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Bellomo RK, Zavalis EA, Ioannidis JPA. Assessment of transparency indicators in space medicine. PLoS One 2024; 19:e0300701. [PMID: 38564591 PMCID: PMC10986997 DOI: 10.1371/journal.pone.0300701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Space medicine is a vital discipline with often time-intensive and costly projects and constrained opportunities for studying various elements such as space missions, astronauts, and simulated environments. Moreover, private interests gain increasing influence in this discipline. In scientific disciplines with these features, transparent and rigorous methods are essential. Here, we undertook an evaluation of transparency indicators in publications within the field of space medicine. A meta-epidemiological assessment of PubMed Central Open Access (PMC OA) eligible articles within the field of space medicine was performed for prevalence of code sharing, data sharing, pre-registration, conflicts of interest, and funding. Text mining was performed with the rtransparent text mining algorithms with manual validation of 200 random articles to obtain corrected estimates. Across 1215 included articles, 39 (3%) shared code, 258 (21%) shared data, 10 (1%) were registered, 110 (90%) contained a conflict-of-interest statement, and 1141 (93%) included a funding statement. After manual validation, the corrected estimates for code sharing, data sharing, and registration were 5%, 27%, and 1%, respectively. Data sharing was 32% when limited to original articles and highest in space/parabolic flights (46%). Overall, across space medicine we observed modest rates of data sharing, rare sharing of code and almost non-existent protocol registration. Enhancing transparency in space medicine research is imperative for safeguarding its scientific rigor and reproducibility.
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Affiliation(s)
- Rosa Katia Bellomo
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, United States of America
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | - Emmanuel A. Zavalis
- Department of Learning Informatics Management and Ethics, Karolinska Institutet, Stockholm, Sweden
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, CA, United States of America
| | - John P. A. Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, United States of America
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, CA, United States of America
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Zavalis EA, Contopoulos-Ioannidis DG, Ioannidis JPA. Transparency in Infectious Disease Research: Meta-research Survey of Specialty Journals. J Infect Dis 2023; 228:227-234. [PMID: 37132475 DOI: 10.1093/infdis/jiad130] [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: 12/19/2022] [Revised: 02/24/2023] [Accepted: 05/01/2023] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND Infectious diseases carry large global burdens and have implications for society at large. Therefore, reproducible, transparent research is extremely important. METHODS We evaluated transparency indicators (code and data sharing, registration, and conflict and funding disclosures) in the 5340 PubMed Central Open Access articles published in 2019 or 2021 in the 9 most cited specialty journals in infectious diseases using the text-mining R package, rtransparent. RESULTS A total of 5340 articles were evaluated (1860 published in 2019 and 3480 in 2021 [of which 1828 were on coronavirus disease 2019, or COVID-19]). Text mining identified code sharing in 98 (2%) articles, data sharing in 498 (9%), registration in 446 (8%), conflict of interest disclosures in 4209 (79%), and funding disclosures in 4866 (91%). There were substantial differences across the 9 journals: 1%-9% for code sharing, 5%-25% for data sharing, 1%-31% for registration, 7%-100% for conflicts of interest, and 65%-100% for funding disclosures. Validation-corrected imputed estimates were 3%, 11%, 8%, 79%, and 92%, respectively. There were no major differences between articles published in 2019 and non-COVID-19 articles in 2021. In 2021, non-COVID-19 articles had more data sharing (12%) than COVID-19 articles (4%). CONCLUSIONS Data sharing, code sharing, and registration are very uncommon in infectious disease specialty journals. Increased transparency is required.
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Affiliation(s)
- Emmanuel A Zavalis
- Department of Learning Informatics Management and Ethics, Karolinska Institutet, Stockholm, Sweden
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University
| | | | - John P A Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University
- Stanford Prevention Research Center, Department of Medicine
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA
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Fosse V, Oldoni E, Bietrix F, Budillon A, Daskalopoulos EP, Fratelli M, Gerlach B, Groenen PMA, Hölter SM, Menon JML, Mobasheri A, Osborne N, Ritskes-Hoitinga M, Ryll B, Schmitt E, Ussi A, Andreu AL, McCormack E, Demotes J, Garcia P, Gerardi C, Glaab E, Haro JM, Hulstaert F, Miguel LS, Mirete JS, Niubo AS, Porcher R, Rauschenberger A, Rodriguez MC, Superchi C, Torres T. Recommendations for robust and reproducible preclinical research in personalised medicine. BMC Med 2023; 21:14. [PMID: 36617553 PMCID: PMC9826728 DOI: 10.1186/s12916-022-02719-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 12/19/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Personalised medicine is a medical model that aims to provide tailor-made prevention and treatment strategies for defined groups of individuals. The concept brings new challenges to the translational step, both in clinical relevance and validity of models. We have developed a set of recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. METHODS These recommendations have been developed following four main steps: (1) a scoping review of the literature with a gap analysis, (2) working sessions with a wide range of experts in the field, (3) a consensus workshop, and (4) preparation of the final set of recommendations. RESULTS Despite the progress in developing innovative and complex preclinical model systems, to date there are fundamental deficits in translational methods that prevent the further development of personalised medicine. The literature review highlighted five main gaps, relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. We identified five points of focus for the recommendations, based on the consensus reached during the consultation meetings: (1) clinically relevant translational research, (2) robust model development, (3) transparency and education, (4) revised regulation, and (5) interaction with clinical research and patient engagement. Here, we present a set of 15 recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. CONCLUSIONS Appropriate preclinical models should be an integral contributor to interventional clinical trial success rates, and predictive translational models are a fundamental requirement to realise the dream of personalised medicine. The implementation of these guidelines is ambitious, and it is only through the active involvement of all relevant stakeholders in this field that we will be able to make an impact and effectuate a change which will facilitate improved translation of personalised medicine in the future.
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Affiliation(s)
- Vibeke Fosse
- Department of Clinical Science, Centre for Cancer Biomarkers, University of Bergen, Bergen, Norway.
| | - Emanuela Oldoni
- EATRIS ERIC, European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
| | - Florence Bietrix
- EATRIS ERIC, European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
| | - Alfredo Budillon
- Istituto Nazionale per lo Studio e la Cura dei Tumori "Fondazione G. Pascale" - IRCCS, Naples, Italy
| | | | - Maddalena Fratelli
- Department of Biochemistry and Molecular Pharmacology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Björn Gerlach
- PAASP GmbH, Guarantors of EQIPD e.V., Central Institute for Mental Health in Mannheim, Mannheim, Germany
| | | | | | - Julia M L Menon
- Preclinicaltrials.eu, Netherlands Heart Institute, Utrecht, The Netherlands
| | - Ali Mobasheri
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, 90570, Oulu, Finland.,Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, LT-08406, Vilnius, Lithuania.,Department of Joint Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.,Departments of Orthopedics, Rheumatology and Clinical Immunology, University Medical Center Utrecht, 508, GA, Utrecht, The Netherlands.,World Health Organization Collaborating Centre for Public Health Aspects of Musculoskeletal Health and Aging, Université de Liège, B-4000, Liège, Belgium
| | | | - Merel Ritskes-Hoitinga
- Department of Population Health Sciences, IRAS, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.,Department of Clinical Medicine, AUGUST, Aarhus University, Aarhus, Denmark
| | - Bettina Ryll
- Melanoma Patient Network Europe, Uppsala, Sweden
| | - Elmar Schmitt
- Global Regulatory Oncology, Merck Healthcare KGaA, Frankfurter Str. 250, 64293, Darmstadt, Germany
| | - Anton Ussi
- EATRIS ERIC, European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
| | - Antonio L Andreu
- EATRIS ERIC, European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
| | - Emmet McCormack
- Department of Clinical Science, Centre for Cancer Biomarkers, University of Bergen, Bergen, Norway.,Department of Clinical Science, Centre for Pharmacy, The University of Bergen, Bergen, Norway
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Zavalis EA, Ioannidis JPA. A meta-epidemiological assessment of transparency indicators of infectious disease models. PLoS One 2022; 17:e0275380. [PMID: 36206207 PMCID: PMC9543956 DOI: 10.1371/journal.pone.0275380] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/15/2022] [Indexed: 01/04/2023] Open
Abstract
Mathematical models have become very influential, especially during the COVID-19 pandemic. Data and code sharing are indispensable for reproducing them, protocol registration may be useful sometimes, and declarations of conflicts of interest (COIs) and of funding are quintessential for transparency. Here, we evaluated these features in publications of infectious disease-related models and assessed whether there were differences before and during the COVID-19 pandemic and for COVID-19 models versus models for other diseases. We analysed all PubMed Central open access publications of infectious disease models published in 2019 and 2021 using previously validated text mining algorithms of transparency indicators. We evaluated 1338 articles: 216 from 2019 and 1122 from 2021 (of which 818 were on COVID-19); almost a six-fold increase in publications within the field. 511 (39.2%) were compartmental models, 337 (25.2%) were time series, 279 (20.9%) were spatiotemporal, 186 (13.9%) were agent-based and 25 (1.9%) contained multiple model types. 288 (21.5%) articles shared code, 332 (24.8%) shared data, 6 (0.4%) were registered, and 1197 (89.5%) and 1109 (82.9%) contained COI and funding statements, respectively. There was no major changes in transparency indicators between 2019 and 2021. COVID-19 articles were less likely to have funding statements and more likely to share code. Further validation was performed by manual assessment of 10% of the articles identified by text mining as fulfilling transparency indicators and of 10% of the articles lacking them. Correcting estimates for validation performance, 26.0% of papers shared code and 41.1% shared data. On manual assessment, 5/6 articles identified as registered had indeed been registered. Of articles containing COI and funding statements, 95.8% disclosed no conflict and 11.7% reported no funding. Transparency in infectious disease modelling is relatively low, especially for data and code sharing. This is concerning, considering the nature of this research and the heightened influence it has acquired.
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Affiliation(s)
- Emmanuel A. Zavalis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, United States of America
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Solna, Stockholm, Sweden
| | - John P. A. Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, United States of America
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, California, United States of America
- * E-mail:
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Zitomer RA, Karr J, Kerstens M, Perry L, Ruth K, Adrean L, Austin S, Cornelius J, Dachenhaus J, Dinkins J, Harrington A, Kim H, Owens T, Revekant C, Schroeder V, Sink C, Valente JJ, Woodis E, Rivers JW. Ten simple rules for getting started with statistics in graduate school. PLoS Comput Biol 2022; 18:e1010033. [PMID: 35446846 PMCID: PMC9022819 DOI: 10.1371/journal.pcbi.1010033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Rachel A. Zitomer
- Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon, United States of America
| | - Jessica Karr
- Department of Integrative Biology, Oregon State University, Corvallis, Oregon, United States of America
| | - Mark Kerstens
- Department of Forest Engineering, Resources, and Management, Oregon State University, Corvallis, Oregon, United States of America
| | - Lindsey Perry
- Department of Animal and Rangeland Sciences, Oregon State University, Corvallis, Oregon, United States of America
| | - Kayla Ruth
- Department of Animal and Rangeland Sciences, Oregon State University, Corvallis, Oregon, United States of America
| | - Lindsay Adrean
- Department of Forest Engineering, Resources, and Management, Oregon State University, Corvallis, Oregon, United States of America
| | - Suzanne Austin
- Department of Integrative Biology, Oregon State University, Corvallis, Oregon, United States of America
| | - Jamie Cornelius
- Department of Integrative Biology, Oregon State University, Corvallis, Oregon, United States of America
| | - Jonathan Dachenhaus
- Department of Forest Engineering, Resources, and Management, Oregon State University, Corvallis, Oregon, United States of America
| | - Jonathan Dinkins
- Department of Animal and Rangeland Sciences, Oregon State University, Corvallis, Oregon, United States of America
| | - Alan Harrington
- Department of Animal and Rangeland Sciences, Oregon State University, Corvallis, Oregon, United States of America
| | - Hankyu Kim
- Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon, United States of America
| | - Terrah Owens
- Department of Animal and Rangeland Sciences, Oregon State University, Corvallis, Oregon, United States of America
| | - Claire Revekant
- Department of Animal and Rangeland Sciences, Oregon State University, Corvallis, Oregon, United States of America
| | - Vanessa Schroeder
- Department of Animal and Rangeland Sciences, Oregon State University, Corvallis, Oregon, United States of America
| | - Chelsea Sink
- Department of Fisheries, Wildlife, and Conservation Sciences, Oregon State University, Corvallis, Oregon, United States of America
| | - Jonathon J. Valente
- Department of Forest Engineering, Resources, and Management, Oregon State University, Corvallis, Oregon, United States of America
- Smithsonian Conservation Biology Institute, Migratory Bird Center, National Zoological Park, Washington, DC, United States of America
| | - Ethan Woodis
- Department of Forest Engineering, Resources, and Management, Oregon State University, Corvallis, Oregon, United States of America
| | - James W. Rivers
- Department of Forest Engineering, Resources, and Management, Oregon State University, Corvallis, Oregon, United States of America
- * E-mail:
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