1
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Law M, Couturier DL, Choodari-Oskooei B, Crout P, Gamble C, Jacko P, Pallmann P, Pilling M, Robertson DS, Robling M, Sydes MR, Villar SS, Wason J, Wheeler G, Williamson SF, Yap C, Jaki T. Medicines and Healthcare products Regulatory Agency's "Consultation on proposals for legislative changes for clinical trials": a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing. Trials 2023; 24:640. [PMID: 37798805 PMCID: PMC10552399 DOI: 10.1186/s13063-023-07576-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 08/04/2023] [Indexed: 10/07/2023] Open
Abstract
In the UK, the Medicines and Healthcare products Regulatory Agency consulted on proposals "to improve and strengthen the UK clinical trials legislation to help us make the UK the best place to research and develop safe and innovative medicines". The purpose of the consultation was to help finalise the proposals and contribute to the drafting of secondary legislation. We discussed these proposals as members of the Trials Methodology Research Partnership Adaptive Designs Working Group, which is jointly funded by the Medical Research Council and the National Institute for Health and Care Research. Two topics arose frequently in the discussion: the emphasis on legislation, and the absence of questions on data sharing. It is our opinion that the proposals rely heavily on legislation to change practice. However, clinical trials are heterogeneous, and as a result some trials will struggle to comply with all of the proposed legislation. Furthermore, adaptive design clinical trials are even more heterogeneous than their non-adaptive counterparts, and face more challenges. Consequently, it is possible that increased legislation could have a greater negative impact on adaptive designs than non-adaptive designs. Overall, we are sceptical that the introduction of legislation will achieve the desired outcomes, with some exceptions. Meanwhile the topic of data sharing - making anonymised individual-level clinical trial data available to other investigators for further use - is entirely absent from the proposals and the consultation in general. However, as an aspect of the wider concept of open science and reproducible research, data sharing is an increasingly important aspect of clinical trials. The benefits of data sharing include faster innovation, improved surveillance of drug safety and effectiveness and decreasing participant exposure to unnecessary risk. There are already a number of UK-focused documents that discuss and encourage data sharing, for example, the Concordat on Open Research Data and the Medical Research Council's Data Sharing Policy. We strongly suggest that data sharing should be the norm rather than the exception, and hope that the forthcoming proposals on clinical trials invite discussion on this important topic.
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Affiliation(s)
- Martin Law
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
- Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK.
| | - Dominique-Laurent Couturier
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | | | - Phillip Crout
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Carrol Gamble
- Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK
| | - Peter Jacko
- Lancaster University Management School, Lancaster University, Lancaster, UK
- Berry Consultants, Abingdon, UK
| | | | - Mark Pilling
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - David S Robertson
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | | | - Matthew R Sydes
- University College London, London, UK
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Sofía S Villar
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - James Wason
- Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Graham Wheeler
- Imperial Clinical Trials Unit, Imperial College London, London, W12 7RH, UK
| | - S Faye Williamson
- Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Christina Yap
- Clinical Trials and Statistics Unit, The Institute of Cancer Research, London, UK
| | - Thomas Jaki
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Faculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany
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2
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Wu Y, Sun Y, Liu Y, Levis B, Krishnan A, He C, Neupane D, Patten SB, Cuijpers P, Ziegelstein RC, Benedetti A, Thombs BD. Depression screening tool accuracy individual participant data meta-analyses: data contribution was associated with multiple factors. J Clin Epidemiol 2023; 162:63-71. [PMID: 37619800 DOI: 10.1016/j.jclinepi.2023.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/12/2023] [Accepted: 08/16/2023] [Indexed: 08/26/2023]
Abstract
OBJECTIVES To examine the proportion of eligible primary studies that contributed data, study characteristics associated with data contribution, and reasons for noncontribution using diagnostic test accuracy Individual Participant Data Meta-Analysis (IPDMA) data sets from the DEPRESsion Screening Data project. STUDY DESIGN AND SETTING We reviewed data set contributions from four IPDMAs. A multivariable logistic regression model was fitted to evaluate study factors associated with data contribution. RESULTS Of 456 eligible studies from four included IPDMAs, 295 (65%) contributed data. More recent year of publication and higher journal impact factor were associated with greater odds of data contribution. Studies conducted in Europe (excluding the United Kingdom), Oceania, Canada, the Middle East, Africa, and Central or South America (reference = the United States), that have recruitment from inpatient care or nonmedical settings (reference = outpatient), that reported screening accuracy results, or that drew negative conclusions (reference = positive conclusions) were more likely to contribute data. Studies of the Geriatric Depression Scale (reference = the Patient Health Questionnaire) or lacking funding information were negatively associated with data contribution. Over 80% of noncontributions were due to authors being unreachable or data being unavailable. CONCLUSION The study identified factors associated with data contribution that may support future research to promote data contribution to IPDMAs.
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Affiliation(s)
- Yin Wu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Ying Sun
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Yi Liu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Brooke Levis
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, UK
| | - Ankur Krishnan
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Chen He
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Dipika Neupane
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Scott B Patten
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Pim Cuijpers
- Department of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Roy C Ziegelstein
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Andrea Benedetti
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada; Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montreal, Quebec, Canada; Department of Medicine, McGill University, Montreal, Quebec, Canada
| | - Brett D Thombs
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada; Department of Medicine, McGill University, Montreal, Quebec, Canada; Department of Psychology, McGill University, Montreal, Quebec, Canada; Department of Educational and Counselling Psychology, McGill University, Montreal, Quebec, Canada; Biomedical Ethics Unit, McGill University, Montreal, Quebec, Canada.
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3
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van Wijk RC, Imperial MZ, Savic RM, Solans BP. Pharmacokinetic analysis across studies to drive knowledge-integration: A tutorial on individual patient data meta-analysis (IPDMA). CPT Pharmacometrics Syst Pharmacol 2023; 12:1187-1200. [PMID: 37303132 PMCID: PMC10508576 DOI: 10.1002/psp4.13002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 05/10/2023] [Accepted: 05/16/2023] [Indexed: 06/13/2023] Open
Abstract
Answering challenging questions in drug development sometimes requires pharmacokinetic (PK) data analysis across different studies, for example, to characterize PKs across diverse regions or populations, or to increase statistical power for subpopulations by combining smaller size trials. Given the growing interest in data sharing and advanced computational methods, knowledge integration based on multiple data sources is increasingly applied in the context of model-informed drug discovery and development. A powerful analysis method is the individual patient data meta-analysis (IPDMA), leveraging systematic review of databases and literature, with the most detailed data type of the individual patient, and quantitative modeling of the PK processes, including capturing heterogeneity of variance between studies. The methodology that should be used in IPDMA in the context of population PK analysis is summarized in this tutorial, highlighting areas of special attention compared to standard PK modeling, including hierarchical nested variability terms for interstudy variability, and handling between-assay differences in limits of quantification within a single analysis. This tutorial is intended for any pharmacological modeler who is interested in performing an integrated analysis of PK data across different studies in a systematic and thorough manner, to answer questions that transcend individual primary studies.
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Affiliation(s)
- Rob C. van Wijk
- University of California San Francisco Schools of Pharmacy and MedicineSan FranciscoCaliforniaUSA
- UCSF Center for Tuberculosis, University of California San FranciscoSan FranciscoCaliforniaUSA
| | - Marjorie Z. Imperial
- University of California San Francisco Schools of Pharmacy and MedicineSan FranciscoCaliforniaUSA
- UCSF Center for Tuberculosis, University of California San FranciscoSan FranciscoCaliforniaUSA
| | - Radojka M. Savic
- University of California San Francisco Schools of Pharmacy and MedicineSan FranciscoCaliforniaUSA
- UCSF Center for Tuberculosis, University of California San FranciscoSan FranciscoCaliforniaUSA
| | - Belén P. Solans
- University of California San Francisco Schools of Pharmacy and MedicineSan FranciscoCaliforniaUSA
- UCSF Center for Tuberculosis, University of California San FranciscoSan FranciscoCaliforniaUSA
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4
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McCarthy M, Gillies K, Rousseau N, Wade J, Gamble C, Toomey E, Matvienko-Sikar K, Sydes M, Dowling M, Bryant V, Biesty L, Houghton C. Qualitative data sharing practices in clinical trials in the UK and Ireland: towards the production of good practice guidance. HRB Open Res 2023; 6:10. [PMID: 37456658 PMCID: PMC10345597 DOI: 10.12688/hrbopenres.13667.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/27/2023] [Indexed: 08/17/2023] Open
Abstract
Background: Data sharing enables researchers to conduct novel research with previously collected datasets, thus maximising scientific findings and cost effectiveness, and reducing research waste. The value of sharing, even de-identified, quantitative data from clinical trials is well recognised with a moderated access approach recommended. While substantial challenges to sharing quantitative data remain, there are additional challenges for sharing qualitative data in trials. Incorporating the necessary information about how qualitative data will be shared into already complex trial recruitment and consent processes proves challenging. The aim of this study was to explore whether and how trial teams share qualitative data collected as part of the design, conduct, analysis, or delivery of clinical trials. Methods: Phase 1 involved semi-structured, in-depth qualitative interviews and focus groups with key trial stakeholder groups including trial managers and clinical trialists (n=3), qualitative researchers in trials (n=9), members of research funding bodies (n=2) and trial participants (n=1). Data were analysed using thematic analysis. In Phase 2, we conducted a content analysis of 16 participant information leaflets (PIL) and consent forms (CF) for trials that collected qualitative data. Results: Three key themes were identified from our Phase 1 findings: ' Understanding and experiences of the potential benefits of sharing qualitative data from trials', 'Concerns about qualitative data sharing', and ' Future guidance and funding'. In phase 2, the PILs and CFs received revealed that the benefits of data sharing for participants were only explained in two of the study documents. Conclusions: The value of sharing qualitative data was acknowledged, but there are many uncertainties as to how, when, and where to share this data. In addition, there were ethical concerns in relation to the consent process required for qualitative data sharing in trials. This study provides insight into the existing practice of qualitative data sharing in trials.
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Affiliation(s)
- Megan McCarthy
- School of Nursing and Midwifery, University College Cork, Cork, Ireland
| | - Katie Gillies
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK
| | - Nikki Rousseau
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Julia Wade
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Carrol Gamble
- Health Data Science, University of Liverpool, Liverpool, UK
| | - Elaine Toomey
- School of Allied Health, University of Limerick, Limerick, Ireland
| | | | - Matthew Sydes
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, UK
- BHF Data Science Centre, Health Data Research UK, London, UK
| | - Maura Dowling
- School of Nursing and Midwifery, University of Galway, Galway, Ireland
| | - Val Bryant
- No particular affiliation, No particular affiliation, UK
| | - Linda Biesty
- School of Nursing and Midwifery, University of Galway, Galway, Ireland
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5
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DeVito NJ, Morton C, Cashin AG, Richards GC, Lee H. Sharing study materials in health and medical research. BMJ Evid Based Med 2022:bmjebm-2022-111987. [PMID: 36162960 DOI: 10.1136/bmjebm-2022-111987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/03/2022] [Indexed: 11/04/2022]
Abstract
Making study materials available allows for a more comprehensive understanding of the scientific literature. Sharing can take many forms and include a wide variety of outputs including code and data. Biomedical research can benefit from increased transparency but faces unique challenges for sharing, for instance, confidentiality concerns around participants' medical data. Both general and specialised repositories exist to aid in sharing most study materials. Sharing may also require skills and resources to ensure that it is done safely and effectively. Educating researchers on how to best share their materials, and properly rewarding these practices, requires action from a variety of stakeholders including journals, funders and research institutions.
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Affiliation(s)
- Nicholas J DeVito
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Caroline Morton
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Aidan Gregory Cashin
- School of Health Sciences, University of New South Wales, Sydney, New South Wales, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Randwick, New South Wales, Australia
| | - Georgia C Richards
- Centre for Evidence Based Medicine, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Hopin Lee
- Centre for Statistics in Medicine & Rehabilitation Research in Oxford, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, Oxfordshire, UK
- School of Medicine and Public Health, The University of Newcastle, Callaghan, New South Wales, Australia
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6
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Ohmann C, Moher D, Siebert M, Motschall E, Naudet F. Status, use and impact of sharing individual participant data from clinical trials: a scoping review. BMJ Open 2021; 11:e049228. [PMID: 34408052 PMCID: PMC8375721 DOI: 10.1136/bmjopen-2021-049228] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVES To explore the impact of data-sharing initiatives on the intent to share data, on actual data sharing, on the use of shared data and on research output and impact of shared data. ELIGIBILITY CRITERIA All studies investigating data-sharing practices for individual participant data (IPD) from clinical trials. SOURCES OF EVIDENCE We searched the Medline database, the Cochrane Library, the Science Citation Index Expanded and the Social Sciences Citation Index via Web of Science, and preprints and proceedings of the International Congress on Peer Review and Scientific Publication. In addition, we inspected major clinical trial data-sharing platforms, contacted major journals/publishers, editorial groups and some funders. CHARTING METHODS Two reviewers independently extracted information on methods and results from resources identified using a standardised questionnaire. A map of the extracted data was constructed and accompanied by a narrative summary for each outcome domain. RESULTS 93 studies identified in the literature search (published between 2001 and 2020, median: 2018) and 5 from additional information sources were included in the scoping review. Most studies were descriptive and focused on early phases of the data-sharing process. While the willingness to share IPD from clinical trials is extremely high, actual data-sharing rates are suboptimal. A survey of journal data suggests poor to moderate enforcement of the policies by publishers. Metrics provided by platforms suggest that a large majority of data remains unrequested. When requested, the purpose of the reuse is more often secondary analyses and meta-analyses, rarely re-analyses. Finally, studies focused on the real impact of data-sharing were rare and used surrogates such as citation metrics. CONCLUSIONS There is currently a gap in the evidence base for the impact of IPD sharing, which entails uncertainties in the implementation of current data-sharing policies. High level evidence is needed to assess whether the value of medical research increases with data-sharing practices.
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Affiliation(s)
- Christian Ohmann
- European Clinical Research Infrastructure Network, Paris, France
| | - David Moher
- Ottawa Methods Centre, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Maximilian Siebert
- CHU Rennes, CIC 1414 (Centre d'Investigation Clinique de Rennes), University Rennes, Rennes, France
| | - Edith Motschall
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Baden-Württemberg, Germany
| | - Florian Naudet
- CHU Rennes, INSERM CIC 1414 (Centre d'Investigation Clinique de Rennes), University Rennes, Rennes, Bretagne, France
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7
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Houghton C, McCarthy M, Gillies K, Rousseau N, Wade J, Gamble C, Toomey E, Matvienko-Sikar K, Sydes M, Dowling M, Bryant V, Biesty L. A study protocol of qualitative data sharing practices in clinical trials in the UK and Ireland: towards the production of good practice guidance. HRB Open Res 2021; 4:47. [PMID: 34124575 PMCID: PMC8167499 DOI: 10.12688/hrbopenres.13269.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/14/2021] [Indexed: 02/02/2023] Open
Abstract
Background: Data sharing enables researchers to conduct novel research with previously collected data sets, thus maximising scientific findings and cost effectiveness, and reducing research waste. The value of sharing anonymised data from clinical trials is well recognised with a moderated access approach recommended. While substantial challenges to data sharing remain, there are additional challenges for qualitative data. Qualitative data including videos, interviews, and observations are often more readily identifiable than quantitative data. Existing guidance from UK Economic and Social Research Council applies to sharing qualitative data but does not address the additional challenges related to sharing qualitative data collected within trials, including the need to incorporate the necessary information and consent into already complex recruitment processes, with the additional sensitive nature of health-related data. Methods: Work package 1 will involve separate focus group interviews with members of each stakeholder group: trial managers, clinical trialists, qualitative researchers, members of research funding bodies and trial participants who have been involved in qualitative research. Data will be analysed using thematic analysis and managed within QSR NVivo to enhance transparency. Work package 2 will involve a documentary analysis of current consent procedures for qualitative data collected as part of the conduct of clinical trials. We will include documents such as participant information leaflets and consent forms for the qualitative components in trials. We will extract data such as whether specific clauses for data sharing are included in the consent form. Content analysis will be used to analyse whether and how consent is being obtained for qualitative data sharing. Conclusions: This study will provide insight into the existing practice of sharing of qualitative data in clinical trials and the current issues and opportunities, to help shape future research and development of guidance to encourage maximum learning to be gained from this valuable data.
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Affiliation(s)
- Catherine Houghton
- School of Nursing and Midwifery, National University of Ireland Galway, Galway, Ireland
| | - Megan McCarthy
- School of Nursing and Midwifery, National University of Ireland Galway, Galway, Ireland
| | - Katie Gillies
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK
| | - Nikki Rousseau
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Julia Wade
- Bristol Population Health Science Institute, University of Bristol, Bristol, UK
| | - Carrol Gamble
- Health Data Science, University of Liverpool, Liverpool, UK
| | - Elaine Toomey
- School of Allied Health, University of Limerick, Limerick, Ireland
| | | | - Matthew Sydes
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK
| | - Maura Dowling
- School of Nursing and Midwifery, National University of Ireland Galway, Galway, Ireland
| | - Val Bryant
- No particular affiliation, No particular affiliation, UK
| | - Linda Biesty
- School of Nursing and Midwifery, National University of Ireland Galway, Galway, Ireland
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8
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Houghton C, McCarthy M, Gillies K, Rousseau N, Wade J, Gamble C, Toomey E, Matvienko-Sikar K, Sydes M, Dowling M, Bryant V, Biesty L. A study protocol of qualitative data sharing practices in clinical trials in the UK and Ireland: towards the production of good practice guidance. HRB Open Res 2021; 4:47. [PMID: 34124575 PMCID: PMC8167499 DOI: 10.12688/hrbopenres.13269.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2021] [Indexed: 02/02/2023] Open
Abstract
Background: Data sharing enables researchers to conduct novel research with previously collected data sets, thus maximising scientific findings and cost effectiveness, and reducing research waste. The value of sharing anonymised data from clinical trials is well recognised with a moderated access approach recommended. While substantial challenges to data sharing remain, there are additional challenges for qualitative data. Qualitative data including videos, interviews, and observations are often more readily identifiable than quantitative data. Existing guidance from UK Economic and Social Research Council applies to sharing qualitative data but does not address the additional challenges related to sharing qualitative data collected within trials, including the need to incorporate the necessary information and consent into already complex recruitment processes, with the additional sensitive nature of health-related data. Methods: Work package 1 will involve separate focus group interviews with members of each stakeholder group: trial managers, clinical trialists, qualitative researchers, members of research funding bodies and trial participants who have been involved in qualitative research. Data will be analysed using thematic analysis and managed within QSR NVivo to enhance transparency. Work package 2 will involve a documentary analysis of current consent procedures for qualitative data collected as part of the conduct of clinical trials. We will include documents such as participant information leaflets and consent forms for the qualitative components in trials. We will extract data such as whether specific clauses for data sharing are included in the consent form. Content analysis will be used to analyse whether and how consent is being obtained for qualitative data sharing. Conclusions: This study will provide insight into the existing practice of sharing of qualitative data in clinical trials and the current issues and opportunities, to help shape future research and development of guidance to encourage maximum learning to be gained from this valuable data.
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Affiliation(s)
- Catherine Houghton
- School of Nursing and Midwifery, National University of Ireland Galway, Galway, Ireland
| | - Megan McCarthy
- School of Nursing and Midwifery, National University of Ireland Galway, Galway, Ireland
| | - Katie Gillies
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK
| | - Nikki Rousseau
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Julia Wade
- Bristol Population Health Science Institute, University of Bristol, Bristol, UK
| | - Carrol Gamble
- Health Data Science, University of Liverpool, Liverpool, UK
| | - Elaine Toomey
- School of Allied Health, University of Limerick, Limerick, Ireland
| | | | - Matthew Sydes
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK
| | - Maura Dowling
- School of Nursing and Midwifery, National University of Ireland Galway, Galway, Ireland
| | - Val Bryant
- No particular affiliation, No particular affiliation, UK
| | - Linda Biesty
- School of Nursing and Midwifery, National University of Ireland Galway, Galway, Ireland
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9
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Rollando P, Parc C, Naudet F, Gaba JF. [Data sharing policies of clinical trials funders in France]. Therapie 2020; 75:527-536. [PMID: 32446662 DOI: 10.1016/j.therap.2020.04.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 01/09/2020] [Accepted: 04/03/2020] [Indexed: 11/16/2022]
Abstract
AIMS The aims of this survey were to evaluate the percentage of French clinical trial funders with a data sharing policy, to describe their data sharing policies and, more generally, the transparency of the research they fund. METHODS The different funders of clinical trials in France have been identified from 3 lists of tenders for clinical research projects: the internal list of the University Hospital Center (CHU) of Rennes, the list of the Interregional Group for Clinical Research and Innovation (GIRCI EST), the list of the portal for calls for projects in health research. Funders were contacted, first by email and then by phone (at least two email and/or phone reminders) to respond to an online survey via Google form. The questionnaire aimed to assess the existence of a sharing policy or not, as well as the way in which it was set up. RESULTS Out of 190 funders contacted, 94 did not respond. Sixty-five of the respondents were excluded because they did not fund clinical trials. Of the 31 funders included (including Direction générale de l'offre de soins [DGOS], Institut national contre le cancer [INCa], Groupement Interrégional de Recherche Clinique et d'Innovation [GIRCIs]), only 9 (29%) had implemented a data sharing policy. Among these nine funders, only one had a mandatory sharing policy and eight a policy supporting but not enforcing data sharing. Five allowed the use of budget lines dedicated to data sharing. Three reported granting data sharing incentives. Three had dedicated guidelines indicating a specific mode of sharing data (sharing on request and/or on a specialized platform) and specifying the type of data (individual patient data and/or protocol and amendments). For all three, there were restrictions on sharing data to researchers only. Data sharing policies concerned 19% of the total financial volume (850,032,000 euros) of the 26 funders who reported this information. CONCLUSION Despite international interest in clinical trial data sharing practices, clinical trials funders with a strong data-sharing policy remain an exception in France.
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Affiliation(s)
- Pauline Rollando
- Inserm, CIC 1414 (centre d'investigation clinique de Rennes), université Rennes, CHU Rennes, 35000 Rennes, France.
| | - Céline Parc
- Direction de la recherche et de l'innovation (DRI), CHU Rennes, 35000 Rennes, France
| | - Florian Naudet
- Inserm, CIC 1414 (centre d'investigation clinique de Rennes), université Rennes, CHU Rennes, 35000 Rennes, France
| | - Jeanne Fabiola Gaba
- Inserm, CIC 1414 (centre d'investigation clinique de Rennes), université Rennes, CHU Rennes, 35000 Rennes, France; REcherche en Pharmaco-Épidémiologie et REcours aux Soins (REPERES), 35000 Rennes, France
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10
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Ventresca M, Schünemann HJ, Macbeth F, Clarke M, Thabane L, Griffiths G, Noble S, Garcia D, Marcucci M, Iorio A, Zhou Q, Crowther M, Akl EA, Lyman GH, Gloy V, DiNisio M, Briel M. Obtaining and managing data sets for individual participant data meta-analysis: scoping review and practical guide. BMC Med Res Methodol 2020; 20:113. [PMID: 32398016 PMCID: PMC7218569 DOI: 10.1186/s12874-020-00964-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 03/30/2020] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Shifts in data sharing policy have increased researchers' access to individual participant data (IPD) from clinical studies. Simultaneously the number of IPD meta-analyses (IPDMAs) is increasing. However, rates of data retrieval have not improved. Our goal was to describe the challenges of retrieving IPD for an IPDMA and provide practical guidance on obtaining and managing datasets based on a review of the literature and practical examples and observations. METHODS We systematically searched MEDLINE, Embase, and the Cochrane Library, until January 2019, to identify publications focused on strategies to obtain IPD. In addition, we searched pharmaceutical websites and contacted industry organizations for supplemental information pertaining to recent advances in industry policy and practice. Finally, we documented setbacks and solutions encountered while completing a comprehensive IPDMA and drew on previous experiences related to seeking and using IPD. RESULTS Our scoping review identified 16 articles directly relevant for the conduct of IPDMAs. We present short descriptions of these articles alongside overviews of IPD sharing policies and procedures of pharmaceutical companies which display certification of Principles for Responsible Clinical Trial Data Sharing via Pharmaceutical Research and Manufacturers of America or European Federation of Pharmaceutical Industries and Associations websites. Advances in data sharing policy and practice affected the way in which data is requested, obtained, stored and analyzed. For our IPDMA it took 6.5 years to collect and analyze relevant IPD and navigate additional administrative barriers. Delays in obtaining data were largely due to challenges in communication with study sponsors, frequent changes in data sharing policies of study sponsors, and the requirement for a diverse skillset related to research, administrative, statistical and legal issues. CONCLUSIONS Knowledge of current data sharing practices and platforms as well as anticipation of necessary tasks and potential obstacles may reduce time and resources required for obtaining and managing data for an IPDMA. Sufficient project funding and timeline flexibility are pre-requisites for successful collection and analysis of IPD. IPDMA researchers must acknowledge the additional and unexpected responsibility they are placing on corresponding study authors or data sharing administrators and should offer assistance in readying data for sharing.
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Affiliation(s)
- Matthew Ventresca
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario Canada
| | - Holger J. Schünemann
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario Canada
| | - Fergus Macbeth
- Centre for Trials Research, School of Medicine, Cardiff University, Cardiff, Wales, UK
| | - Mike Clarke
- Northern Ireland Hub for Trials Methodology Research and Cochrane Individual Participant Data Meta-analysis Methods Group, Queen’s University Belfast, Belfast, UK
| | - Lehana Thabane
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario Canada
| | - Gareth Griffiths
- Wales Cancer Trials Unit, School of Medicine, Cardiff University, Wales, UK; Faculty of Medicine, University of Southampton, Southampton General Hospital, Southampton, UK
| | - Simon Noble
- Marie Curie Palliative Care Research Centre, Cardiff University, Cardiff, Wales, UK
| | - David Garcia
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Maura Marcucci
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario Canada
- Department of Medicine, McMaster University, Hamilton, Ontario Canada
| | - Alfonso Iorio
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario Canada
- Department of Medicine, McMaster University, Hamilton, Ontario Canada
| | - Qi Zhou
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario Canada
| | - Mark Crowther
- Department of Medicine, McMaster University, Hamilton, Ontario Canada
| | - Elie A. Akl
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario Canada
- Department of Internal Medicine, American University of Beirut, Beirut, Lebanon
| | - Gary H. Lyman
- Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA
- Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center, Seattle, Washington USA
| | - Viktoria Gloy
- Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University of Basel and University Hospital Basel, Basel, Switzerland
| | - Marcello DiNisio
- Department of Medicine and Ageing Sciences, University G. D’Annunzio, Chieti-Pescara, Italy
| | - Matthias Briel
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario Canada
- Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University of Basel and University Hospital Basel, Basel, Switzerland
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11
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Azar M, Benedetti A, Riehm KE, Imran M, Krishnan A, Chiovitti M, Sanchez T, Shrier I, Thombs BD. Individual participant data meta-analyses (IPDMA): data contribution was associated with trial corresponding author country, publication year, and journal impact factor. J Clin Epidemiol 2020; 124:16-23. [PMID: 32298776 DOI: 10.1016/j.jclinepi.2020.03.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 03/01/2020] [Accepted: 03/13/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVES The objectives were to determine the proportion of eligible randomized controlled trials (RCTs) that contributed data to individual participant data meta-analyses (IPDMAs) and explore associated factors. STUDY DESIGN AND SETTING IPDMAs with ≥10 eligible RCTs were identified by searching MEDLINE, EMBASE, CINAHL, and Cochrane May 1, 2015 to February 13, 2017. Mixed-effect logistic regression was used to identify factors associated with data contribution. RESULTS Of 774 eligible RCTs from 35 included IPDMAs, 517 (67%, 95% confidence interval [CI]: 63%-70%) contributed data. Compared to RCTs from journals with low-impact factors (0-2.4), RCTs from journals with higher impact factors were more likely to contribute data: impact factor 5.0-9.9, odds ratio [OR] 2.6, 95% CI: 1.37-4.86; impact factor: 10.0-19.9, OR: 5.7, 95% CI: 3.0-10.8; impact factor >20.0, OR: 4.6, 95% CI: 1.9-11.4. RCTs from the United Kingdom were more likely to contribute data than those from the United States (reference; OR: 2.4, 95% CI, 1.3-4.6). There was an increase in OR per publication year (OR: 1.05, 95% CI: 1.02-1.09). CONCLUSION The country where RCTs are conducted, impact factor of the journal where RCTs are published, and RCT publication year were associated with data contribution in IPDMAs with ≥10 eligible RCTs.
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Affiliation(s)
- Marleine Azar
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Andrea Benedetti
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada; Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montreal, Quebec, Canada
| | - Kira E Riehm
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Mahrukh Imran
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Ankur Krishnan
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Matthew Chiovitti
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Tatiana Sanchez
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Ian Shrier
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Brett D Thombs
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Medicine, McGill University, Montreal, Quebec, Canada; Department of Psychology, McGill University, Montreal, Quebec, Canada; Department of Educational and Counselling Psychology, McGill University, Montreal, Quebec, Canada; Biomedical Ethics Unit, McGill University, Montreal, Quebec, Canada.
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12
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Clinical trial data reuse - overcoming complexities in trial design and data sharing. Trials 2019; 20:513. [PMID: 31426840 PMCID: PMC6701093 DOI: 10.1186/s13063-019-3627-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 07/31/2019] [Indexed: 11/10/2022] Open
Abstract
There are many acknowledged benefits for the reuse of clinical trial data; from independent verification of published results to the evaluation of new hypotheses. However, the reuse of shared clinical trial data is not without obstacles. Here we present some of the issues and lessons learned from our own experiences in accessing and analyzing trial data; specifically, where we aim to combine and pool data from multiple different trials. In addition to issues around missing annotation and incomplete datasets, we identify trial-design complexity as a potential hurdle that may complicate downstream analyses. We address potential solutions and emphasize the need for benefits of transparent sharing and analysis of participant-level clinical trial data with appropriate risk mitigation, a matter important to efficient clinical research.
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13
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Hajduk GK, Jamieson NE, Baker BL, Olesen OF, Lang T. It is not enough that we require data to be shared; we have to make sharing easy, feasible and accessible too! BMJ Glob Health 2019; 4:e001550. [PMID: 31406588 PMCID: PMC6666804 DOI: 10.1136/bmjgh-2019-001550] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 05/30/2019] [Accepted: 06/08/2019] [Indexed: 12/21/2022] Open
Affiliation(s)
| | - Nina E Jamieson
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Bonny L Baker
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Ole F Olesen
- International Cooperation Europe, European and Developing Countries Clinical Trials Partnership, The Hague, The Netherlands
| | - Trudie Lang
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
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14
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Kuntz RE, Antman EM, Califf RM, Ingelfinger JR, Krumholz HM, Ommaya A, Peterson ED, Ross JS, Waldstreicher J, Wang SV, Zarin DA, Whicher DM, Siddiqi SM, Lopez MH. Individual Patient-Level Data Sharing for Continuous Learning: A Strategy for Trial Data Sharing. NAM Perspect 2019; 2019:201906b. [PMID: 34532668 DOI: 10.31478/201906b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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15
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Naudet F, Sakarovitch C, Janiaud P, Cristea I, Fanelli D, Moher D, Ioannidis JPA. Data sharing and reanalysis of randomized controlled trials in leading biomedical journals with a full data sharing policy: survey of studies published in The BMJ and PLOS Medicine. BMJ 2018; 360:k400. [PMID: 29440066 PMCID: PMC5809812 DOI: 10.1136/bmj.k400] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
OBJECTIVES To explore the effectiveness of data sharing by randomized controlled trials (RCTs) in journals with a full data sharing policy and to describe potential difficulties encountered in the process of performing reanalyses of the primary outcomes. DESIGN Survey of published RCTs. SETTING PubMed/Medline. ELIGIBILITY CRITERIA RCTs that had been submitted and published by The BMJ and PLOS Medicine subsequent to the adoption of data sharing policies by these journals. MAIN OUTCOME MEASURE The primary outcome was data availability, defined as the eventual receipt of complete data with clear labelling. Primary outcomes were reanalyzed to assess to what extent studies were reproduced. Difficulties encountered were described. RESULTS 37 RCTs (21 from The BMJ and 16 from PLOS Medicine) published between 2013 and 2016 met the eligibility criteria. 17/37 (46%, 95% confidence interval 30% to 62%) satisfied the definition of data availability and 14 of the 17 (82%, 59% to 94%) were fully reproduced on all their primary outcomes. Of the remaining RCTs, errors were identified in two but reached similar conclusions and one paper did not provide enough information in the Methods section to reproduce the analyses. Difficulties identified included problems in contacting corresponding authors and lack of resources on their behalf in preparing the datasets. In addition, there was a range of different data sharing practices across study groups. CONCLUSIONS Data availability was not optimal in two journals with a strong policy for data sharing. When investigators shared data, most reanalyses largely reproduced the original results. Data sharing practices need to become more widespread and streamlined to allow meaningful reanalyses and reuse of data. TRIAL REGISTRATION Open Science Framework osf.io/c4zke.
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Affiliation(s)
- Florian Naudet
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA
| | - Charlotte Sakarovitch
- Quantitative Sciences Unit, Division of Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Perrine Janiaud
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA
| | - Ioana Cristea
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA
- Department of Clinical Psychology and Psychotherapy, Babes-Bolyai University, Romania
| | - Daniele Fanelli
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA
- Department of Methodology, London School of Economics and Political Science, UK
| | - David Moher
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA
- Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - John P A Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA
- Departments of Medicine, of Health Research and Policy, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, California, USA
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16
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Broes S, Lacombe D, Verlinden M, Huys I. Toward a Tiered Model to Share Clinical Trial Data and Samples in Precision Oncology. Front Med (Lausanne) 2018; 5:6. [PMID: 29435448 PMCID: PMC5797296 DOI: 10.3389/fmed.2018.00006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 01/11/2018] [Indexed: 02/05/2023] Open
Abstract
The recent revolution in science and technology applied to medical research has left in its wake a trial of biomedical data and human samples; however, its opportunities remain largely unfulfilled due to a number of legal, ethical, financial, strategic, and technical barriers. Precision oncology has been at the vanguard to leverage this potential of "Big data" and samples into meaningful solutions for patients, considering the need for new drug development approaches in this area (due to high costs, late-stage failures, and the molecular diversity of cancer). To harness the potential of the vast quantities of data and samples currently fragmented across databases and biobanks, it is critical to engage all stakeholders and share data and samples across research institutes. Here, we identified two general types of sharing strategies. First, open access models, characterized by the absence of any review panel or decision maker, and second controlled access model where some form of control is exercised by either the donor (i.e., patient), the data provider (i.e., initial organization), or an independent party. Further, we theoretically describe and provide examples of nine different strategies focused on greater sharing of patient data and material. These models provide varying levels of control, access to various data and/or samples, and different types of relationship between the donor, data provider, and data requester. We propose a tiered model to share clinical data and samples that takes into account privacy issues and respects sponsors' legitimate interests. Its implementation would contribute to maximize the value of existing datasets, enabling unraveling the complexity of tumor biology, identify novel biomarkers, and re-direct treatment strategies better, ultimately to help patients with cancer.
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Affiliation(s)
- Stefanie Broes
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Denis Lacombe
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | - Michiel Verlinden
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Isabelle Huys
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
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17
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Keerie C, Tuck C, Milne G, Eldridge S, Wright N, Lewis SC. Data sharing in clinical trials - practical guidance on anonymising trial datasets. Trials 2018; 19:25. [PMID: 29321053 PMCID: PMC5763739 DOI: 10.1186/s13063-017-2382-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 12/06/2017] [Indexed: 11/10/2022] Open
Abstract
Background There is an increasing demand by non-commercial funders that trialists should provide access to trial data once the primary analysis is completed. This has to take into account concerns about identifying individual trial participants, and the legal and regulatory requirements. Methods Using the good practice guideline laid out by the work funded by the Medical Research Council Hubs for Trials Methodology Research (MRC HTMR), we anonymised a dataset from a recently completed trial. Using this example, we present practical guidance on how to anonymise a dataset, and describe rules that could be used on other trial datasets. We describe how these might differ if the trial was to be made freely available to all, or if the data could only be accessed with specific permission and data usage agreements in place. Results Following the good practice guidelines, we successfully created a controlled access model for trial data sharing. The data were assessed on a case-by-case basis classifying variables as direct, indirect and superfluous identifiers with differing methods of anonymisation assigned depending on the type of identifier. A final dataset was created and checks of the anonymised dataset were applied. Lastly, a procedure for release of the data was implemented to complete the process. Conclusions We have implemented a practical solution to the data anonymisation process resulting in a bespoke anonymised dataset for a recently completed trial. We have gained useful learnings in terms of efficiency of the process going forward, the need to balance anonymity with data utilisation and future work that should be undertaken. Electronic supplementary material The online version of this article (doi:10.1186/s13063-017-2382-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Catriona Keerie
- Edinburgh Clinical Trials Unit, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Nine Bioquarter, 9 Little France Road, Edinburgh, EH16 4UX, UK.
| | - Christopher Tuck
- Edinburgh Clinical Trials Unit, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Nine Bioquarter, 9 Little France Road, Edinburgh, EH16 4UX, UK
| | - Garry Milne
- Edinburgh Clinical Trials Unit, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Nine Bioquarter, 9 Little France Road, Edinburgh, EH16 4UX, UK
| | | | - Neil Wright
- CTSU - Clinical Trial Service Unit and Epidemiological Studies Unit University of Oxford, Oxford, UK
| | - Steff C Lewis
- Edinburgh Clinical Trials Unit, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Nine Bioquarter, 9 Little France Road, Edinburgh, EH16 4UX, UK
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18
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Dal-Ré R. Access to Anonymized Individual Participant Clinical Trials Data: A Radical Change of Mind by the Most Prestigious Medical Journals. Arch Bronconeumol 2017; 54:65-67. [PMID: 28927858 DOI: 10.1016/j.arbres.2017.07.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 07/25/2017] [Accepted: 07/26/2017] [Indexed: 10/18/2022]
Affiliation(s)
- Rafael Dal-Ré
- Unidad de Epidemiología, Instituto de Investigación Sanitaria-Hospital Universitario Fundación Jiménez Díaz, Universidad Autónoma de Madrid, Madrid, España.
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Johnson SB. Clinical Research Informatics: Supporting the Research Study Lifecycle. Yearb Med Inform 2017; 26:193-200. [PMID: 29063565 PMCID: PMC6239240 DOI: 10.15265/iy-2017-022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Indexed: 12/27/2022] Open
Abstract
Objectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years 2015-2016. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.
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Affiliation(s)
- S. B. Johnson
- Healthcare Policy and Research, Weill Cornell Medicine, New York, USA
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21
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Tudur Smith C, Nevitt S, Appelbe D, Appleton R, Dixon P, Harrison J, Marson A, Williamson P, Tremain E. Resource implications of preparing individual participant data from a clinical trial to share with external researchers. Trials 2017; 18:319. [PMID: 28712359 PMCID: PMC5512949 DOI: 10.1186/s13063-017-2067-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 06/15/2017] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Demands are increasingly being made for clinical trialists to actively share individual participant data (IPD) collected from clinical trials using responsible methods that protect the confidentiality and privacy of clinical trial participants. Clinical trialists, particularly those receiving public funding, are often concerned about the additional time and money that data-sharing activities will require, but few published empirical data are available to help inform these decisions. We sought to evaluate the activity and resources required to prepare anonymised IPD from a clinical trial in anticipation of a future data-sharing request. METHODS Data from two UK publicly funded clinical trials were used for this exercise: 2437 participants with epilepsy recruited from 90 hospital outpatient clinics in the SANAD trial and 146 children with neuro-developmental problems recruited from 18 hospitals in the MENDS trial. We calculated the time and resources required to prepare each anonymised dataset and assemble a data pack ready for sharing. RESULTS The older SANAD trial (published 2007) required 50 hours of staff time with a total estimated associated cost of £3185 whilst the more recently completed MENDS trial (published 2012) required 39.5 hours of staff time with total estimated associated cost of £2540. CONCLUSIONS Clinical trial researchers, funders and sponsors should consider appropriate resourcing and allow reasonable time for preparing IPD ready for subsequent sharing. This process would be most efficient if prospectively built into the standard operational design and conduct of a clinical trial. Further empirical examples exploring the resource requirements in other settings is recommended. TRIAL REGISTRATION SANAD: International Standard Randomised Controlled Trials Registry: ISRCTN38354748 . Registered on 25 April 2003. MENDS EU Clinical Trials Register Eudract 2006-004025-28 . Registered on 16 May 2007. International Standard Randomised Controlled Trials Registry: ISRCTN05534585 /MREC 07/MRE08/43. Registered on 26 January 2007.
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Affiliation(s)
- Catrin Tudur Smith
- Department of Biostatistics, University of Liverpool, Block F, Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK.
| | - Sarah Nevitt
- Department of Biostatistics, University of Liverpool, Block F, Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK
| | - Duncan Appelbe
- Department of Biostatistics, University of Liverpool, Block F, Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK
| | | | - Pete Dixon
- Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK
| | - Janet Harrison
- Department of Biostatistics, University of Liverpool, Block F, Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK
| | - Anthony Marson
- Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK
| | - Paula Williamson
- Department of Biostatistics, University of Liverpool, Block F, Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK
| | - Elizabeth Tremain
- National Institute for Health Research Evaluation, Trials and Studies Coordinating Centre, University of Southampton, Southampton, UK
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22
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Phillips PPJ. Setting Tuberculosis Regimen Development on a Firm Foundation. Clin Infect Dis 2017; 65:55-56. [PMID: 28402531 DOI: 10.1093/cid/cix250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 03/17/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Patrick P J Phillips
- Medical Research Council Clinical Trials Unit at University College London, Aviation House, United Kingdom
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Ross JS, Ritchie JD, Finn E, Desai NR, Lehman RL, Krumholz HM, Gross CP. Data sharing through an NIH central database repository: a cross-sectional survey of BioLINCC users. BMJ Open 2016; 6:e012769. [PMID: 27670522 PMCID: PMC5051517 DOI: 10.1136/bmjopen-2016-012769] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE To characterise experiences using clinical research data shared through the National Institutes of Health (NIH)'s Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) clinical research data repository, along with data recipients' perceptions of the value, importance and challenges with using BioLINCC data. DESIGN AND SETTING Cross-sectional web-based survey. PARTICIPANTS All investigators who requested and received access to clinical research data from BioLINCC between 2007 and 2014. MAIN OUTCOME MEASURES Reasons for BioLINCC data request, research project plans, interactions with original study investigators, BioLINCC experience and other project details. RESULTS There were 536 investigators who requested and received access to clinical research data from BioLINCC between 2007 and 2014. Of 441 potential respondents, 195 completed the survey (response rate=44%); 89% (n=174) requested data for an independent study, 17% (n=33) for pilot/preliminary analysis. Commonly cited reasons for requesting data through BioLINCC were feasibility of collecting data of similar size and scope (n=122) and insufficient financial resources for primary data collection (n=76). For 95% of respondents (n=186), a primary research objective was to complete new research, as opposed to replicate prior analyses. Prior to requesting data from BioLINCC, 18% (n=36) of respondents had contacted the original study investigators to obtain data, whereas 24% (n=47) had done so to request collaboration. Nearly all (n=176; 90%) respondents found the data to be suitable for their proposed project; among those who found the data unsuitable (n=19; 10%), cited reasons were data too complicated to use (n=5) and data poorly organised (n=5). Half (n=98) of respondents had completed their proposed projects, of which 67% (n=66) have been published. CONCLUSIONS Investigators were primarily using clinical research data from BioLINCC for independent research, making use of data that would otherwise have not been feasible to collect.
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Affiliation(s)
- Joseph S Ross
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA
- Department of Internal Medicine, Section of General Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Internal Medicine, Robert Wood Johnson Foundation Clinical Scholars Program, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, USA
| | - Jessica D Ritchie
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Emily Finn
- Department of Internal Medicine, Section of General Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nihar R Desai
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA
- Department of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Richard L Lehman
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA
- UK Cochrane Center, Oxford, UK
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA
- Department of Internal Medicine, Robert Wood Johnson Foundation Clinical Scholars Program, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, USA
- Department of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Cary P Gross
- Department of Internal Medicine, Section of General Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Internal Medicine, Robert Wood Johnson Foundation Clinical Scholars Program, Yale School of Medicine, New Haven, Connecticut, USA
- Cancer Outcomes, Public Policy, and Effectiveness Research Center, Yale Cancer Center, New Haven, Connecticut, USA
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24
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Lewandowsky S, Mann ME, Brown NJL, Friedman H. Science and the public: Debate, denial, and skepticism. JOURNAL OF SOCIAL AND POLITICAL PSYCHOLOGY 2016. [DOI: 10.5964/jspp.v4i2.604] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
When the scientific method yields discoveries that imperil people’s lifestyle or worldviews or impinge on corporate vested interests, the public and political response can be anything but favorable. Sometimes the response slides into overt denial of scientific facts, although this denial is often claimed to involve “skepticism”. We outline the distinction between true skepticism and denial with several case studies. We propose some guidelines to enable researchers to differentiate legitimate critical engagement from bad-faith harassment, and to enable members of the public to pursue their skeptical engagement and critique without such engagement being mistaken for harassment.
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25
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Fletcher C, Hollis S, Burger HU, Gerlinger C. Statistical guidance for responsible data sharing: an overview. BMC Med Res Methodol 2016; 16 Suppl 1:74. [PMID: 27409824 PMCID: PMC4943511 DOI: 10.1186/s12874-016-0168-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
| | - Sally Hollis
- AstraZeneca, Alderley Park, Cheshire, SK10 4TG, UK.,Centre for Biostatistics, Institute of Population Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester, M13 9PL, UK
| | | | - Christoph Gerlinger
- Global Research and Development Statistics, Bayer Pharma AG, 13353, Berlin, Germany.,Gynecology, Obstetrics and Reproductive Medicine, University Medical School of Saarland, 66421, Homburg, Saar, Germany
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26
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Hollis S, Fletcher C, Lynn F, Urban HJ, Branson J, Burger HU, Tudur Smith C, Sydes MR, Gerlinger C. Best practice for analysis of shared clinical trial data. BMC Med Res Methodol 2016; 16 Suppl 1:76. [PMID: 27410240 PMCID: PMC4943488 DOI: 10.1186/s12874-016-0170-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background Greater transparency, including sharing of patient-level data for further research, is an increasingly important topic for organisations who sponsor, fund and conduct clinical trials. This is a major paradigm shift with the aim of maximising the value of patient-level data from clinical trials for the benefit of future patients and society. We consider the analysis of shared clinical trial data in three broad categories: (1) reanalysis - further investigation of the efficacy and safety of the randomized intervention, (2) meta-analysis, and (3) supplemental analysis for a research question that is not directly assessing the randomized intervention. Discussion In order to support appropriate interpretation and limit the risk of misleading findings, analysis of shared clinical trial data should have a pre-specified analysis plan. However, it is not generally possible to limit bias and control multiplicity to the extent that is possible in the original trial design, conduct and analysis, and this should be acknowledged and taken into account when interpreting results. We highlight a number of areas where specific considerations arise in planning, conducting, interpreting and reporting analyses of shared clinical trial data. A key issue is that that these analyses essentially share many of the limitations of any post hoc analyses beyond the original specified analyses. The use of individual patient data in meta-analysis can provide increased precision and reduce bias. Supplemental analyses are subject to many of the same issues that arise in broader epidemiological analyses. Specific discussion topics are addressed within each of these areas. Summary Increased provision of patient-level data from industry and academic-led clinical trials for secondary research can benefit future patients and society. Responsible data sharing, including transparency of the research objectives, analysis plans and of the results will support appropriate interpretation and help to address the risk of misleading results and avoid unfounded health scares.
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Affiliation(s)
- Sally Hollis
- AstraZeneca, Alderley Park, Cheshire, SK10 4TG, UK.,Centre for Biostatistics, Institute of Population Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester, M13 9PL, UK
| | | | - Frances Lynn
- BioMarin, 10 Bloomsbury Way, London, WC1A 2SL, UK
| | - Hans-Joerg Urban
- Hoffman-La Roche, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | | | | | - Catrin Tudur Smith
- MRC North West Hub for Trials Methodology Research, University of Liverpool, Liverpool, UK
| | - Matthew R Sydes
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, Aviation House, 125 Kingsway, London, WC2B 6NH, UK.,MRC London Hub for Trials Methodology Research, Aviation House, 125 Kingsway, London, WC2B 6NH, UK
| | - Christoph Gerlinger
- Research and Development Statistics, Bayer Pharma AG, 13353, Berlin, Germany. .,Gynecology, Obstetrics and Reproductive Medicine, University Medical School of Saarland, 66421, Homburg/Saar, Germany.
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27
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Hrynaszkiewicz I, Khodiyar V, Hufton AL, Sansone SA. Publishing descriptions of non-public clinical datasets: proposed guidance for researchers, repositories, editors and funding organisations. Res Integr Peer Rev 2016; 1:6. [PMID: 29451541 PMCID: PMC5793987 DOI: 10.1186/s41073-016-0015-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 04/22/2016] [Indexed: 12/17/2022] Open
Abstract
Sharing of experimental clinical research data usually happens between individuals or research groups rather than via public repositories, in part due to the need to protect research participant privacy. This approach to data sharing makes it difficult to connect journal articles with their underlying datasets and is often insufficient for ensuring access to data in the long term. Voluntary data sharing services such as the Yale Open Data Access (YODA) and Clinical Study Data Request (CSDR) projects have increased accessibility to clinical datasets for secondary uses while protecting patient privacy and the legitimacy of secondary analyses but these resources are generally disconnected from journal articles-where researchers typically search for reliable information to inform future research. New scholarly journal and article types dedicated to increasing accessibility of research data have emerged in recent years and, in general, journals are developing stronger links with data repositories. There is a need for increased collaboration between journals, data repositories, researchers, funders, and voluntary data sharing services to increase the visibility and reliability of clinical research. Using the journal Scientific Data as a case study, we propose and show examples of changes to the format and peer-review process for journal articles to more robustly link them to data that are only available on request. We also propose additional features for data repositories to better accommodate non-public clinical datasets, including Data Use Agreements (DUAs).
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Affiliation(s)
- Iain Hrynaszkiewicz
- Springer Nature, The Campus, Trematon Walk, Wharfdale Road, London, N1 9FN UK
| | - Varsha Khodiyar
- Scientific Data, The Campus, Trematon Walk, Wharfdale Road, London, N1 9FN UK
| | - Andrew L. Hufton
- Scientific Data, The Campus, Trematon Walk, Wharfdale Road, London, N1 9FN UK
| | - Susanna-Assunta Sansone
- Scientific Data, The Campus, Trematon Walk, Wharfdale Road, London, N1 9FN UK
- Oxford e-Research Centre, University of Oxford, Oxford, OX1 3QG UK
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28
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Polanin JR, Williams RT. Overcoming obstacles in obtaining individual participant data for meta-analysis. Res Synth Methods 2016; 7:333-41. [DOI: 10.1002/jrsm.1208] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Revised: 03/02/2016] [Accepted: 03/06/2016] [Indexed: 11/12/2022]
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Hopkins C, Sydes M, Murray G, Woolfall K, Clarke M, Williamson P, Tudur Smith C. UK publicly funded Clinical Trials Units supported a controlled access approach to share individual participant data but highlighted concerns. J Clin Epidemiol 2016; 70:17-25. [PMID: 26169841 PMCID: PMC4742521 DOI: 10.1016/j.jclinepi.2015.07.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Revised: 05/22/2015] [Accepted: 07/06/2015] [Indexed: 02/08/2023]
Abstract
OBJECTIVES Evaluate current data sharing activities of UK publicly funded Clinical Trial Units (CTUs) and identify good practices and barriers. STUDY DESIGN AND SETTING Web-based survey of Directors of 45 UK Clinical Research Collaboration (UKCRC)-registered CTUs. RESULTS Twenty-three (51%) CTUs responded: Five (22%) of these had an established data sharing policy and eight (35%) specifically requested consent to use patient data beyond the scope of the original trial. Fifteen (65%) CTUs had received requests for data, and seven (30%) had made external requests for data in the previous 12 months. CTUs supported the need for increased data sharing activities although concerns were raised about patient identification, misuse of data, and financial burden. Custodianship of clinical trial data and requirements for a CTU to align its policy to their parent institutes were also raised. No CTUs supported the use of an open access model for data sharing. CONCLUSION There is support within the publicly funded UKCRC-registered CTUs for data sharing, but many perceived barriers remain. CTUs are currently using a variety of approaches and procedures for sharing data. This survey has informed further work, including development of guidance for publicly funded CTUs, to promote good practice and facilitate data sharing.
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Affiliation(s)
- Carolyn Hopkins
- MRC North West Hub for Trials Methodology Research, Department of Biostatistics, University of Liverpool, Block F Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK
| | - Matthew Sydes
- MRC Clinical Trials Unit, University College London, Aviation House, 125 Kingsway, London, WC2B 6NH, UK
| | - Gordon Murray
- Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, UK
| | - Kerry Woolfall
- MRC North West Hub for Trials Methodology Research, Department of Psychological Sciences, Block B Waterhouse Building, Brownlow Street, Liverpool L69 3GL, UK
| | - Mike Clarke
- All-Ireland Hub for Trials Methodology Research, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Health Sciences Building, 97 Lisburn Road, Belfast, BT9 7BL, UK
| | - Paula Williamson
- MRC North West Hub for Trials Methodology Research, Department of Biostatistics, University of Liverpool, Block F Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK
| | - Catrin Tudur Smith
- MRC North West Hub for Trials Methodology Research, Department of Biostatistics, University of Liverpool, Block F Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK.
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30
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Tudur Smith C, Hopkins C, Sydes MR, Woolfall K, Clarke M, Murray G, Williamson P. How should individual participant data (IPD) from publicly funded clinical trials be shared? BMC Med 2015; 13:298. [PMID: 26675031 PMCID: PMC4682216 DOI: 10.1186/s12916-015-0532-z] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Accepted: 11/24/2015] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Individual participant data (IPD) from completed clinical trials should be responsibly shared to support efficient clinical research, generate new knowledge and bring benefit to patients. The Medical Research Council (MRC) Hubs for Trials Methodology Research (HTMR) has developed guidance to facilitate the sharing of IPD from publicly funded clinical trials. METHODS Development of the guidance was completed over four phases which included a focussed review of policy documents, a web-based survey of the UK Clinical Research Collaboration (CRC) Registered Clinical Trials Units (CTU) Network, participation of an expert committee and an open consultation with the UKCRC Registered CTU Network. The project was funded by the MRC HTMR (MR/L004933/1-R39). RESULTS Good practice principles include: (i) the use of a controlled access approach, using a transparent and robust system to review requests and provide secure data access; (ii) seeking consent for sharing IPD from trial participants in all future clinical trials with adequate assurance that patient privacy and confidentiality can be maintained; and (iii) establishing an approach to resource the sharing of IPD which would include support from trial funders, sponsor organisations and users of IPD. The guidance has been endorsed by Cancer Research UK, MRC Methodology Research Programme Advisory Group, Wellcome Trust and the Executive Group of the UKCRC Registered CTU Network. The National Institute for Health Research (NIHR) has confirmed it is supportive of the application of this guidance. CONCLUSIONS Implementation of these principles will improve transparency, increase the coherent sharing of IPD from publicly funded trials, and help publicly funded trials to adhere to trial funder and journal requirements for data sharing.
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Affiliation(s)
- C Tudur Smith
- MRC North West Hub for Trials Methodology Research, Department of Biostatistics, University of Liverpool, Block F Waterhouse Building, Liverpool, L69 3GL, UK.
| | - C Hopkins
- MRC North West Hub for Trials Methodology Research, Department of Biostatistics, University of Liverpool, Block F Waterhouse Building, Liverpool, L69 3GL, UK
| | - M R Sydes
- MRC Clinical Trials Unit, University College London, Aviation House, 125 Kingsway, London, WC2B 6NH, UK
| | - K Woolfall
- MRC North West Hub for Trials Methodology Research, Department of Psychological Sciences, Block B Waterhouse Building, Brownlow Street, Liverpool, L69 3GL, UK
| | - M Clarke
- All-Ireland Hub for Trials Methodology Research, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Health Sciences Building, 97 Lisburn Road, Belfast, BT9 7BL, UK
| | - G Murray
- Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, UK
| | - P Williamson
- MRC North West Hub for Trials Methodology Research, Department of Biostatistics, University of Liverpool, Block F Waterhouse Building, Liverpool, L69 3GL, UK
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Flather M. Open access data sharing from clinical trials: is it really feasible? EUROPEAN HEART JOURNAL. QUALITY OF CARE & CLINICAL OUTCOMES 2015; 1:49-50. [PMID: 29474599 DOI: 10.1093/ehjqcco/qcv019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Affiliation(s)
- Marcus Flather
- Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
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Naci H, Cooper J, Mossialos E. Timely publication and sharing of trial data: opportunities and challenges for comparative effectiveness research in cardiovascular disease. EUROPEAN HEART JOURNAL. QUALITY OF CARE & CLINICAL OUTCOMES 2015; 1:58-65. [PMID: 29474595 DOI: 10.1093/ehjqcco/qcv012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Indexed: 11/12/2022]
Abstract
There is growing enthusiasm for the timely publication and sharing of clinical trial data. The rationale for open access includes greater transparency, reproducibility, and efficiency of the research enterprise. In cardiovascular diseases, routinely sharing clinical trial data would create opportunities for undertaking comparative effectiveness research, providing much needed evidence on how different interventions compare to each other on key outcomes. Access to individual patient-level data would strengthen the validity of such research. Novel methodological approaches like network meta-analyses using individual patient-level data could reliably compare interventions that have not been compared with each other in head-to-head trials. However, there are significant practical, methodological, financial, and legal challenges to this utopian open access that need to be continually addressed. Sharing clinical trial data openly will only occur when the previously tolerated process of clinical research involving direct ownership and secrecy is abandoned for a new culture in which medical science is open to all of its stakeholders. With this new culture, data will be accessible, reanalysis will be considered commonplace, and comparative effectiveness research through novel synthesis approaches, such as network meta-analyses, can thrive-as long as measures are taken to adequately ensure the goal remains to promote public health.
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Affiliation(s)
- Huseyin Naci
- LSE Health, Department of Social Policy, London School of Economics and Political Science, London UK
| | - Jacob Cooper
- LSE Health, Department of Social Policy, London School of Economics and Political Science, London UK
| | - Elias Mossialos
- LSE Health, Department of Social Policy, London School of Economics and Political Science, London UK
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