4. Data sharing

What to write

Where and how the individual de-identified participant data (including data dictionary), statistical code, and any other materials can be accessed

Examples

“All data requests should be submitted to the corresponding author (AR) for consideration as agreed in our publication plan. Access to anonymised data may be granted following review with the Trial Management Group and agreement of the chief investigator (AR).”1

“Deidentified data collected and presented in this study, including individual participant data and a data dictionary defining each field in the set, will be made available upon reasonable request after publication of this Article, following approval by regulatory authorities. Data can be requested by contacting the corresponding author.”2

Explanation

Data and code sharing can take the transparency of trial reporting to a different, more desirable level. Sharing individual de-identified participant data would be helpful in many ways: verifying results and increasing trust; using data more extensively for secondary analyses; and using data for individual patient data meta-analysis (IPD MA). Data sharing is also associated with increased citations3 (ie, broader dissemination). Some trial groups have worked collaboratively to conduct IPD MA.4 However, for most randomised trials, data sharing does not happen.59 During the covid-19 pandemic, there were many examples of authors’ intentions to share data that then did not transpire (ie, they did not share their data).8,10 There is increasing concern that some trials are fraudulent or considered to be so-called zombie trials, which becomes evident only on inspection of the raw data.11,12 However, even if zombie trials are not as prevalent as feared, genuine trials can have such an important role and high value that it is important to maximise their utility by making them more open. Detailed documentation of sharing plans may help in this direction.13

All data sharing should abide by the principle of being as open as possible and as closed as necessary throughout a randomised trial’s life cycle (from SPIRIT to CONSORT). It is important to ensure that all the appropriate permissions are included on the patient consent forms. Trials cannot share data that are not fully anonymised without the appropriate patient consent, and full anonymisation can be difficult. Care must be taken to share participant data appropriately to maintain confidentiality. Suitable mechanisms must be in place to appropriately de-identify participant data, and data should only be shared in a safe and secure manner that fits with the consent obtained from participants.

Data sharing typically involves sharing: the underlying data generated from the trial’s conduct; a data dictionary (ie, structure, content, and meaning of each data variable); and other relevant material(s) used as part of the trial’s analysis such as the trial protocol, data management plan, statistical analysis plan, and code used to analyse the data. A trial’s data can be shared in a variety of ways, such as via an institutional repository (eg, belonging to the university associated with the trial’s coordinating centre) and/or a public-facing repository, or by having a bespoke process to provide data. Often, a data use agreement is necessary, which will, at a minimum: prohibit attempts to reidentify or contact trial participants; address any requirements regarding planned outputs of the proposed research (eg, publication and acknowledgment requirements); and prohibit non-approved uses or further distribution of the data.14

In a growing number of jurisdictions, funders such as the National Institute for Health (NIH),15 in the US and the National Institute for Health and Care Research (NIHR) in the UK, alongside other funders such as the Gate’s Foundation, now require researchers to share their data and make the results publicly available for anyone to read. Similarly, some journals are also requiring authors to include a data sharing statement as part of the article submission process (eg, Annals of Internal Medicine, The BMJ, JAMA Network journals, PLoS Medicine).

The process of signalling how data sharing will be achieved is often contained in a data management plan but may also be found in the trial protocol or statistical analysis plan. More complete details regarding developing a data management plan are beyond the scope of this paper. Such details can be found elsewhere.16 Authors should provide some description of where these details can be found (eg, name of repository and URL to data, code, and materials). Sharing may also entail embargo periods, and if so, the choice of an embargo should be justified and its length should be stated.17 If data (or some parts thereof) cannot be shared, the reasons for this should be reported and should be sensible and following ethical principles.

For more complex trials (eg, types of talking therapies, physiotherapy), additional materials to share might include a handbook and/or video to detail the intervention.14 Often these can be shared much more freely than the data, as there are fewer issues with confidentiality.

Training

The UK EQUATOR Centre runs training on how to write using reporting guidelines.

Discuss this item

Visit this items’ discussion page to ask questions and give feedback.

References

1.
Rangan A, Brealey SD, Keding A, et al. Management of adults with primary frozen shoulder in secondary care (UK FROST): A multicentre, pragmatic, three-arm, superiority randomised clinical trial. The Lancet. 2020;396(10256):977-989. doi:10.1016/s0140-6736(20)31965-6
2.
Roaldsen MB, Eltoft A, Wilsgaard T, et al. Safety and efficacy of tenecteplase in patients with wake-up stroke assessed by non-contrast CT (TWIST): A multicentre, open-label, randomised controlled trial. The Lancet Neurology. 2023;22(2):117-126. doi:10.1016/s1474-4422(22)00484-7
3.
Colavizza G, Hrynaszkiewicz I, Staden I, Whitaker K, McGillivray B. The citation advantage of linking publications to research data. Wicherts JM, ed. PLOS ONE. 2020;15(4):e0230416. doi:10.1371/journal.pone.0230416
4.
Stewart LA, Clarke M, Rovers M, et al. Preferred reporting items for a systematic review and meta-analysis of individual participant data: The PRISMA-IPD statement. JAMA. 2015;313(16):1657. doi:10.1001/jama.2015.3656
5.
Naudet F, Sakarovitch C, Janiaud P, et al. Data sharing and reanalysis of randomized controlled trials in leading biomedical journals with a full data sharing policy: Survey of studies published inThe BMJandPLOS medicine. BMJ. Published online February 2018:k400. doi:10.1136/bmj.k400
6.
Bergeat D, Lombard N, Gasmi A, Le Floch B, Naudet F. Data sharing and reanalyses among randomized clinical trials published in surgical journals before and after adoption of a data availability and reproducibility policy. JAMA Network Open. 2022;5(6):e2215209. doi:10.1001/jamanetworkopen.2022.15209
7.
Rowhani-Farid A, Grewal M, Solar S, et al. Clinical trial data sharing: A cross-sectional study of outcomes associated with two u.s. National institutes of health models. Scientific Data. 2023;10(1). doi:10.1038/s41597-023-02436-0
8.
Esmail LC, Kapp P, Assi R, et al. Sharing of individual patient-level data by trialists of randomized clinical trials of pharmacological treatments for COVID-19. JAMA. 2023;329(19):1695. doi:10.1001/jama.2023.4590
9.
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(8):e049228. doi:10.1136/bmjopen-2021-049228
10.
Larregue j, vincent-lamarre p, lebaron f, larivière v. COVID-19: Where is the data? LSE impact blog, 30 novembre 2020. Https://unesco.ebsi.umontreal.ca/en/publications/covid-19-where-is-the-data/.
11.
Ioannidis JPA. Hundreds of thousands of zombie randomised trials circulate among us. Anaesthesia. 2020;76(4):444-447. doi:10.1111/anae.15297
12.
Carlisle JB. False individual patient data and zombie randomised controlled trials submitted to anaesthesia. Anaesthesia. 2020;76(4):472-479. doi:10.1111/anae.15263
13.
Pellen C, Le Louarn A, Spurrier-Bernard G, et al. Ten (not so) simple rules for clinical trial data-sharing. Schwartz R, ed. PLOS Computational Biology. 2023;19(3):e1010879. doi:10.1371/journal.pcbi.1010879
14.
Smith CT, Hopkins C, Sydes M, et al. Good practice principles for sharing individual participant data from publicly funded clinical trials. Trials. 2015;16(S2). doi:10.1186/1745-6215-16-s2-o1
15.
The white house. OSTP issues guidance to make federally funded research freely available without delay. Https://www.whitehouse.gov/ostp/news-updates/2022/08/25/ostp-issues-guidance-to-make-federally-funded-research-freely-available-without-delay/ [accessed 17 september 2024]. 2022.
16.
National institutes of health. Data management and sharing policy. Https://sharing.nih.gov/data-management-and-sharing-policy/data-management#: :text=NIH.
17.
Siebert M, Ioannidis JPA. Lifting of embargoes to data sharing in clinical trials published in top medical journals. JAMA. 2024;331(4):354. doi:10.1001/jama.2023.25394

Reuse

Most of the reporting guidelines and checklists on this website were originally published under permissive licenses that allowed their reuse. Some were published with propriety licenses, where copyright is held by the publisher and/or original authors. The original content of the reporting checklists and explanation pages on this website were drawn from these publications with knowledge and permission from the reporting guideline authors, and subsequently revised in response to feedback and evidence from research as part of an ongoing scholarly dialogue about how best to disseminate reporting guidance. The UK EQUATOR Centre makes no copyright claims over reporting guideline content. Our use of copyrighted content on this website falls under fair use guidelines.

Citation

For attribution, please cite this work as:
Hopewell S, Chan AW, Collins GS, et al. CONSORT 2025 statement: updated guideline for reporting randomised trials. BMJ. 2025;389:e081123. doi:10.1136/bmj-2024-081123

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.

Smoother publishing

Many journals require completed reporting checklists at submission.

Maximum impact

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

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]{.defined data-bs-toggle="offcanvas" href="#glossaryItemmeta_analyses" aria-controls="offcanvasExample" role="button"}.

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 behaviour. 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 people 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

Asdfghj

sdfghjk