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Modi ND, Kichenadasse G, Hoffmann TC, Haseloff M, Logan JM, Veroniki AA, Venchiarutti RL, Smit AK, Tuffaha H, Jayasekara H, Manning-Bennet A, Morton E, McKinnon RA, Rowland A, Sorich MJ, Hopkins AM. A 10-year update to the principles for clinical trial data sharing by pharmaceutical companies: perspectives based on a decade of literature and policies. BMC Med 2023; 21:400. [PMID: 37872545 PMCID: PMC10594907 DOI: 10.1186/s12916-023-03113-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 10/13/2023] [Indexed: 10/25/2023] Open
Abstract
Data sharing is essential for promoting scientific discoveries and informed decision-making in clinical practice. In 2013, PhRMA/EFPIA recognised the importance of data sharing and supported initiatives to enhance clinical trial data transparency and promote scientific advancements. However, despite these commitments, recent investigations indicate significant scope for improvements in data sharing by the pharmaceutical industry. Drawing on a decade of literature and policy developments, this article presents perspectives from a multidisciplinary team of researchers, clinicians, and consumers. The focus is on policy and process updates to the PhRMA/EFPIA 2013 data sharing commitments, aiming to enhance the sharing and accessibility of participant-level data, clinical study reports, protocols, statistical analysis plans, lay summaries, and result publications from pharmaceutical industry-sponsored trials. The proposed updates provide clear recommendations regarding which data should be shared, when it should be shared, and under what conditions. The suggested improvements aim to develop a data sharing ecosystem that supports science and patient-centred care. Good data sharing principles require resources, time, and commitment. Notwithstanding these challenges, enhancing data sharing is necessary for efficient resource utilization, increased scientific collaboration, and better decision-making for patients and healthcare professionals.
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Affiliation(s)
- Natansh D Modi
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Ganessan Kichenadasse
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Flinders Centre for Innovation in Cancer, Department of Medical Oncology, Flinders Medical Centre, Adelaide, SA, Australia
| | - Tammy C Hoffmann
- Institute for Evidence-Based Healthcare, Faculty of Health Sciences and Medicine, Bond University, Gold Coast, QLD, Australia
| | | | - Jessica M Logan
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Areti A Veroniki
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | - Rebecca L Venchiarutti
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
- Department of Head and Neck Surgery, Chris O'Brien Lifehouse, Sydney, NSW, Australia
| | - Amelia K Smit
- The Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, NSW, Australia
| | - Haitham Tuffaha
- Centre for the Business and Economics of Health, The University of Queensland, Brisbane, QLD, Australia
| | - Harindra Jayasekara
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC, Australia
| | | | - Erin Morton
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Ross A McKinnon
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Andrew Rowland
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Michael J Sorich
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Ashley M Hopkins
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.
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Modi ND, Abuhelwa AY, McKinnon RA, Boddy AV, Haseloff M, Wiese MD, Hoffmann TC, Perakslis ED, Rowland A, Sorich MJ, Hopkins AM. Audit of Data Sharing by Pharmaceutical Companies for Anticancer Medicines Approved by the US Food and Drug Administration. JAMA Oncol 2022; 8:1310-1316. [PMID: 35900732 PMCID: PMC9335250 DOI: 10.1001/jamaoncol.2022.2867] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Question What proportion of clinical trials that underpin registration of contemporary anticancer medicines are eligible for individual participant data (IPD) sharing with qualified researchers? Findings In this quality improvement study of the 304 trials that underpinned the US Food and Drug Administration (FDA) registration of 115 anticancer medicines over the past 10 years, 136 (45%) were eligible for IPD sharing. Meaning Although inroads have been made toward improving IPD transparency over the past 5 years, these findings suggest that a substantial portion of pivotal oncology trials that support the FDA registration of modern anticancer medicines remain unavailable to qualified researchers. Importance Emerging policies drafted by the pharmaceutical industry indicate that they will transparently share clinical trial data. These data offer an unparalleled opportunity to advance evidence-based medicine and support decision-making. Objective To evaluate the eligibility of independent, qualified researchers to access individual participant data (IPD) from oncology trials that supported US Food and Drug Administration (FDA) approval of new anticancer medicines within the past 10 years. Design, Setting, and Participants In this quality improvement study, a cross-sectional analysis was performed of pivotal clinical trials whose results supported FDA-approved anticancer medicines between January 1, 2011, and June 30, 2021. These trials’ results were identified from product labels. Exposures Eligibility for IPD sharing was confirmed by identification of a public listing of the trial as eligible for sharing or by receipt of a positive response from the sponsor to a standardized inquiry. Main Outcomes and Measures The main outcome was frequency of IPD sharing eligibility. Reasons for data sharing ineligibility were requested and collated, and company-, drug-, and trial-level subgroups were evaluated and presented using χ2 tests and forest plots. Results During the 10-year period examined, 115 anticancer medicines were approved by the FDA on the basis of evidence from 304 pharmaceutical industry–sponsored trials. Of these trials, 136 (45%) were eligible for IPD sharing and 168 (55%) were not. Data sharing rates differed substantially among industry sponsors, with the most common reason for not sharing trial IPD being that the collection of long-term follow-up data was still ongoing (89 of 168 trials [53%]). Of the top 10 anticancer medicines by global sales, nivolumab, pembrolizumab, and pomalidomide had the lowest eligibility rates for data sharing (<10% of trials). Conclusions and Relevance There has been a substantial increase in IPD sharing for industry-sponsored oncology trials over the past 5 years. However, this quality improvement study found that more than 50% of queried trials for FDA-approved anticancer medicines were ineligible for IPD sharing. Data accessibility would be substantially improved if, at the time of FDA registration of a medicine, all data that support the registration were made available.
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Affiliation(s)
- Natansh D Modi
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Ahmad Y Abuhelwa
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Ross A McKinnon
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Alan V Boddy
- Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
| | - Mark Haseloff
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Michael D Wiese
- Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
| | - Tammy C Hoffmann
- Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Queensland, Australia
| | - Eric D Perakslis
- Duke Forge, Duke University Medical Center, Durham, North Carolina
| | - Andrew Rowland
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Michael J Sorich
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Ashley M Hopkins
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
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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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Dewidar O, Riddle A, Ghogomu E, Hossain A, Arora P, Bhutta ZA, Black RE, Cousens S, Gaffey MF, Mathew C, Trawin J, Tugwell P, Welch V, Wells GA. PRIME-IPD SERIES Part 1. The PRIME-IPD tool promoted verification and standardization of study datasets retrieved for IPD meta-analysis. J Clin Epidemiol 2021; 136:227-234. [PMID: 34044099 PMCID: PMC8442853 DOI: 10.1016/j.jclinepi.2021.05.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 01/19/2021] [Accepted: 05/05/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVES We describe a systematic approach to preparing data in the conduct of Individual Participant Data (IPD) analysis. STUDY DESIGN AND SETTING A guidance paper proposing methods for preparing individual participant data for meta-analysis from multiple study sources, developed by consultation of relevant guidance and experts in IPD. We present an example of how these steps were applied in checking data for our own IPD meta analysis (IPD-MA). RESULTS We propose five steps of Processing, Replication, Imputation, Merging, and Evaluation to prepare individual participant data for meta-analysis (PRIME-IPD). Using our own IPD-MA as an exemplar, we found that this approach identified missing variables and potential inconsistencies in the data, facilitated the standardization of indicators across studies, confirmed that the correct data were received from investigators, and resulted in a single, verified dataset for IPD-MA. CONCLUSION The PRIME-IPD approach can assist researchers to systematically prepare, manage and conduct important quality checks on IPD from multiple studies for meta-analyses. Further testing of this framework in IPD-MA would be useful to refine these steps.
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Affiliation(s)
- Omar Dewidar
- Bruyère Research Institute, University of Ottawa, 85 Primrose Ave, Ottawa, Ontario, K1R 6M1, Canada; School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Ottawa, Ontario, K1G 5Z3, Canada.
| | - Alison Riddle
- Bruyère Research Institute, University of Ottawa, 85 Primrose Ave, Ottawa, Ontario, K1R 6M1, Canada; School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Ottawa, Ontario, K1G 5Z3, Canada
| | - Elizabeth Ghogomu
- Bruyère Research Institute, University of Ottawa, 85 Primrose Ave, Ottawa, Ontario, K1R 6M1, Canada
| | - Alomgir Hossain
- School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Ottawa, Ontario, K1G 5Z3, Canada; Department of Medicine (Cardiology), The University of Ottawa Heart Institute and University of Ottawa, 40 Ruskin Street, Ottawa, Ontario, K1Y 4W7, Canada
| | - Paul Arora
- Dalla Lana School of Public Health, University of Toronto, 155 College St Room 500, Toronto, Ontario M5T 3M7, Canada
| | - Zulfiqar A Bhutta
- Centre for Global Child Health, Hospital for Sick Children, 555 University Ave, Toronto, Ontario, M5G 1X8, Canada; Institute for Global Health & Development, Aga Khan University, South-Central Asia, East Africa & United Kingdom, Karachi, Pakistan
| | - Robert E Black
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615N Wolfe St Suite E8545, Baltimore, MD, 21205, USA
| | - Simon Cousens
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (LSHTM), Keppel Street, London, WC1E 7HT, UK
| | - Michelle F Gaffey
- Centre for Global Child Health, Hospital for Sick Children, 555 University Ave, Toronto, Ontario, M5G 1X8, Canada
| | - Christine Mathew
- Bruyère Research Institute, University of Ottawa, 85 Primrose Ave, Ottawa, Ontario, K1R 6M1, Canada
| | - Jessica Trawin
- Bruyère Research Institute, University of Ottawa, 85 Primrose Ave, Ottawa, Ontario, K1R 6M1, Canada
| | - Peter Tugwell
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, 501 Smyth Rd, Ottawa, Ontario K1H 8L6, Canada; Department of Medicine, University of Ottawa Faculty of Medicine, Roger Guindon Hall, 451 Smyth Rd #2044, Ottawa, Ontario, K1H 8M5, Canada; WHO Collaborating Centre for Knowledge Translation and Health Technology Assessment in Health Equity, Bruyère Research Institute, 85 Primrose Ave, Ottawa, Ontario, K1R 6M1, Canada; Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, 40 Ruskin St, Ottawa, Ontario, K1Y 4W7, Canada
| | - Vivian Welch
- Bruyère Research Institute, University of Ottawa, 85 Primrose Ave, Ottawa, Ontario, K1R 6M1, Canada; School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Ottawa, Ontario, K1G 5Z3, Canada; WHO Collaborating Centre for Knowledge Translation and Health Technology Assessment in Health Equity, Bruyère Research Institute, 85 Primrose Ave, Ottawa, Ontario, K1R 6M1, Canada
| | - George A Wells
- School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Ottawa, Ontario, K1G 5Z3, Canada; WHO Collaborating Centre for Knowledge Translation and Health Technology Assessment in Health Equity, Bruyère Research Institute, 85 Primrose Ave, Ottawa, Ontario, K1R 6M1, Canada; Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, 40 Ruskin St, Ottawa, Ontario, K1Y 4W7, Canada
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Axson S, Mello MM, Lincow D, Yang C, Gross C, Ross JS, Miller J. Clinical trial transparency and data sharing among biopharmaceutical companies and the role of company size, location and product type: a cross-sectional descriptive analysis. BMJ Open 2021; 11:e053248. [PMID: 34281933 PMCID: PMC8291313 DOI: 10.1136/bmjopen-2021-053248] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVES To examine company characteristics associated with better transparency and to apply a tool used to measure and improve clinical trial transparency among large companies and drugs, to smaller companies and biologics. DESIGN Cross-sectional descriptive analysis. SETTING AND PARTICIPANTS Novel drugs and biologics Food and Drug Administration (FDA) approved in 2016 and 2017 and their company sponsors. MAIN OUTCOME MEASURES Using established Good Pharma Scorecard (GPS) measures, companies and products were evaluated on their clinical trial registration, results dissemination and FDA Amendments Act (FDAAA) implementation; companies were ranked using these measures and a multicomponent data sharing measure. Associations between company transparency scores with company size (large vs non-large), location (US vs non-US) and sponsored product type (drug vs biologic) were also examined. RESULTS 26% of products (16/62) had publicly available results for all clinical trials supporting their FDA approval and 67% (39/58) had public results for trials in patients by 6 months after their FDA approval; 58% (32/55) were FDAAA compliant. Large companies were significantly more transparent than non-large companies (overall median transparency score of 95% (IQR 91-100) vs 59% (IQR 41-70), p<0.001), attributable to higher FDAAA compliance (median of 100% (IQR 88-100) vs 57% (0-100), p=0.01) and better data sharing (median of 100% (IQR 80-100) vs 20% (IQR 20-40), p<0.01). No significant differences were observed by company location or product type. CONCLUSIONS It was feasible to apply the GPS transparency measures and ranking tool to non-large companies and biologics. Large companies are significantly more transparent than non-large companies, driven by better data sharing procedures and implementation of FDAAA trial reporting requirements. Greater research transparency is needed, particularly among non-large companies, to maximise the benefits of research for patient care and scientific innovation.
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Affiliation(s)
- Sydney Axson
- Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Michelle M Mello
- Center for Health Policy/Primary Care and Outcomes Research, Department of Medicine, Stanford University School of Medicine; Stanford University Law School, Stanford, CA, USA
| | - Deborah Lincow
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - Catherine Yang
- University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Cary Gross
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Joseph S Ross
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Jennifer Miller
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
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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: 31] [Impact Index Per Article: 7.8] [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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Barrett JS. Perspective on Data-Sharing Requirements for the Necessary Evolution of Drug Development. J Clin Pharmacol 2020; 60:688-690. [PMID: 32222078 PMCID: PMC7318194 DOI: 10.1002/jcph.1607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Accepted: 02/21/2020] [Indexed: 11/26/2022]
Affiliation(s)
- Jeffrey S Barrett
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, USA
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Miller J, Ross JS, Wilenzick M, Mello MM. Sharing of clinical trial data and results reporting practices among large pharmaceutical companies: cross sectional descriptive study and pilot of a tool to improve company practices. BMJ 2019; 366:l4217. [PMID: 31292127 PMCID: PMC6614834 DOI: 10.1136/bmj.l4217] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/21/2019] [Indexed: 11/17/2022]
Abstract
OBJECTIVES To develop and pilot a tool to measure and improve pharmaceutical companies' clinical trial data sharing policies and practices. DESIGN Cross sectional descriptive analysis. SETTING Large pharmaceutical companies with novel drugs approved by the US Food and Drug Administration in 2015. DATA SOURCES Data sharing measures were adapted from 10 prominent data sharing guidelines from expert bodies and refined through a multi-stakeholder deliberative process engaging patients, industry, academics, regulators, and others. Data sharing practices and policies were assessed using data from ClinicalTrials.gov, Drugs@FDA, corporate websites, data sharing platforms and registries (eg, the Yale Open Data Access (YODA) Project and Clinical Study Data Request (CSDR)), and personal communication with drug companies. MAIN OUTCOME MEASURES Company level, multicomponent measure of accessibility of participant level clinical trial data (eg, analysis ready dataset and metadata); drug and trial level measures of registration, results reporting, and publication; company level overall transparency rankings; and feasibility of the measures and ranking tool to improve company data sharing policies and practices. RESULTS Only 25% of large pharmaceutical companies fully met the data sharing measure. The median company data sharing score was 63% (interquartile range 58-85%). Given feedback and a chance to improve their policies to meet this measure, three companies made amendments, raising the percentage of companies in full compliance to 33% and the median company data sharing score to 80% (73-100%). The most common reasons companies did not initially satisfy the data sharing measure were failure to share data by the specified deadline (75%) and failure to report the number and outcome of their data requests. Across new drug applications, a median of 100% (interquartile range 91-100%) of trials in patients were registered, 65% (36-96%) reported results, 45% (30-84%) were published, and 95% (69-100%) were publicly available in some form by six months after FDA drug approval. When examining results on the drug level, less than half (42%) of reviewed drugs had results for all their new drug applications trials in patients publicly available in some form by six months after FDA approval. CONCLUSIONS It was feasible to develop a tool to measure data sharing policies and practices among large companies and have an impact in improving company practices. Among large companies, 25% made participant level trial data accessible to external investigators for new drug approvals in accordance with the current study's measures; this proportion improved to 33% after applying the ranking tool. Other measures of trial transparency were higher. Some companies, however, have substantial room for improvement on transparency and data sharing of clinical trials.
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Affiliation(s)
- Jennifer Miller
- Department of Internal Medicine, Yale School of Medicine, Yale University, New Haven, CT, USA
- Bioethics International, New York, NY, USA
| | - Joseph S Ross
- Department of Internal Medicine, Yale School of Medicine, Yale University, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA
| | - Marc Wilenzick
- Bioethics International, New York, NY, USA
- Taro Pharmaceuticals, USA, Hawthorne, NY, USA
| | - Michelle M Mello
- Stanford Law School, Stanford University, Stanford, CA, USA
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
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Enhancing patient-level clinical data access to promote evidence-based practice and incentivize therapeutic innovation. Adv Drug Deliv Rev 2018; 136-137:97-104. [PMID: 29408180 DOI: 10.1016/j.addr.2018.01.017] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 01/24/2018] [Accepted: 01/27/2018] [Indexed: 02/06/2023]
Abstract
Clinical trials are crucial to determining the human safety and efficacy of new therapeutic innovations. Extraordinary amounts of human experiential data are generated over the course of any clinical trial, however, much of these data is never made publicly accessible. Improved, reliable data sharing is essential to inform clinical decisions and incentivize further therapeutic improvements; this need, and the call and concept to enhance patient-level clinical trial data accessibility is not new. Several recent public and private shifts in clinical data sharing policies and procedures promise to improve access and data utility to reduce waste in research and increase efficiency of evidence synthesis. Nonetheless, pharmaceutical industry remain reluctant to share full clinical data sets at some level to protect their commercial interests and avoid misuse of their data. Here, we review the landscape of emerging regulations related to the sharing of patient level data and current clinical data access models of major pharmaceutical companies. We also summarize the different measures that could satisfy both clinical data producers and users in achieving the benefits of accessing patient-level data while mitigating any associated risks.
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Data sharing from pharmaceutical industry sponsored clinical studies: audit of data availability. BMC Med 2018; 16:165. [PMID: 30261889 PMCID: PMC6161442 DOI: 10.1186/s12916-018-1154-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 08/14/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Clinical trial transparency is important to participants, trialists, publishers, and regulators, and there have been recent major policy changes by the pharmaceutical industry regarding clinical study data sharing. However, it is unknown if these changes are enabling independent researchers to access participant-level data from prominent contemporary clinical trials sponsored by the pharmaceutical industry 2 years after publication of the primary results. MAIN TEXT PubMed and ClinicalTrials.gov were searched to identify clinical trials of medicines sponsored by the pharmaceutical industry and first published between 1 July 2015 and 31 December 2015 in the top 10 general and internal medical journals by impact factor. For each clinical trial, the eligibility of independent researchers to request participant-level data was identified via the sponsor having a data sharing policy/process and a positive response to an enquiry. Fifty-six publications reporting on 61 industry-sponsored clinical trials were identified, of which 32 (52%) had a public data sharing policy/process and 9 (15%) were confirmed eligible for data sharing. Industry sponsors within the top 25 by global sales were more likely to have a data sharing policy (93% vs 10%), and there was a trend towards increased data sharing eligibility (23% vs 4%). Twenty-six studies were explicitly confirmed as ineligible for data sharing. The two most common data sharing policy conditions that prevented sharing of data for published results were the exclusion of studies that had ongoing follow-up of the published results and the exclusion of studies of medicines that have not yet achieved regulatory approval in the USA and the European Union. CONCLUSIONS Fifteen percent of the sampled clinical trials were available for data sharing 2 years after publication of primary results of the trial. Key issues limiting data sharing include a large proportion of industry sponsors who do not have a data sharing policy/process, and data sharing policy conditions that exclude access on the basis of ongoing follow-up and regulatory activity.
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The Open Translational Science in Schizophrenia (OPTICS) project: an open-science project bringing together Janssen clinical trial and NIMH data. NPJ SCHIZOPHRENIA 2018; 4:14. [PMID: 29950580 PMCID: PMC6021398 DOI: 10.1038/s41537-018-0055-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 05/03/2018] [Accepted: 05/15/2018] [Indexed: 11/09/2022]
Abstract
Clinical trial data are the gold standard for evaluating pharmaceutical safety and efficacy. There is an ethical and scientific imperative for transparency and data sharing to confirm published results and generate new knowledge. The Open Translational Science in Schizophrenia (OPTICS) Project was an open-science initiative aggregating Janssen clinical trial and NIH/NIMH data from real-world studies and trials in schizophrenia. The project aims were to show the value of using shared data to examine: therapeutic safety and efficacy; disease etiologies and course; and methods development. The success of project investigators was due to collaboration from project applications through analyses, with support from the Harvard Catalyst. Project work was independent of Janssen; all intellectual property was dedicated to the public. Efforts such as this are necessary to gain deeper insights into the biology of disease, foster collaboration, and to achieve the goal of developing better treatments, reducing the overall public health burden of devastating brain diseases.
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Vaduganathan M, Nagarur A, Qamar A, Patel RB, Navar AM, Peterson ED, Bhatt DL, Fonarow GC, Yancy CW, Butler J. Availability and Use of Shared Data From Cardiometabolic Clinical Trials. Circulation 2017; 137:938-947. [PMID: 29133600 DOI: 10.1161/circulationaha.117.031883] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2017] [Accepted: 10/16/2017] [Indexed: 11/16/2022]
Abstract
BACKGROUND Sharing of patient-level clinical trial data has been widely endorsed. Little is known about how extensively these data have been used for cardiometabolic diseases. We sought to evaluate the availability and use of shared data from cardiometabolic clinical trials. METHODS We extracted data from ClinicalStudyDataRequest.com, a large, multisponsor data-sharing platform hosting individual patient-level data from completed studies sponsored by 13 pharmaceutical companies. RESULTS From January 2013 to May 2017, the platform had data from 3374 clinical trials, of which 537 (16%) evaluated cardiometabolic therapeutics (phase 1, 36%; phase 2, 17%; phase 2/3, 1%; phase 3, 42%; phase 4, 4%). They covered 74 therapies and 398 925 patients. Diabetes mellitus (60%) and hypertension (15%) were the most common study topics. Median time from study completion to data availability was 79 months. As of May 2017, ClinicalStudyDataRequest.com had received 318 submitted proposals, of which 163 had signed data-sharing agreements. Thirty of these proposals were related to cardiometabolic therapies and requested data from 79 unique studies (15% of all trials, 29% of phase 3/4 trials). Most (96%) data requesters of cardiometabolic clinical trial data were from academic centers in North America and Western Europe, and half the proposals were unfunded. Most proposals were for secondary hypothesis-generating questions, with only 1 proposed reanalysis of the original study primary hypothesis. To date, 3 peer-reviewed articles have been published after a median of 19 months (9-32 months) from the data-sharing agreement. CONCLUSIONS Despite availability of data from >500 cardiometabolic trials in a multisponsor data-sharing platform, only 15% of these trials and 29% of phase 3/4 trials have been accessed by investigators thus far, and a negligible minority of analyses have reached publication.
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Affiliation(s)
- Muthiah Vaduganathan
- Brigham and Women's Hospital Heart & Vascular Center and Harvard Medical School, Boston, MA (M.V., A.Q., D.L.B.).
| | - Amulya Nagarur
- Department of Medicine, Massachusetts General Hospital, Boston (A.N.)
| | - Arman Qamar
- Brigham and Women's Hospital Heart & Vascular Center and Harvard Medical School, Boston, MA (M.V., A.Q., D.L.B.)
| | - Ravi B Patel
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (R.B.P., C.W.Y.)
| | - Ann Marie Navar
- Duke Clinical Research Institute and Division of Cardiology, Duke University Medical Center, Durham, NC (A.M.N., E.D.P.)
| | - Eric D Peterson
- Duke Clinical Research Institute and Division of Cardiology, Duke University Medical Center, Durham, NC (A.M.N., E.D.P.)
| | - Deepak L Bhatt
- Brigham and Women's Hospital Heart & Vascular Center and Harvard Medical School, Boston, MA (M.V., A.Q., D.L.B.)
| | - Gregg C Fonarow
- Ahmanson-UCLA Cardiomyopathy Center, University of California Los Angeles (G.C.F.)
| | - Clyde W Yancy
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (R.B.P., C.W.Y.)
| | - Javed Butler
- Division of Cardiology, Stony Brook University, NY (J.B.)
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Murugiah K, Ritchie JD, Desai NR, Ross JS, Krumholz HM. Availability of Clinical Trial Data From Industry-Sponsored Cardiovascular Trials. J Am Heart Assoc 2016; 5:e003307. [PMID: 27098969 PMCID: PMC4859296 DOI: 10.1161/jaha.116.003307] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND Industry-sponsored clinical trials produce high-quality data sets that can be used by researchers to generate new knowledge. We assessed the availability of individual participant-level data (IPD) from large cardiovascular trials conducted by major pharmaceutical companies and compiled a list of available trials. METHODS AND RESULTS We identified all randomized cardiovascular interventional trials registered on ClinicalTrials.gov with >5000 enrollment, sponsored by 1 of the top 20 pharmaceutical companies by 2014 global sales. Availability of IPD for each trial was ascertained by searching each company's website/data-sharing portal. If availability could not be determined, each company was contacted electronically. Of 60 included trials, IPD are available for 15 trials (25%) consisting of 204 452 patients. IPD are unavailable for 15 trials (25%). Reasons for unavailability were: cosponsor did not agree to make IPD available (4 trials) and trials were not conducted within a specific time (5 trials); for the remaining 6 trials, no specific reason was provided. For 30 trials (50%), availability of IPD could not be definitively determined either because of no response or requirements for a full proposal (23 trials). CONCLUSIONS IPD from 1 in 4 large cardiovascular trials conducted by major pharmaceutical companies are confirmed available to researchers for secondary research, a valuable opportunity to enhance science. However, IPD from 1 in 4 trials are not available, and data availability could not be definitively determined for half of our sample. For several of these trials, companies require a full proposal to determine availability, making use of the IPD by researchers less certain.
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Affiliation(s)
- Karthik Murugiah
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT
| | - Jessica D Ritchie
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT
| | - Nihar R Desai
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT
| | - Joseph S Ross
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT Robert Wood Johnson Foundation Clinical Scholars Program, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT Department of Health Policy and Management, Yale School of Public Health, New Haven, CT
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT Robert Wood Johnson Foundation Clinical Scholars Program, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT Department of Health Policy and Management, Yale School of Public Health, New Haven, CT
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