas
- University of BedfordshireInstitute for Health ResearchPark SquareLutonBedfordUKLU1 3JU
| | - Marie‐Pierre Gagnon
- Traumatologie – Urgence – Soins IntensifsCentre de recherche du CHU de Québec, Axe Santé des populations ‐ Pratiques optimales en santé10 Rue de l'Espinay, D6‐727QuébecQCCanadaG1L 3L5
| | - Steven A Julious
- University of SheffieldMedical Statistics Group, School of Health and Related ResearchRegent Court, 30 Regent StreetSheffieldUKS1 4DA
| | - Liming Xiang
- Nanyang Technological UniversityDivision of Mathematical Sciences, School of Physical and Mathematical Sciences21 Nanyang LinkSingaporeSingapore
| | - Brian Oldenburg
- University of MelbourneMelbourne School of Population and Global HealthMelbourneVictoriaAustralia
| | - Josip Car
- Lee Kong Chian School of Medicine, Nanyang Technological UniversityCentre for Population Health Sciences (CePHaS)3 Fusionopolis Link, #06‐13Nexus@one‐northSingaporeSingapore138543
- Imperial College LondonGlobal eHealth Unit, Department of Primary Care and Public Health, School of Public HealthSt Dunstans RoadLondonHammersmithUKW6 8RP
- University of LjubljanaDepartment of Family Medicine, Faculty of MedicineLjubljanaSlovenia
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Flight L, Julious SA. Corrections: The disagreeable behaviour of the kappa statistic. Pharm Stat 2016; 16:95. [PMID: 27910219 DOI: 10.1002/pst.1795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2016] [Accepted: 10/24/2016] [Indexed: 11/11/2022]
Affiliation(s)
- Laura Flight
- Medical Statistics Group, ScHARR, University of Sheffield, Sheffield, England
| | - Steven A Julious
- Medical Statistics Group, ScHARR, University of Sheffield, Sheffield, England
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Flight L, Julious SA, Goodacre S. Can emergency medicine research benefit from adaptive design clinical trials? Emerg Med J 2016; 34:243-248. [DOI: 10.1136/emermed-2016-206046] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 10/03/2016] [Indexed: 11/04/2022]
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Hatfield I, Allison A, Flight L, Julious SA, Dimairo M. Adaptive designs undertaken in clinical research: a review of registered clinical trials. Trials 2016; 17:150. [PMID: 26993469 PMCID: PMC4799596 DOI: 10.1186/s13063-016-1273-9] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Accepted: 03/02/2016] [Indexed: 12/25/2022] Open
Abstract
Adaptive designs have the potential to improve efficiency in the evaluation of new medical treatments in comparison to traditional fixed sample size designs. However, they are still not widely used in practice in clinical research. Little research has been conducted to investigate what adaptive designs are being undertaken. This review highlights the current state of registered adaptive designs and their characteristics. The review looked at phase II, II/III and III trials registered on ClinicalTrials.gov from 29 February 2000 to 1 June 2014, supplemented with trials from the National Institute for Health Research register and known adaptive trials. A range of adaptive design search terms were applied to the trials extracted from each database. Characteristics of the adaptive designs were then recorded including funder, therapeutic area and type of adaptation. The results in the paper suggest that the use of adaptive designs has increased. They seem to be most often used in phase II trials and in oncology. In phase III trials, the most popular form of adaptation is the group sequential design. The review failed to capture all trials with adaptive designs, which suggests that the reporting of adaptive designs, such as in clinical trials registers, needs much improving. We recommend that clinical trial registers should contain sections dedicated to the type and scope of the adaptation and that the term 'adaptive design' should be included in the trial title or at least in the brief summary or design sections.
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Affiliation(s)
- Isabella Hatfield
- />School of Mathematics & Statistics, Newcastle University, Herschel Building, Newcastle upon Tyne, NE1 7RU UK
- />ScHARR, University of Sheffield, 30 Regent Street, Sheffield, S1 4DA UK
| | - Annabel Allison
- />ScHARR, University of Sheffield, 30 Regent Street, Sheffield, S1 4DA UK
- />MRC Biostatistics Unit, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR UK
| | - Laura Flight
- />ScHARR, University of Sheffield, 30 Regent Street, Sheffield, S1 4DA UK
| | - Steven A. Julious
- />ScHARR, University of Sheffield, 30 Regent Street, Sheffield, S1 4DA UK
| | - Munyaradzi Dimairo
- />ScHARR, University of Sheffield, 30 Regent Street, Sheffield, S1 4DA UK
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Dimairo M, Julious SA, Todd S, Nicholl JP, Boote J. Cross-sector surveys assessing perceptions of key stakeholders towards barriers, concerns and facilitators to the appropriate use of adaptive designs in confirmatory trials. Trials 2015; 16:585. [PMID: 26700741 PMCID: PMC4690427 DOI: 10.1186/s13063-015-1119-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Accepted: 12/14/2015] [Indexed: 11/10/2022] Open
Abstract
Background Appropriately conducted adaptive designs (ADs) offer many potential advantages over conventional trials. They make better use of accruing data, potentially saving time, trial participants, and limited resources compared to conventional, fixed sample size designs. However, one can argue that ADs are not implemented as often as they should be, particularly in publicly funded confirmatory trials. This study explored barriers, concerns, and potential facilitators to the appropriate use of ADs in confirmatory trials among key stakeholders. Methods We conducted three cross-sectional, online parallel surveys between November 2014 and January 2015. The surveys were based upon findings drawn from in-depth interviews of key research stakeholders, predominantly in the UK, and targeted Clinical Trials Units (CTUs), public funders, and private sector organisations. Response rates were as follows: 30(55 %) UK CTUs, 17(68 %) private sector, and 86(41 %) public funders. A Rating Scale Model was used to rank barriers and concerns in order of perceived importance for prioritisation. Results Top-ranked barriers included the lack of bridge funding accessible to UK CTUs to support the design of ADs, limited practical implementation knowledge, preference for traditional mainstream designs, difficulties in marketing ADs to key stakeholders, time constraints to support ADs relative to competing priorities, lack of applied training, and insufficient access to case studies of undertaken ADs to facilitate practical learning and successful implementation. Associated practical complexities and inadequate data management infrastructure to support ADs were reported as more pronounced in the private sector. For funders of public research, the inadequate description of the rationale, scope, and decision-making criteria to guide the planned AD in grant proposals by researchers were all viewed as major obstacles. Conclusions There are still persistent and important perceptions of individual and organisational obstacles hampering the use of ADs in confirmatory trials research. Stakeholder perceptions about barriers are largely consistent across sectors, with a few exceptions that reflect differences in organisations’ funding structures, experiences and characterisation of study interventions. Most barriers appear connected to a lack of practical implementation knowledge and applied training, and limited access to case studies to facilitate practical learning. Electronic supplementary material The online version of this article (doi:10.1186/s13063-015-1119-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Munyaradzi Dimairo
- ScHARR, Regent Court, University of Sheffield, 30 Regent Street, S1 4DA, Sheffield, UK.
| | - Steven A Julious
- ScHARR, Regent Court, University of Sheffield, 30 Regent Street, S1 4DA, Sheffield, UK.
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, Whiteknights, Reading, RG6 6AX, UK.
| | - Jonathan P Nicholl
- ScHARR, Regent Court, University of Sheffield, 30 Regent Street, S1 4DA, Sheffield, UK.
| | - Jonathan Boote
- ScHARR, Regent Court, University of Sheffield, 30 Regent Street, S1 4DA, Sheffield, UK. .,Centre for Research in Primary and Community Care, University of Hertfordshire, Hatfield, AL109AB, Hertfordshire, UK.
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Cameron D, Epton T, Norman P, Sheeran P, Harris PR, Webb TL, Julious SA, Brennan A, Thomas C, Petroczi A, Naughton D, Shah I. A theory-based online health behaviour intervention for new university students (U@Uni:LifeGuide): results from a repeat randomized controlled trial. Trials 2015; 16:555. [PMID: 26643917 PMCID: PMC4672536 DOI: 10.1186/s13063-015-1092-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Accepted: 11/27/2015] [Indexed: 01/08/2023] Open
Abstract
Background This paper reports the results of a repeat trial assessing the effectiveness of an online theory-based intervention to promote healthy lifestyle behaviours in new university students. The original trial found that the intervention reduced the number of smokers at 6-month follow-up compared with the control condition, but had non-significant effects on the other targeted health behaviours. However, the original trial suffered from low levels of engagement, which the repeat trial sought to rectify. Methods Three weeks before staring university, all incoming undergraduate students at a large university in the UK were sent an email inviting them to participate in the study. After completing a baseline questionnaire, participants were randomly allocated to intervention or control conditions. The intervention consisted of a self-affirmation manipulation, health messages based on the theory of planned behaviour and implementation intention tasks. Participants were followed-up 1 and 6 months after starting university. The primary outcome measures were portions of fruit and vegetables consumed, physical activity levels, units of alcohol consumed and smoking status at 6-month follow-up. Results The study recruited 2,621 students (intervention n = 1346, control n = 1275), of whom 1495 completed at least one follow-up (intervention n = 696, control n = 799). Intention-to-treat analyses indicated that the intervention had a non-significant effect on the primary outcomes, although the effect of the intervention on fruit and vegetable intake was significant in the per-protocol analyses. Secondary analyses revealed that the intervention had significant effects on having smoked at university (self-report) and on a biochemical marker of alcohol use. Conclusions Despite successfully increasing levels of engagement, the intervention did not have a significant effect on the primary outcome measures. The relatively weak effects of the intervention, found in both the original and repeat trials, may be due to the focus on multiple versus single health behaviours. Future interventions targeting the health behaviour of new university students should therefore focus on single health behaviours. Trial registration Current Controlled Trials ISRCTN07407344.
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Affiliation(s)
- David Cameron
- Department of Psychology, University of Sheffield, Western Bank, Sheffield, S10 2TP, UK.
| | - Tracy Epton
- School of Psychological Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
| | - Paul Norman
- Department of Psychology, University of Sheffield, Western Bank, Sheffield, S10 2TP, UK.
| | - Paschal Sheeran
- Psychology Department, University of North Carolina, 323 Davie Hall, Chapel Hill, NC, 27599-3270, USA.
| | - Peter R Harris
- School of Psychology, University of Sussex, Falmer, Brighton, BN1 9QH, UK.
| | - Thomas L Webb
- Department of Psychology, University of Sheffield, Western Bank, Sheffield, S10 2TP, UK.
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Regent Court, Sheffield, S1 4DA, UK.
| | - Alan Brennan
- School of Health and Related Research, University of Sheffield, Regent Court, Sheffield, S1 4DA, UK.
| | - Chloe Thomas
- School of Health and Related Research, University of Sheffield, Regent Court, Sheffield, S1 4DA, UK.
| | - Andrea Petroczi
- School of Life Sciences, Pharmacy and Chemistry, Kingston University, Penrhyn Road, Kingston upon Thames, KT1 2EE, UK.
| | - Declan Naughton
- School of Life Sciences, Pharmacy and Chemistry, Kingston University, Penrhyn Road, Kingston upon Thames, KT1 2EE, UK.
| | - Iltaf Shah
- School of Life Sciences, Pharmacy and Chemistry, Kingston University, Penrhyn Road, Kingston upon Thames, KT1 2EE, UK.
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Flight L, Julious SA. Practical guide to sample size calculations: non-inferiority and equivalence trials. Pharm Stat 2015; 15:80-9. [DOI: 10.1002/pst.1716] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Revised: 08/26/2015] [Accepted: 08/26/2015] [Indexed: 11/11/2022]
Affiliation(s)
- Laura Flight
- Medical Statistics Group; The University of Sheffield; Sheffield UK
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40
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Flight L, Julious SA. Practical guide to sample size calculations: superiority trials. Pharm Stat 2015; 15:75-9. [DOI: 10.1002/pst.1718] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Revised: 08/26/2015] [Accepted: 08/26/2015] [Indexed: 11/07/2022]
Affiliation(s)
- Laura Flight
- Medical Statistics Group; University of Sheffield; Sheffield UK
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Flight L, Julious SA. Practical guide to sample size calculations: an introduction. Pharm Stat 2015; 15:68-74. [DOI: 10.1002/pst.1709] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Revised: 08/14/2015] [Accepted: 07/21/2015] [Indexed: 11/12/2022]
Affiliation(s)
- Laura Flight
- Medical Statistics Group; University of Sheffield; Sheffield England
| | - Steven A. Julious
- Medical Statistics Group; University of Sheffield; Sheffield England
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Dimairo M, Boote J, Julious SA, Nicholl JP, Todd S. Missing steps in a staircase: a qualitative study of the perspectives of key stakeholders on the use of adaptive designs in confirmatory trials. Trials 2015; 16:430. [PMID: 26416387 PMCID: PMC4587783 DOI: 10.1186/s13063-015-0958-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2014] [Accepted: 09/14/2015] [Indexed: 11/30/2022] Open
Abstract
Background Despite the promising benefits of adaptive designs (ADs), their routine use, especially in confirmatory trials, is lagging behind the prominence given to them in the statistical literature. Much of the previous research to understand barriers and potential facilitators to the use of ADs has been driven from a pharmaceutical drug development perspective, with little focus on trials in the public sector. In this paper, we explore key stakeholders’ experiences, perceptions and views on barriers and facilitators to the use of ADs in publicly funded confirmatory trials. Methods Semi-structured, in-depth interviews of key stakeholders in clinical trials research (CTU directors, funding board and panel members, statisticians, regulators, chief investigators, data monitoring committee members and health economists) were conducted through telephone or face-to-face sessions, predominantly in the UK. We purposively selected participants sequentially to optimise maximum variation in views and experiences. We employed the framework approach to analyse the qualitative data. Results We interviewed 27 participants. We found some of the perceived barriers to be: lack of knowledge and experience coupled with paucity of case studies, lack of applied training, degree of reluctance to use ADs, lack of bridge funding and time to support design work, lack of statistical expertise, some anxiety about the impact of early trial stopping on researchers’ employment contracts, lack of understanding of acceptable scope of ADs and when ADs are appropriate, and statistical and practical complexities. Reluctance to use ADs seemed to be influenced by: therapeutic area, unfamiliarity, concerns about their robustness in decision-making and acceptability of findings to change practice, perceived complexities and proposed type of AD, among others. Conclusions There are still considerable multifaceted, individual and organisational obstacles to be addressed to improve uptake, and successful implementation of ADs when appropriate. Nevertheless, inferred positive change in attitudes and receptiveness towards the appropriate use of ADs by public funders are supportive and are a stepping stone for the future utilisation of ADs by researchers. Electronic supplementary material The online version of this article (doi:10.1186/s13063-015-0958-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Munyaradzi Dimairo
- School of Health and Related Research, Regent Court, University of Sheffield, 30 Regent Street, S1 4DA, Sheffield, UK.
| | - Jonathan Boote
- School of Health and Related Research, Regent Court, University of Sheffield, 30 Regent Street, S1 4DA, Sheffield, UK. .,Centre for Research in Primary and Community Care, University of Hertfordshire, Hatfield, AL109AB, Hertfordshire, UK.
| | - Steven A Julious
- School of Health and Related Research, Regent Court, University of Sheffield, 30 Regent Street, S1 4DA, Sheffield, UK.
| | - Jonathan P Nicholl
- School of Health and Related Research, Regent Court, University of Sheffield, 30 Regent Street, S1 4DA, Sheffield, UK.
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, Whiteknights, Reading, RG6 6AX, UK.
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Whitehead AL, Julious SA, Cooper CL, Campbell MJ. Estimating the sample size for a pilot randomised trial to minimise the overall trial sample size for the external pilot and main trial for a continuous outcome variable. Stat Methods Med Res 2015; 25:1057-73. [PMID: 26092476 PMCID: PMC4876429 DOI: 10.1177/0962280215588241] [Citation(s) in RCA: 747] [Impact Index Per Article: 83.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Sample size justification is an important consideration when planning a clinical trial, not only for the main trial but also for any preliminary pilot trial. When the outcome is a continuous variable, the sample size calculation requires an accurate estimate of the standard deviation of the outcome measure. A pilot trial can be used to get an estimate of the standard deviation, which could then be used to anticipate what may be observed in the main trial. However, an important consideration is that pilot trials often estimate the standard deviation parameter imprecisely. This paper looks at how we can choose an external pilot trial sample size in order to minimise the sample size of the overall clinical trial programme, that is, the pilot and the main trial together. We produce a method of calculating the optimal solution to the required pilot trial sample size when the standardised effect size for the main trial is known. However, as it may not be possible to know the standardised effect size to be used prior to the pilot trial, approximate rules are also presented. For a main trial designed with 90% power and two-sided 5% significance, we recommend pilot trial sample sizes per treatment arm of 75, 25, 15 and 10 for standardised effect sizes that are extra small (≤0.1), small (0.2), medium (0.5) or large (0.8), respectively.
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Affiliation(s)
- Amy L Whitehead
- Medical Statistics Group, Design, Trials and Statistics Group, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Steven A Julious
- Medical Statistics Group, Design, Trials and Statistics Group, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Cindy L Cooper
- Clinical Trials Research Unit, Design, Trials and Statistics Group, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Michael J Campbell
- Medical Statistics Group, Design, Trials and Statistics Group, School of Health and Related Research, University of Sheffield, Sheffield, UK
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Flight L, Julious SA. The disagreeable behaviour of the kappa statistic. Pharm Stat 2014; 14:74-8. [PMID: 25470361 DOI: 10.1002/pst.1659] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2014] [Revised: 08/14/2014] [Accepted: 10/22/2014] [Indexed: 11/08/2022]
Abstract
It is often of interest to measure the agreement between a number of raters when an outcome is nominal or ordinal. The kappa statistic is used as a measure of agreement. The statistic is highly sensitive to the distribution of the marginal totals and can produce unreliable results. Other statistics such as the proportion of concordance, maximum attainable kappa and prevalence and bias adjusted kappa should be considered to indicate how well the kappa statistic represents agreement in the data. Each kappa should be considered and interpreted based on the context of the data being analysed.
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Affiliation(s)
- Laura Flight
- Medical Statistics Group, University of Sheffield, Sheffield, England
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Clayton S, Apperley E, Hannon F, Karia A, Baxter V, Julious SA. A survey of birth order status of students studying for medical degree at the University of Sheffield. JRSM Open 2014; 5:2054270414533327. [PMID: 25352987 PMCID: PMC4207295 DOI: 10.1177/2054270414533327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Objective It is hypothesised that firstborn children and only children are more intelligent with higher intelligence scores having been observed in firstborn or only children. Evidence of the increased intelligence has been suggested by the fact that 21/23 (91%) of US astronauts, 23/43 (53%) of US presidents and between 75 and 80% of students at Harvard are firstborn or only children. It is of interest to investigate, therefore, whether a high achieving career such as medicine has a disproportionate number of firstborn or only children. Design A survey of medical students. Setting The University of Sheffield Medical School. Participants All students studying medicine in the academic year 2011–2012. Main outcome measures The proportion of firstborn or only children. Results There was a disproportionate number of students who were firstborn or only children: 53% (95% CI 49 to 58%). The expected percentage is 39.8% and therefore we can reject the null hypothesis. The results were consistent across all phases of study. Conclusions There is a higher than expected proportion of medical students at the University of Sheffield who are firstborn or only children. The data though highlight the issue of comparing populations. Here we are comparing a population of medical students with a general population. A comparison which may not be appropriate as medical students may be drawn from a subsample of the general population.
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Affiliation(s)
- Sarah Clayton
- Medical Statistics Group, ScHARR, University of Sheffield, Sheffield S1 4DA, UK
| | - Elizabeth Apperley
- Medical Statistics Group, ScHARR, University of Sheffield, Sheffield S1 4DA, UK
| | - Fergus Hannon
- Medical Statistics Group, ScHARR, University of Sheffield, Sheffield S1 4DA, UK
| | - Anika Karia
- Medical Statistics Group, ScHARR, University of Sheffield, Sheffield S1 4DA, UK
| | - Victoria Baxter
- Medical Statistics Group, ScHARR, University of Sheffield, Sheffield S1 4DA, UK
| | - Steven A Julious
- Medical Statistics Group, ScHARR, University of Sheffield, Sheffield S1 4DA, UK
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Epton T, Norman P, Dadzie AS, Harris PR, Webb TL, Sheeran P, Julious SA, Ciravegna F, Brennan A, Meier PS, Naughton D, Petroczi A, Kruger J, Shah I. A theory-based online health behaviour intervention for new university students (U@Uni): results from a randomised controlled trial. BMC Public Health 2014; 14:563. [PMID: 24903620 PMCID: PMC4067627 DOI: 10.1186/1471-2458-14-563] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Accepted: 05/23/2014] [Indexed: 11/25/2022] Open
Abstract
Background Too few young people engage in behaviours that reduce the risk of morbidity and premature mortality, such as eating healthily, being physically active, drinking sensibly and not smoking. This study sought to assess the efficacy and cost-effectiveness of a theory-based online health behaviour intervention (based on self-affirmation theory, the Theory of Planned Behaviour and implementation intentions) targeting these behaviours in new university students, in comparison to a measurement-only control. Methods Two-weeks before starting university all incoming undergraduates at the University of Sheffield were invited to take part in a study of new students’ health behaviour. A randomised controlled design, with a baseline questionnaire, and two follow-ups (1 and 6 months after starting university), was used to evaluate the intervention. Primary outcomes were measures of the four health behaviours targeted by the intervention at 6-month follow-up, i.e., portions of fruit and vegetables, metabolic equivalent of tasks (physical activity), units of alcohol, and smoking status. Results The study recruited 1,445 students (intervention n = 736, control n = 709, 58% female, Mean age = 18.9 years), of whom 1,107 completed at least one follow-up (23% attrition). The intervention had a statistically significant effect on one primary outcome, smoking status at 6-month follow-up, with fewer smokers in the intervention arm (8.7%) than in the control arm (13.0%; Odds ratio = 1.92, p = .010). There were no significant intervention effects on the other primary outcomes (physical activity, alcohol or fruit and vegetable consumption) at 6-month follow-up. Conclusions The results of the RCT indicate that the online health behaviour intervention reduced smoking rates, but it had little effect on fruit and vegetable intake, physical activity or alcohol consumption, during the first six months at university. However, engagement with the intervention was low. Further research is needed before strong conclusions can be made regarding the likely effectiveness of the intervention to promote health lifestyle habits in new university students. Trial registration Current Controlled Trials, ISRCTN67684181.
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Affiliation(s)
- Tracy Epton
- Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, UK.
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Lee EC, Whitehead AL, Jacques RM, Julious SA. The statistical interpretation of pilot trials: should significance thresholds be reconsidered? BMC Med Res Methodol 2014; 14:41. [PMID: 24650044 PMCID: PMC3994566 DOI: 10.1186/1471-2288-14-41] [Citation(s) in RCA: 217] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2013] [Accepted: 03/12/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In an evaluation of a new health technology, a pilot trial may be undertaken prior to a trial that makes a definitive assessment of benefit. The objective of pilot studies is to provide sufficient evidence that a larger definitive trial can be undertaken and, at times, to provide a preliminary assessment of benefit. METHODS We describe significance thresholds, confidence intervals and surrogate markers in the context of pilot studies and how Bayesian methods can be used in pilot trials. We use a worked example to illustrate the issues raised. RESULTS We show how significance levels other than the traditional 5% should be considered to provide preliminary evidence for efficacy and how estimation and confidence intervals should be the focus to provide an estimated range of possible treatment effects. We also illustrate how Bayesian methods could also assist in the early assessment of a health technology. CONCLUSIONS We recommend that in pilot trials the focus should be on descriptive statistics and estimation, using confidence intervals, rather than formal hypothesis testing and that confidence intervals other than 95% confidence intervals, such as 85% or 75%, be used for the estimation. The confidence interval should then be interpreted with regards to the minimum clinically important difference. We also recommend that Bayesian methods be used to assist in the interpretation of pilot trials. Surrogate endpoints can also be used in pilot trials but they must reliably predict the overall effect on the clinical outcome.
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Affiliation(s)
| | | | | | - Steven A Julious
- Medical Statistics Group, School of Health and Related Research (ScHARR), University of Sheffield, 30 Regent Street, Sheffield S1 4DA, UK.
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Crisp E, Julious SA. The analysis of the use of 'unascertained' for sudden unexpected deaths in infancy from 1988 to 2010. Arch Dis Child 2014; 99:300-1. [PMID: 24265413 DOI: 10.1136/archdischild-2013-305196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Elinor Crisp
- Medical Statistics Group, ScHARR, University of Sheffield, , Sheffield, UK
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Sully BGO, Julious SA, Nicholl J. An investigation of the impact of futility analysis in publicly funded trials. Trials 2014; 15:61. [PMID: 24533447 PMCID: PMC3945066 DOI: 10.1186/1745-6215-15-61] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2013] [Accepted: 01/23/2014] [Indexed: 11/10/2022] Open
Abstract
Background Publicly funded trials regularly fail to recruit their target sample size or find a significant positive result. Adaptive clinical trials which may partly mediate against the problems are not often applied. In this paper we investigate the potential of a form of adaption in a clinical trial - a futility analysis - to see if it has potential to improve publicly funded trials. Methods Outcome data from trials funded by two UK bodies, the Health Technology Assessment (HTA) programme and the UK Medical Research Council (MRC), were collected. These data were then used to simulate each trial with a single futility analysis using conditional power, undertaken after 50% to 90% of the patients had been recruited. Thirty-three trials recruiting between 2002 and 2008 met the inclusion criteria. Stopping boundaries of conditional powers of 20%, 30% and 40% were considered and outcomes included the number of trials successfully stopped and number of patients saved. Results Inclusion of a futility analysis after 75% of the patients had been recruited would have potentially resulted in 10 trials, which went on to have negative results, correctly stopping for futility using a stopping boundary of 30%. A total of 807 patients across all the trials would potentially have been saved using these futility parameters. The proportion of studies successfully recruiting would also have increased from 45% to 64%. Conclusions A futility assessment has the potential to increase efficiency, save patients and decrease costs in publicly funded trials. While there are logistical issues in undertaking futility assessments we recommend that investigators should aim to include a futility analysis in their trial design wherever possible.
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Affiliation(s)
| | - Steven A Julious
- Medical Statistics Group, School of Health and Related Research, University of Sheffield, 30 Regent Court, Regent Street, Sheffield S1 4DA, UK.
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Horspool MJ, Julious SA, Boote J, Bradburn MJ, Cooper CL, Davis S, Elphick H, Norman P, Smithson WH, vanStaa T. Preventing and lessening exacerbations of asthma in school-age children associated with a new term (PLEASANT): study protocol for a cluster randomised control trial. Trials 2013; 14:297. [PMID: 24041259 PMCID: PMC4016495 DOI: 10.1186/1745-6215-14-297] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2013] [Accepted: 09/04/2013] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Within the UK, during September, there is a pronounced increase in the number of unscheduled medical contacts by school-aged children (4-16 years) with asthma. It is thought that that this might be caused by the return back to school after the summer holidays, suddenly mixing with other children again and picking up viruses which could affect their asthma. There is also a drop in the number of prescriptions administered in August. It is possible therefore that children might not be taking their medication as they should during the summer contributing to them becoming ill when they return to school.It is hoped that a simple intervention from the GP to parents of children with asthma at the start of the summer holiday period, highlighting the importance of maintaining asthma medication can help prevent increased asthma exacerbation, and unscheduled NHS appointments, following return to school in September. METHODS/DESIGN PLEASANT is a cluster randomised trial. A total of 140 General Practices (GPs) will be recruited into the trial; 70 GPs randomised to the intervention and 70 control practices of "usual care". An average practice is expected to have approximately 100 children (aged 4-16 with a diagnosis of asthma) hence observational data will be collected on around 14000 children over a 24-month period. The Clinical Practice Research Datalink will collect all data required for the study which includes diagnostic, prescription and referral data. DISCUSSION The trial will assess whether the intervention can reduce exacerbation of asthma and unscheduled medical contacts in school-aged children associated with the return to school after the summer holidays. It has the potential to benefit the health and quality of life of children with asthma while also improving the effectiveness of NHS services by reducing NHS use in one of the busiest months of the year.An exploratory health economic analysis will gauge any cost saving associated with the intervention and subsequent impacts on quality of life. If results for the intervention are positive it is hoped that this could be adopted as part of routine care management of childhood asthma in general practice. TRIAL REGISTRATION Current controlled trials: ISRCTN03000938 (assigned 19/10/12) http://www.controlled-trials.com/ISRCTN03000938/. UKCRN ID 13572.
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Affiliation(s)
- Michelle J Horspool
- Clinical Trials Research Unit, University of Sheffield, School of Health and Related Research, 30 Regent Street, Sheffield S1 4DA, UK
| | - Steven A Julious
- Medical Statistics Group, University of Sheffield, School of Health and Related Research, 30 Regent Street, Sheffield S1 4DA, UK
| | - Jonathan Boote
- Design, Trials and Statistics, University of Sheffield, School of Health and Related Research, 30 Regent Street, Sheffield S1 4DA, UK
| | - Mike J Bradburn
- Clinical Trials Research Unit, University of Sheffield, School of Health and Related Research, 30 Regent Street, Sheffield S1 4DA, UK
| | - Cindy L Cooper
- Clinical Trials Research Unit, University of Sheffield, School of Health and Related Research, 30 Regent Street, Sheffield S1 4DA, UK
| | - Sarah Davis
- Health Economics and Decision Science, University of Sheffield, School of Health and Related Research, 30 Regent Street, Sheffield S1 4DA, UK
| | - Heather Elphick
- Department of Paediatric Respiratory Medicine, Sheffield Children’s Hospital, Western Bank, Sheffield S10 2TH, UK
| | - Paul Norman
- Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, UK
| | - W Henry Smithson
- Academic Unit of Primary Medical Care, University of Sheffield, Samuel Fox House, Northern General Hospital, Herries Road, Sheffield S5 7AU, UK
| | - Tjeerd vanStaa
- Clinical Practice Research Datalink, Medicines and Healthcare Products Regulatory Agency, 5th Floor, 151 Buckingham Palace Road, Victoria, London SW1W 9SZ, UK
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