Frequently Asked Questions

Frequently Asked Questions

How to cite PRISMA 2020

In your methods section, state which guideline resources you used to write your article, refer readers to the supplementary materials to view your completed checklist, and cite this reporting guideline. For example:

We used the PRISMA 2020 writing guide when drafting this article, and the PRISMA 2020 checklist (see supplementary materials A) to demonstrate adherence to the PRISMA 2020 reporting guideline. [1].

You can use your reference manager to save citation information for this webpage, or copy the BibTeX below.

Who made PRISMA 2020?

Matthew J. Page Affiliation: School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia ORCID: https://orcid.org/0000-0002-4242-7526

Joanne E. McKenzie Affiliation: School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia ORCID: https://orcid.org/0000-0003-3534-1641

Patrick M. Bossuyt Affiliation: Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, Netherlands ORCID: https://orcid.org/0000-0003-4427-0128

Isabelle Boutron Affiliation: Université de Paris, Centre of Epidemiology and Statistics (CRESS), Inserm, Paris, France ORCID: https://orcid.org/0000-0002-5263-6241

Tammy C. Hoffmann Affiliation: Institute for Evidence-Based Healthcare, Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Australia ORCID: https://orcid.org/0000-0001-5210-8548

Cynthia D. Mulrow Affiliation: University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America; Annals of Internal Medicine

Larissa Shamseer Affiliation: Knowledge Translation Program, Li Ka Shing Knowledge Institute, Toronto, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada ORCID: https://orcid.org/0000-0003-3690-3378

Jennifer M. Tetzlaff Affiliation: Evidence Partners, Ottawa, Canada

Elie A. Akl Affiliation: Clinical Research Institute, American University of Beirut, Beirut, Lebanon; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada ORCID: https://orcid.org/0000-0002-3444-8618

Sue E. Brennan Affiliation: School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia ORCID: https://orcid.org/0000-0003-1789-8809

Roger Chou Affiliation: Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America

Julie Glanville Affiliation: York Health Economics Consortium (YHEC Ltd), University of York, York, United Kingdom ORCID: https://orcid.org/0000-0002-1253-8524

Jeremy M. Grimshaw Affiliation: Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada; Department of Medicine, University of Ottawa, Ottawa, Canada ORCID: https://orcid.org/0000-0001-8015-8243

Asbjørn Hróbjartsson Affiliation: Centre for Evidence-Based Medicine Odense (CEBMO) and Cochrane Denmark, Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Open Patient data Exploratory Network (OPEN), Odense University Hospital, Odense, Denmark

Manoj M. Lalu Affiliation: Department of Anesthesiology and Pain Medicine, The Ottawa Hospital, Ottawa, Canada; Clinical Epidemiology Program, Blueprint Translational Research Group, Ottawa Hospital Research Institute, Ottawa, Canada; Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, Canada ORCID: https://orcid.org/0000-0002-0322-382X

Tianjing Li Affiliation: Department of Ophthalmology, School of Medicine, University of Colorado Denver, Denver, Colorado, United States; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America

Elizabeth W. Loder Affiliation: Division of Headache, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA; Head of Research, The BMJ, London, United Kingdom

Evan Mayo-Wilson Affiliation: Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, United States of America ORCID: https://orcid.org/0000-0001-6126-2459

Steve McDonald Affiliation: School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia ORCID: https://orcid.org/0000-0003-2832-5205

Luke A. McGuinness Affiliation: Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom ORCID: https://orcid.org/0000-0001-8730-9761

Lesley A. Stewart Affiliation: Centre for Reviews and Dissemination, University of York, York, United Kingdom ORCID: https://orcid.org/0000-0003-0287-4724

James Thomas Affiliation: EPPI-Centre, UCL Social Research Institute, University College London, London, United Kingdom ORCID: https://orcid.org/0000-0003-4805-4190

Andrea C. Tricco Affiliation: Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Unity Health Toronto, Toronto, Canada; Epidemiology Division of the Dalla Lana School of Public Health and the Institute of Health Management, Policy, and Evaluation, University of Toronto, Toronto, Canada; Queen’s Collaboration for Health Care Quality Joanna Briggs Institute Centre of Excellence, Queen’s University, Kingston, Canada ORCID: https://orcid.org/0000-0002-4114-8971

Vivian A. Welch Affiliation: Methods Centre, Bruyère Research Institute, Ottawa, Ontario, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada ORCID: https://orcid.org/0000-0002-5238-7097

Penny Whiting Affiliation: Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

David Moher Affiliation: Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada ORCID: https://orcid.org/0000-0003-2434-4206

How was PRISMA 2020 made?

You can read how PRISMA 2020 was developed here.

#TODO

The UK EQUATOR Centre then worked with PRISMA 2020’s authors to make PRISMA 2020 easier to use by clarifying language, adding definitions, examples, extra information and resources. Although worded differently, the guidance on this website is conceptually the same as the original publication and can be used interchangeably.

Does PRISMA 2020 prescribe structure?

No. PRISMA 2020 does not prescribe a rigid format or standardized content. Consider each item and prioritize elements that are most relevant to your study, findings, context, and readers.

You may prefer to report an item in a different order, section, or in a table or figure. For example, some authors may prefer to include some methods items in their Results section. Others may call their Results section Findings, or have a completely different manuscript structure.

How to prioritize items and keep writing concise

Although all items should be reported, you should prioritize items most relevant to your study, findings, context, and readership.

You should include information in the article body when possible so it’s easy for readers to find. However, if you are worried about word counts or brevity, consider placing information in tables.

If you feel confident that an item is less important to your study, you could report it in an appendix or supplement. Be aware that supplementary materials may not be peer reviewed, are not indexed by search engines, and can be difficult for readers to find. Therefore, they are best only used for details you feel are less important, and you should point readers to them from the article body. For example, “For more details, see the supplementary materials A”.

The UK EQUATOR centre runs training on how to write concisely.

What to write if you feel an item is not applicable

If you think an item is not applicable, state why. You could state this in the text or in the reporting checklist. Remember to publish your completed reporting checklist as a supplement, and to refer authors to it from your methods section.

What to do if asked to remove guideline related content

If a colleague or reviewer asks you to remove content that is related to this guideline, you can direct them to this guideline and the explanation for why that item is important. If they insist, consider moving the item to a supplement, table or figure.

Where can I get general writing training?

The EQUATOR Network provides in-person training for writing research articles.

AuthorAID have resources, an online course, and mentoring to help authors.

Citation

BibTeX citation:
@article{pagePRISMA2020Explanation2021,
  author = {Page, Matthew J and Moher, David and Bossuyt, Patrick M and
    Boutron, Isabelle and Hoffmann, Tammy C and Mulrow, Cynthia D and
    Shamseer, Larissa and Tetzlaff, Jennifer M and Akl, Elie A and
    Brennan, Sue E and Chou, Roger and Glanville, Julie and Grimshaw,
    Jeremy M and Hróbjartsson, Asbjørn and Lalu, Manoj M and Li,
    Tianjing and Loder, Elizabeth W and Mayo-Wilson, Evan and McDonald,
    Steve and McGuinness, Luke A and Stewart, Lesley A and Thomas, James
    and Tricco, Andrea C and Welch, Vivian A and Whiting, Penny and
    McKenzie, Joanne E},
  title = {PRISMA 2020 Explanation and Elaboration: Updated Guidance and
    Exemplars for Reporting Systematic Reviews},
  journal = {The BMJ},
  volume = {372},
  pages = {n160},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8005925/},
  doi = {10.1136/bmj.n160},
  langid = {en},
  abstract = {The methods and results of systematic reviews should be
    reported in sufficient detail to allow users to assess the
    trustworthiness and applicability of the review findings. The
    Preferred Reporting Items for Systematic reviews and Meta-Analyses
    (PRISMA) statement was developed to facilitate transparent and
    complete reporting of systematic reviews and has been updated (to
    PRISMA 2020) to reflect recent advances in systematic review
    methodology and terminology. Here, we present the explanation and
    elaboration paper for PRISMA 2020, where we explain why reporting of
    each item is recommended, present bullet points that detail the
    reporting recommendations, and present examples from published
    reviews. We hope that changes to the content and structure of PRISMA
    2020 will facilitate uptake of the guideline and lead to more
    transparent, complete, and accurate reporting of systematic
    reviews.}
}
For attribution, please cite this work as:
Page MJ, Moher D, Bossuyt PM, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 372:n160. doi:10.1136/bmj.n160

Reporting Guidelines are recommendations to help describe your work clearly

Your research will be used by people from different disciplines and backgrounds for decades to come. Reporting guidelines list the information you should describe so that everyone can understand, replicate, and synthesise your work.

Reporting guidelines do not prescribe how research should be designed or conducted. Rather, they help authors transparently describe what they did, why they did it, and what they found.

Reporting guidelines make writing research easier, and transparent research leads to better patient outcomes.

Easier writing

Following guidance makes writing easier and quicker.

Learn more »

Smoother publishing

Many journals require completed reporting checklists at submission.

Learn more »

Maximum impact

From nobel prizes to null results, articles have more impact when everyone can use them.

Learn more »

Who reads research?

You work will be read by different people, for different reasons, around the world, and for decades to come. Reporting guidelines help you consider all of your potential audiences. For example, your research may be read by researchers from different fields, by clinicians, patients, evidence synthesisers, peer reviewers, or editors. Your readers will need information to understand, to replicate, apply, appraise, synthesise, and use your work.

Cohort studies

A cohort study is an observational study in which a group of people with a particular exposure (e.g. a putative risk factor or protective factor) and a group of people without this exposure are followed over time. The outcomes of the people in the exposed group are compared to the outcomes of the people in the unexposed group to see if the exposure is associated with particular outcomes (e.g. getting cancer or length of life).

Source.

Case-control studies

A case-control study is a research method used in healthcare to investigate potential risk factors for a specific disease. It involves comparing individuals who have been diagnosed with the disease (cases) to those who have not (controls). By analysing the differences between the two groups, researchers can identify factors that may contribute to the development of the disease.

An example would be when researchers conducted a case-control study examining whether exposure to diesel exhaust particles increases the risk of respiratory disease in underground miners. Cases included miners diagnosed with respiratory disease, while controls were miners without respiratory disease. Participants' past occupational exposures to diesel exhaust particles were evaluated to compare exposure rates between cases and controls.

Source.

Cross-sectional studies

A cross-sectional study (also sometimes called a "cross-sectional survey") serves as an observational tool, where researchers capture data from a cohort of participants at a singular point. This approach provides a 'snapshot'— a brief glimpse into the characteristics or outcomes prevalent within a designated population at that precise point in time. The primary aim here is not to track changes or developments over an extended period but to assess and quantify the current situation regarding specific variables or conditions. Such a methodology is instrumental in identifying patterns or correlations among various factors within the population, providing a basis for further, more detailed investigation.

Source

Systematic reviews

A systematic review is a comprehensive approach designed to identify, evaluate, and synthesise all available evidence relevant to a specific research question. In essence, it collects all possible studies related to a given topic and design, and reviews and analyses their results.

The process involves a highly sensitive search strategy to ensure that as much pertinent information as possible is gathered. Once collected, this evidence is often critically appraised to assess its quality and relevance, ensuring that conclusions drawn are based on robust data. Systematic reviews often involve defining inclusion and exclusion criteria, which help to focus the analysis on the most relevant studies, ultimately synthesising the findings into a coherent narrative or statistical synthesis. Some systematic reviews will include a meta-analysis.

Source

Systematic review protocols

TODO

Meta analyses of Observational Studies

TODO

Randomised Trials

A randomised controlled trial (RCT) is a trial in which participants are randomly assigned to one of two or more groups: the experimental group or groups receive the intervention or interventions being tested; the comparison group (control group) receive usual care or no treatment or a placebo. The groups are then followed up to see if there are any differences between the results. This helps in assessing the effectiveness of the intervention.

Source

Randomised Trial Protocols

TODO

Qualitative research

Research that aims to gather and analyse non-numerical (descriptive) data in order to gain an understanding of individuals' social reality, including understanding their attitudes, beliefs, and motivation. This type of research typically involves in-depth interviews, focus groups, or field observations in order to collect data that is rich in detail and context. Qualitative research is often used to explore complex phenomena or to gain insight into people's experiences and perspectives on a particular topic. It is particularly useful when researchers want to understand the meaning that people attach to their experiences or when they want to uncover the underlying reasons for people's behavior. Qualitative methods include ethnography, grounded theory, discourse analysis, and interpretative phenomenological analysis.

Source

Case Reports

TODO

Diagnostic Test Accuracy Studies

Diagnostic accuracy studies focus on estimating the ability of the test(s) to correctly identify subjects with a predefined target condition, or the condition of interest (sensitivity) as well as to clearly identify those without the condition (specificity).

Prediction Models

Prediction model research is used to test the accurarcy of a model or test in estimating an outcome value or risk. Most models estimate the probability of the presence of a particular health condition (diagnostic) or whether a particular outcome will occur in the future (prognostic). Prediction models are used to support clinical decision making, such as whether to refer patients for further testing, monitor disease deterioration or treatment effects, or initiate treatment or lifestyle changes. Examples of well known prediction models include EuroSCORE II for cardiac surgery, the Gail model for breast cancer, the Framingham risk score for cardiovascular disease, IMPACT for traumatic brain injury, and FRAX for osteoporotic and hip fractures.

Source

Animal Research

TODO

Quality Improvement in Healthcare

Quality improvement research is about finding out how to improve and make changes in the most effective way. It is about systematically and rigourously exploring "what works" to improve quality in healthcare and the best ways to measure and disseminate this to ensure positive change. Most quality improvement effectiveness research is conducted in hospital settings, is focused on multiple quality improvement interventions, and uses process measures as outcomes. There is a great deal of variation in the research designs used to examine quality improvement effectiveness.

Source

Economic Evaluations in Healthcare

TODO

Meta Analyses

A meta-analysis is a statistical technique that amalgamates data from multiple studies to yield a single estimate of the effect size. This approach enhances precision and offers a more comprehensive understanding by integrating quantitative findings. Central to a meta-analysis is the evaluation of heterogeneity, which examines variations in study outcomes to ensure that differences in populations, interventions, or methodologies do not skew results. Techniques such as meta-regression or subgroup analysis are frequently employed to explore how various factors might influence the outcomes. This method is particularly effective when aiming to quantify the effect size, odds ratio, or risk ratio, providing a clearer numerical estimate that can significantly inform clinical or policy decisions.

How Meta-analyses and Systematic Reviews Work Together

Systematic reviews and meta-analyses function together, each complementing the other to provide a more robust understanding of research evidence. A systematic review meticulously gathers and evaluates all pertinent studies, establishing a solid foundation of qualitative and quantitative data. Within this framework, if the collected data exhibit sufficient homogeneity, a meta-analysis can be performed. This statistical synthesis allows for the integration of quantitative results from individual studies, producing a unified estimate of effect size. Techniques such as meta-regression or subgroup analysis may further refine these findings, elucidating how different variables impact the overall outcome. By combining these methodologies, researchers can achieve both a comprehensive narrative synthesis and a precise quantitative measure, enhancing the reliability and applicability of their conclusions. This integrated approach ensures that the findings are not only well-rounded but also statistically robust, providing greater confidence in the evidence base.

Why Don't All Systematic Reviews Use a Meta-Analysis?

Systematic reviews do not always have meta-analyses, due to variations in the data. For a meta-analysis to be viable, the data from different studies must be sufficiently similar, or homogeneous, in terms of design, population, and interventions. When the data shows significant heterogeneity, meaning there are considerable differences among the studies, combining them could lead to skewed or misleading conclusions. Furthermore, the quality of the included studies is critical; if the studies are of low methodological quality, merging their results could obscure true effects rather than explain them.

Protocol

A plan or set of steps that defines how something will be done. Before carrying out a research study, for example, the research protocol sets out what question is to be answered and how information will be collected and analysed.

Source

Systematic_review

A review that uses explicit, systematic methods to collate and synthesize findings of studies that address a clearly formulated question.

Source

Statistical synthesis

The combination of quantitative results of two or more studies. This encompasses meta-analysis of effect estimates (described below) and other methods, such as combining P values, calculating the range and distribution of observed effects, and vote counting based on the direction of effect (see McKenzie and Brennan for a description of each method)

Meta-analysis of effect estimates

A statistical technique used to synthesize results when study effect estimates and their variances are available, yielding a quantitative summary of results.

Source

Outcome

An event or measurement collected for participants in a study (such as quality of life, mortality).

Result

The combination of a point estimate (such as a mean difference, risk ratio or proportion) and a measure of its precision (such as a confidence/credible interval) for a particular outcome.

Reports

Documents (paper or electronic) supplying information about a particular study. A report could be a journal article, preprint, conference abstract, study register entry, clinical study report, dissertation, unpublished manuscript, government report, or any other document providing relevant information.

Record

The title or abstract (or both) of a report indexed in a database or website (such as a title or abstract for an article indexed in Medline). Records that refer to the same report (such as the same journal article) are “duplicates”; however, records that refer to reports that are merely similar (such as a similar abstract submitted to two different conferences) should be considered unique.

Study

An investigation, such as a clinical trial, that includes a defined group of participants and one or more interventions and outcomes. A “study” might have multiple reports. For example, reports could include the protocol, statistical analysis plan, baseline characteristics, results for the primary outcome, results for harms, results for secondary outcomes, and results for additional mediator and moderator analyses.