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Moreau D, Wiebels K. Nine quick tips for open meta-analyses. PLoS Comput Biol 2024; 20:e1012252. [PMID: 39052540 PMCID: PMC11271959 DOI: 10.1371/journal.pcbi.1012252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024] Open
Abstract
Open science principles are revolutionizing the transparency, reproducibility, and accessibility of research. Meta-analysis has become a key technique for synthesizing data across studies in a principled way; however, its impact is contingent on adherence to open science practices. Here, we outline 9 quick tips for open meta-analyses, aimed at guiding researchers to maximize the reach and utility of their findings. We advocate for outlining preregistering clear protocols, opting for open tools and software, and the use of version control systems to ensure transparency and facilitate collaboration. We further emphasize the importance of reproducibility, for example, by sharing search syntax and analysis scripts, and discuss the benefits of planning for dynamic updating to enable living meta-analyses. We also recommend publication in open-access formats, as well as open data, open code, and open access publication. We close by encouraging active promotion of research findings to bridge the gap between complex syntheses and public discourse, and provide a detailed submission checklist to equip researchers, reviewers and journal editors with a structured approach to conducting and reporting open meta-analyses.
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Affiliation(s)
- David Moreau
- School of Psychology and Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Kristina Wiebels
- School of Psychology and Centre for Brain Research, University of Auckland, Auckland, New Zealand
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Siedlecki SL. Replication Research and Metascience: Prerequisite for Evidence-Based Nursing Practice. CLIN NURSE SPEC 2024; 38:69-71. [PMID: 38364065 DOI: 10.1097/nur.0000000000000804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Affiliation(s)
- Sandra L Siedlecki
- Author Affiliations: Senior Nurse Scientist, Department of Nursing Research and Innovation, Cleveland Clinic, Ohio
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Luijken K, Lohmann A, Alter U, Claramunt Gonzalez J, Clouth FJ, Fossum JL, Hesen L, Huizing AHJ, Ketelaar J, Montoya AK, Nab L, Nijman RCC, Penning de Vries BBL, Tibbe TD, Wang YA, Groenwold RHH. Replicability of simulation studies for the investigation of statistical methods: the RepliSims project. ROYAL SOCIETY OPEN SCIENCE 2024; 11:231003. [PMID: 38234442 PMCID: PMC10791519 DOI: 10.1098/rsos.231003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 12/14/2023] [Indexed: 01/19/2024]
Abstract
Results of simulation studies evaluating the performance of statistical methods can have a major impact on the way empirical research is implemented. However, so far there is limited evidence of the replicability of simulation studies. Eight highly cited statistical simulation studies were selected, and their replicability was assessed by teams of replicators with formal training in quantitative methodology. The teams used information in the original publications to write simulation code with the aim of replicating the results. The primary outcome was to determine the feasibility of replicability based on reported information in the original publications and supplementary materials. Replicasility varied greatly: some original studies provided detailed information leading to almost perfect replication of results, whereas other studies did not provide enough information to implement any of the reported simulations. Factors facilitating replication included availability of code, detailed reporting or visualization of data-generating procedures and methods, and replicator expertise. Replicability of statistical simulation studies was mainly impeded by lack of information and sustainability of information sources. We encourage researchers publishing simulation studies to transparently report all relevant implementation details either in the research paper itself or in easily accessible supplementary material and to make their simulation code publicly available using permanent links.
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Affiliation(s)
- K. Luijken
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, University Utrecht, Utrecht, The Netherlands
| | - A. Lohmann
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - U. Alter
- Department of Psychology, York University, Toronto, Ontario, Canada
| | - J. Claramunt Gonzalez
- Methodology and Statistics Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - F. J. Clouth
- Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands
- Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands
| | - J. L. Fossum
- Department of Psychology, University of California, Los Angeles, CA, USA
- Department of Psychology, Seattle Pacific University, Seattle, WA, USA
| | - L. Hesen
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - A. H. J. Huizing
- TNO (Netherlands Organization for Applied Scientific Research), Expertise Group Child Health, Leiden, The Netherlands
| | - J. Ketelaar
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - A. K. Montoya
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - L. Nab
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - R. C. C. Nijman
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - B. B. L. Penning de Vries
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
| | - T. D. Tibbe
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Y. A. Wang
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - R. H. H. Groenwold
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
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