If applicable, eligibility criteria for sites and for individuals delivering the interventions (eg, surgeons, physiotherapists)
Example
“All participating centres . . . were major neurosurgical centres, treating large numbers of patients after aneurysmal subarachnoid haemorrhage (SAH), each centre treating between 60 and 200 cases annually . . . Centres had to have expertise in both neurosurgical and endovascular management of ruptured aneurysms. Only accredited neurosurgeons with experience of aneurysm surgery were permitted to manage patients in the trial. Endovascular operators had to have done a minimum of 30 aneurysm treatment procedures, before they were permitted to treat patients in the trial”.1
Explanation
For all types of trials, it is important to define the eligibility criteria for clinical sites, and to consider the characteristics of the treatment providers who will provide both the experimental and comparator interventions.2,3 Evidence suggests that patient outcomes can be associated with hospital and care provider volume.3 A systematic review of 135 trials found that 71% observed a positive association between hospital volume and outcomes, and 70% observed an association between care provider volume and outcomes.4 Different levels of expertise of care providers in each intervention group can bias treatment effect estimates.5 Furthermore, an intervention might be found to be safe and effective in a trial performed in high volume sites by high volume care providers but could have different results in low volume sites. For example, in an analysis of Medicare National Claim files of 167 208 patients who had undergone coronary stent surgery, patients treated by high volume physicians and at high volume sites experienced better outcomes than those treated by low volume physicians at low volume sites.6 Similar studies show that in most non-pharmacological trials, care provider expertise and site volume will influence treatment effects.7–9
Eligibility criteria for care providers and sites are often poorly reported. A systematic review of randomised trials in surgery found that the setting and the site volume of activity were reported in only 7% and 3% of articles, respectively. Eligibility criteria were reported for care providers in 41% of the articles, and the number of care providers performing the intervention was reported in 32%.10 A careful description of care providers involved in the trial, as well as details of the sites in which participants were treated, helps readers appraise the risk of bias and the applicability of the results.2,3 Eligibility criteria for sites typically relate to site volume for the procedure under investigation or similar procedures. Eligibility criteria for care providers might include professional qualifications, years in practice, number of interventions performed, skill as assessed by level of complication when performing the intervention, and specific training before trial initiation. Exclusion criteria should be justified because they will influence the applicability of the trial results.3
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References
1.
Molyneux A. International subarachnoid aneurysm trial (ISAT) of neurosurgical clipping versus endovascular coiling in 2143 patients with ruptured intracranial aneurysms: A randomised trial. The Lancet. 2002;360(9342):1267-1274. doi:10.1016/s0140-6736(02)11314-6
2.
Boutron I, Altman DG, Moher D, Schulz KF, Ravaud P. CONSORT statement for randomized trials of nonpharmacologic treatments: A 2017 update and a CONSORT extension for nonpharmacologic trial abstracts. Annals of Internal Medicine. 2017;167(1):40-47. doi:10.7326/m17-0046
3.
Boutron I, Moher D, Altman DG, Schulz KF, Ravaud P. Extending the CONSORT statement to randomized trials of nonpharmacologic treatment: Explanation and elaboration. Annals of Internal Medicine. 2008;148(4):295-309. doi:10.7326/0003-4819-148-4-200802190-00008
4.
Halm EA, Lee C, Chassin MR. Is volume related to outcome in health care? A systematic review and methodologic critique of the literature. Annals of Internal Medicine. 2002;137(6):511-520. doi:10.7326/0003-4819-137-6-200209170-00012
5.
Devereaux PJ, Bhandari M, Clarke M, et al. Need for expertise based randomised controlled trials. BMJ. 2005;330(7482):88. doi:10.1136/bmj.330.7482.88
6.
McGrath PD. Relation between operator and hospital volume and outcomes following percutaneous coronary interventions in the era of the coronary stent. JAMA. 2000;284(24):3139. doi:10.1001/jama.284.24.3139
7.
Hodgson DC, Zhang W, Zaslavsky AM, Fuchs CS, Wright WE, Ayanian JZ. Relation of hospital volume to colostomy rates and survival for patients with rectal cancer. JNCI Journal of the National Cancer Institute. 2003;95(10):708-716. doi:10.1093/jnci/95.10.708
8.
Khuri SF, Daley J, Henderson W, et al. Relation of surgical volume to outcome in eight common operations: Results from the VA national surgical quality improvement program. Annals of Surgery. 1999;230(3):414. doi:10.1097/00000658-199909000-00014
9.
Urbach DR. Does it matter what a hospital is “high volume” for? Specificity of hospital volume-outcome associations for surgical procedures: Analysis of administrative data. Quality and Safety in Health Care. 2004;13(5):379-383. doi:10.1136/qhc.13.5.379
10.
Jacquier I, Boutron I, Moher D, Roy C, Ravaud P. The reporting of randomized clinical trials using a surgical intervention is in need of immediate improvement: A systematic review. Annals of Surgery. 2006;244(5):677-683. doi:10.1097/01.sla.0000242707.44007.80
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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.
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).
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.
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.
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"}.
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.
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.
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.
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.
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.
