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Prado MC, Dotto L, Agostini B, Sarkis-Onofre R. Assessing transparency practices in dental randomized controlled trials. BMC Med Res Methodol 2024; 24:185. [PMID: 39182028 PMCID: PMC11344353 DOI: 10.1186/s12874-024-02316-0] [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: 03/04/2024] [Accepted: 08/21/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND To evaluate transparency practices in randomized controlled trials (RCTs) in dentistry. METHODS This meta-research study included RCTs in dentistry regardless of topic, methods, or level of detail reported. Only studies in English were considered. We searched PubMed for RCTs in dentistry published in English from December 31, 2016, to December 31, 2021. The screening was performed in duplicate, and data extracted included journal and author details, dental specialty, protocol registration, data and code sharing, conflict of interest declaration, and funding information. A descriptive analysis of the data was performed. We generated maps illustrating the reporting of transparency items by country of the corresponding author and a heat table reflecting reporting levels by dental specialty. RESULTS A total of 844 RCTs were included. Only 12.86% of studies reported any information about data and code sharing. Protocol registration was reported for 50.36% of RCTs. Conflict of interest (83.41%) and funding (71.68%) declarations were present in most studies. Conflicts of interest and funding were consistently reported regardless of country or specialty, while data and code sharing had a low level of reporting across specialties, as well as low dissemination across the world. Protocol registration exhibited considerable variability. CONCLUSIONS Considering the importance of RCTs for evidence-based dentistry, it is crucial that everyone who participates in the scientific production and dissemination process actively and consistently promotes adherence to transparent scientific standards, particularly registration of protocols, and sharing of data and code.
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Affiliation(s)
| | - Lara Dotto
- School of Dentistry, Regional Integrated University of High Uruguay and Missions, Erechim, Brazil
| | - Bernardo Agostini
- Graduate Program in Dentistry, ATITUS Educação, Passo Fundo, RS, Brazil
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2
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Jahanshad N, Lenzini P, Bijsterbosch J. Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01938-8. [PMID: 39117903 DOI: 10.1038/s41386-024-01938-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/05/2024] [Accepted: 07/09/2024] [Indexed: 08/10/2024]
Abstract
Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.
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Affiliation(s)
- Neda Jahanshad
- Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, 90292, USA.
| | - Petra Lenzini
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Janine Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA.
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3
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Neilson CJ, Premji Z. A study of search strategy availability statements and sharing practices for systematic reviews: Ask and you might receive. Res Synth Methods 2024; 15:441-449. [PMID: 38098285 DOI: 10.1002/jrsm.1696] [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: 09/01/2023] [Revised: 11/27/2023] [Accepted: 12/06/2023] [Indexed: 04/26/2024]
Abstract
The literature search underpins data collection for all systematic reviews (SRs). The SR reporting guideline PRISMA, and its extensions, aim to facilitate research transparency and reproducibility, and ultimately improve the quality of research, by instructing authors to provide specific research materials and data upon publication of the manuscript. Search strategies are one item of data that are explicitly included in PRISMA and the critical appraisal tool AMSTAR2. Yet some authors use search availability statements implying that the search strategies are available upon request instead of providing strategies up front. We sought out reviews with search availability statements, characterized them, and requested the search strategies from authors via email. Over half of the included reviews cited PRISMA but less than a third included any search strategies. After requesting the strategies via email as instructed, we received replies from 46% of authors, and eventually received at least one search strategy from 36% of authors. Requesting search strategies via email has a low chance of success. Ask and you might receive-but you probably will not. SRs that do not make search strategies available are low quality at best according to AMSTAR2; Journal editors can and should enforce the requirement for authors to include their search strategies alongside their SR manuscripts.
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Affiliation(s)
| | - Zahra Premji
- University of Victoria, Libraries, Victoria, British Columbia, Canada
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4
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Dulitzki C, Crane SM, Hardwicke TE, Ioannidis JPA. Expanding the data Ark: an attempt to make the data from highly cited social science papers publicly available. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240016. [PMID: 39076822 PMCID: PMC11285638 DOI: 10.1098/rsos.240016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/19/2024] [Indexed: 07/31/2024]
Abstract
Access to scientific data can enable independent reuse and verification; however, most data are not available and become increasingly irrecoverable over time. This study aimed to retrieve and preserve important datasets from 160 of the most highly-cited social science articles published between 2008-2013 and 2015-2018. We asked authors if they would share data in a public repository-the Data Ark-or provide reasons if data could not be shared. Of the 160 articles, data for 117 (73%, 95% CI [67%-80%]) were not available and data for 7 (4%, 95% CI [0%-12%]) were available with restrictions. Data for 36 (22%, 95% CI [16%-30%]) articles were available in unrestricted form: 29 of these datasets were already available and 7 datasets were made available in the Data Ark. Most authors did not respond to our data requests and a minority shared reasons for not sharing, such as legal or ethical constraints. These findings highlight an unresolved need to preserve important scientific datasets and increase their accessibility to the scientific community.
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Affiliation(s)
- Coby Dulitzki
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Steven Michael Crane
- Stanford Prevention Research Center, Stanford School of Medicine, Stanford, CA, USA
| | - Tom E. Hardwicke
- School of Psychological Sciences, University of Melbourne, Melbourne, Australia
| | - John P. A. Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, CA, USA
- Stanford Prevention Research Center, Stanford School of Medicine, Stanford, CA, USA
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5
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Wong LY, Li Y, Elliott IA, Backhus LM, Berry MF, Shrager JB, Oh DS. Randomized controlled trials in lung cancer surgery: How are we doing? JTCVS OPEN 2024; 18:234-252. [PMID: 38690441 PMCID: PMC11056451 DOI: 10.1016/j.xjon.2024.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 05/02/2024]
Abstract
Objective Randomized control trials are considered the highest level of evidence, yet the scalability and practicality of implementing randomized control trials in the thoracic surgical oncology space are not well described. The aim of this study is to understand what types of randomized control trials have been conducted in thoracic surgical oncology and ascertain their success rate in completing them as originally planned. Methods The ClinicalTrials.gov database was queried in April 2023 to identify registered randomized control trials performed in patients with lung cancer who underwent surgery (by any technique) as part of their treatment. Results There were 68 eligible randomized control trials; 33 (48.5%) were intended to examine different perioperative patient management strategies (eg, analgesia, ventilation, drainage) or to examine different intraoperative technical aspects (eg, stapling, number of ports, port placement, ligation). The number of randomized control trials was relatively stable over time until a large increase in randomized control trials starting in 2016. Forty-four of the randomized control trials (64.7%) were open-label studies, 43 (63.2%) were conducted in a single facility, 66 (97.1%) had 2 arms, and the mean number of patients enrolled per randomized control trial was 236 (SD, 187). Of 21 completed randomized control trials (31%), the average time to complete accrual was 1605 days (4.4 years) and average time to complete primary/secondary outcomes and adverse events collection was 2125 days (5.82 years). Conclusions Given the immense investment of resources that randomized control trials require, these findings suggest the need to scrutinize future randomized control trial proposals to assess the likelihood of successful completion. Future study is needed to understand the various contributing factors to randomized control trial success or failure.
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Affiliation(s)
- Lye-Yeng Wong
- Department of Cardiothoracic Surgery, Stanford University Medical Center, Stanford, Calif
| | - Yanli Li
- Department of Medical Affairs, Intuitive Surgical, Sunnyvale, Calif
| | - Irmina A. Elliott
- Department of Cardiothoracic Surgery, Stanford University Medical Center, Stanford, Calif
- VA Palo Alto Health Care System, Palo Alto, Calif
| | - Leah M. Backhus
- Department of Cardiothoracic Surgery, Stanford University Medical Center, Stanford, Calif
- VA Palo Alto Health Care System, Palo Alto, Calif
| | - Mark F. Berry
- Department of Cardiothoracic Surgery, Stanford University Medical Center, Stanford, Calif
- VA Palo Alto Health Care System, Palo Alto, Calif
| | - Joseph B. Shrager
- Department of Cardiothoracic Surgery, Stanford University Medical Center, Stanford, Calif
- VA Palo Alto Health Care System, Palo Alto, Calif
| | - Daniel S. Oh
- Department of Medical Affairs, Intuitive Surgical, Sunnyvale, Calif
- Department of Cardiothoracic Surgery, VA Palo Alto Health Care System, Palo Alto, Calif
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6
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Freitas LT, Khan MA, Uddin A, Halder JB, Singh-Phulgenda S, Raja JD, Balakrishnan V, Harriss E, Rahi M, Brack M, Guérin PJ, Basáñez MG, Kumar A, Walker M, Srividya A. The lymphatic filariasis treatment study landscape: A systematic review of study characteristics and the case for an individual participant data platform. PLoS Negl Trop Dis 2024; 18:e0011882. [PMID: 38227595 PMCID: PMC10817204 DOI: 10.1371/journal.pntd.0011882] [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: 09/05/2023] [Revised: 01/26/2024] [Accepted: 12/22/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Lymphatic filariasis (LF) is a neglected tropical disease (NTD) targeted by the World Health Organization for elimination as a public health problem (EPHP). Since 2000, more than 9 billion treatments of antifilarial medicines have been distributed through mass drug administration (MDA) programmes in 72 endemic countries and 17 countries have reached EPHP. Yet in 2021, nearly 900 million people still required MDA with combinations of albendazole, diethylcarbamazine and/or ivermectin. Despite the reliance on these drugs, there remain gaps in understanding of variation in responses to treatment. As demonstrated for other infectious diseases, some urgent questions could be addressed by conducting individual participant data (IPD) meta-analyses. Here, we present the results of a systematic literature review to estimate the abundance of IPD on pre- and post-intervention indicators of infection and/or morbidity and assess the feasibility of building a global data repository. METHODOLOGY We searched literature published between 1st January 2000 and 5th May 2023 in 15 databases to identify prospective studies assessing LF treatment and/or morbidity management and disease prevention (MMDP) approaches. We considered only studies where individual participants were diagnosed with LF infection or disease and were followed up on at least one occasion after receiving an intervention/treatment. PRINCIPAL FINDINGS We identified 138 eligible studies from 23 countries, having followed up an estimated 29,842 participants after intervention. We estimate 14,800 (49.6%) IPD on pre- and post-intervention infection indicators including microfilaraemia, circulating filarial antigen and/or ultrasound indicators measured before and after intervention using 8 drugs administered in various combinations. We identified 33 studies on MMDP, estimating 6,102 (20.4%) IPD on pre- and post-intervention clinical morbidity indicators only. A further 8,940 IPD cover a mixture of infection and morbidity outcomes measured with other diagnostics, from participants followed for adverse event outcomes only or recruited after initial intervention. CONCLUSIONS The LF treatment study landscape is heterogeneous, but the abundance of studies and related IPD suggest that establishing a global data repository to facilitate IPD meta-analyses would be feasible and useful to address unresolved questions on variation in treatment outcomes across geographies, demographics and in underrepresented groups. New studies using more standardized approaches should be initiated to address the scarcity and inconsistency of data on morbidity management.
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Affiliation(s)
- Luzia T. Freitas
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- Infectious Diseases Data Observatory, University of Oxford, Oxford, United Kingdom
| | | | - Azhar Uddin
- ICMR-Vector Control Research Centre, Puducherry, India
| | - Julia B. Halder
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- Infectious Diseases Data Observatory, University of Oxford, Oxford, United Kingdom
- Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, United Kingdom
| | - Sauman Singh-Phulgenda
- Infectious Diseases Data Observatory, University of Oxford, Oxford, United Kingdom
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | | | | | - Eli Harriss
- The Knowledge Centre, Bodleian Health Care Libraries, University of Oxford, Oxford, United Kingdom
| | - Manju Rahi
- ICMR-Vector Control Research Centre, Puducherry, India
| | - Matthew Brack
- Infectious Diseases Data Observatory, University of Oxford, Oxford, United Kingdom
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Philippe J. Guérin
- Infectious Diseases Data Observatory, University of Oxford, Oxford, United Kingdom
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Maria-Gloria Basáñez
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- Infectious Diseases Data Observatory, University of Oxford, Oxford, United Kingdom
| | - Ashwani Kumar
- Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
| | - Martin Walker
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- Infectious Diseases Data Observatory, University of Oxford, Oxford, United Kingdom
- Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, United Kingdom
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7
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Keener SK, Kepes S, Torka AK. The trustworthiness of the cumulative knowledge in industrial/organizational psychology: The current state of affairs and a path forward. Acta Psychol (Amst) 2023; 239:104005. [PMID: 37625919 DOI: 10.1016/j.actpsy.2023.104005] [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: 04/12/2023] [Revised: 07/13/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023] Open
Abstract
The goal of industrial/organizational (IO) psychology, is to build and organize trustworthy knowledge about people-related phenomena in the workplace. Unfortunately, as with other scientific disciplines, our discipline may be experiencing a "crisis of confidence" stemming from the lack of reproducibility and replicability of many of our field's research findings, which would suggest that much of our research may be untrustworthy. If a scientific discipline's research is deemed untrustworthy, it can have dire consequences, including the withdraw of funding for future research. In this focal article, we review the current state of reproducibility and replicability in IO psychology and related fields. As part of this review, we discuss factors that make it less likely that research findings will be trustworthy, including the prevalence of scientific misconduct, questionable research practices (QRPs), and errors. We then identify some root causes of these issues and provide several potential remedies. In particular, we highlight the need for improved research methods and statistics training as well as a re-alignment of the incentive structure in academia. To accomplish this, we advocate for changes in the reward structure, improvements to the peer review process, and the implementation of open science practices. Overall, addressing the current "crisis of confidence" in IO psychology requires individual researchers, academic institutions, and publishers to embrace system-wide change.
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Affiliation(s)
- Sheila K Keener
- Department of Management, Old Dominion University, Norfolk, VA, United States of America.
| | - Sven Kepes
- Department of Management and Entrepreneurship, Virginia Commonwealth University, Richmond, VA, United States of America.
| | - Ann-Kathrin Torka
- Department of Social, Work, and Organizational Psychology, TU Dortmund University, Dortmund, Germany.
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8
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Hamilton DG, Hong K, Fraser H, Rowhani-Farid A, Fidler F, Page MJ. Prevalence and predictors of data and code sharing in the medical and health sciences: systematic review with meta-analysis of individual participant data. BMJ 2023; 382:e075767. [PMID: 37433624 PMCID: PMC10334349 DOI: 10.1136/bmj-2023-075767] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/07/2023] [Indexed: 07/13/2023]
Abstract
OBJECTIVES To synthesise research investigating data and code sharing in medicine and health to establish an accurate representation of the prevalence of sharing, how this frequency has changed over time, and what factors influence availability. DESIGN Systematic review with meta-analysis of individual participant data. DATA SOURCES Ovid Medline, Ovid Embase, and the preprint servers medRxiv, bioRxiv, and MetaArXiv were searched from inception to 1 July 2021. Forward citation searches were also performed on 30 August 2022. REVIEW METHODS Meta-research studies that investigated data or code sharing across a sample of scientific articles presenting original medical and health research were identified. Two authors screened records, assessed the risk of bias, and extracted summary data from study reports when individual participant data could not be retrieved. Key outcomes of interest were the prevalence of statements that declared that data or code were publicly or privately available (declared availability) and the success rates of retrieving these products (actual availability). The associations between data and code availability and several factors (eg, journal policy, type of data, trial design, and human participants) were also examined. A two stage approach to meta-analysis of individual participant data was performed, with proportions and risk ratios pooled with the Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis. RESULTS The review included 105 meta-research studies examining 2 121 580 articles across 31 specialties. Eligible studies examined a median of 195 primary articles (interquartile range 113-475), with a median publication year of 2015 (interquartile range 2012-2018). Only eight studies (8%) were classified as having a low risk of bias. Meta-analyses showed a prevalence of declared and actual public data availability of 8% (95% confidence interval 5% to 11%) and 2% (1% to 3%), respectively, between 2016 and 2021. For public code sharing, both the prevalence of declared and actual availability were estimated to be <0.5% since 2016. Meta-regressions indicated that only declared public data sharing prevalence estimates have increased over time. Compliance with mandatory data sharing policies ranged from 0% to 100% across journals and varied by type of data. In contrast, success in privately obtaining data and code from authors historically ranged between 0% and 37% and 0% and 23%, respectively. CONCLUSIONS The review found that public code sharing was persistently low across medical research. Declarations of data sharing were also low, increasing over time, but did not always correspond to actual sharing of data. The effectiveness of mandatory data sharing policies varied substantially by journal and type of data, a finding that might be informative for policy makers when designing policies and allocating resources to audit compliance. SYSTEMATIC REVIEW REGISTRATION Open Science Framework doi:10.17605/OSF.IO/7SX8U.
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Affiliation(s)
- Daniel G Hamilton
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, VIC, Australia
- Melbourne Medical School, Faculty of Medicine, Dentistry, and Health Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Kyungwan Hong
- Department of Practice, Sciences, and Health Outcomes Research, University of Maryland School of Pharmacy, Baltimore, MD, USA
| | - Hannah Fraser
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, VIC, Australia
| | - Anisa Rowhani-Farid
- Department of Practice, Sciences, and Health Outcomes Research, University of Maryland School of Pharmacy, Baltimore, MD, USA
| | - Fiona Fidler
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, VIC, Australia
- School of Historical and Philosophical Studies, University of Melbourne, Melbourne, VIC, Australia
| | - Matthew J Page
- Methods in Evidence Synthesis Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
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9
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Implementing clinical trial data sharing requires training a new generation of biomedical researchers. Nat Med 2023; 29:298-301. [PMID: 36732626 DOI: 10.1038/s41591-022-02080-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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10
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Hamilton DG, Page MJ, Finch S, Everitt S, Fidler F. How often do cancer researchers make their data and code available and what factors are associated with sharing? BMC Med 2022; 20:438. [PMID: 36352426 PMCID: PMC9646258 DOI: 10.1186/s12916-022-02644-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 10/31/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Various stakeholders are calling for increased availability of data and code from cancer research. However, it is unclear how commonly these products are shared, and what factors are associated with sharing. Our objective was to evaluate how frequently oncology researchers make data and code available and explore factors associated with sharing. METHODS A cross-sectional analysis of a random sample of 306 cancer-related articles indexed in PubMed in 2019 which studied research subjects with a cancer diagnosis was performed. All articles were independently screened for eligibility by two authors. Outcomes of interest included the prevalence of affirmative sharing declarations and the rate with which declarations connected to data complying with key FAIR principles (e.g. posted to a recognised repository, assigned an identifier, data license outlined, non-proprietary formatting). We also investigated associations between sharing rates and several journal characteristics (e.g. sharing policies, publication models), study characteristics (e.g. cancer rarity, study design), open science practices (e.g. pre-registration, pre-printing) and subsequent citation rates between 2020 and 2021. RESULTS One in five studies declared data were publicly available (59/306, 19%, 95% CI: 15-24%). However, when data availability was investigated this percentage dropped to 16% (49/306, 95% CI: 12-20%), and then to less than 1% (1/306, 95% CI: 0-2%) when data were checked for compliance with key FAIR principles. While only 4% of articles that used inferential statistics reported code to be available (10/274, 95% CI: 2-6%), the odds of reporting code to be available were 5.6 times higher for researchers who shared data. Compliance with mandatory data and code sharing policies was observed in 48% (14/29) and 0% (0/6) of articles, respectively. However, 88% of articles (45/51) included data availability statements when required. Policies that encouraged data sharing did not appear to be any more effective than not having a policy at all. The only factors associated with higher rates of data sharing were studying rare cancers and using publicly available data to complement original research. CONCLUSIONS Data and code sharing in oncology occurs infrequently, and at a lower rate than would be expected given the prevalence of mandatory sharing policies. There is also a large gap between those declaring data to be available, and those archiving data in a way that facilitates its reuse. We encourage journals to actively check compliance with sharing policies, and researchers consult community-accepted guidelines when archiving the products of their research.
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Affiliation(s)
- Daniel G Hamilton
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, Australia.
- Melbourne Medical School, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, Australia.
| | - Matthew J Page
- School of Public Health & Preventive Medicine, Monash University, Melbourne, Australia
| | - Sue Finch
- Melbourne Statistical Consulting Platform, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Sarah Everitt
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Fiona Fidler
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, Australia
- School of Historical and Philosophical Studies, University of Melbourne, Melbourne, Australia
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11
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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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