6. Background and rationale

What to write

Scientific background and rationale

Example

“Most problems with shoulder pain are managed in primary care by physiotherapists and GPs [general practitioners] . . . Evidence from small, short-term trials suggests that physiotherapist-prescribed exercise is promising. However, a Cochrane review highlighted the insufficient evidence about the treatment's long-term clinical effectiveness and cost-effectiveness. Despite widespread provision, uncertainty exists about which types of exercise and levels of physiotherapy supervision are associated with the best outcomes. This evidence is limited by problems in study design and lack of comparator groups. Progressive resistance training to improve muscular strength, whether supervised or home based, has been identified as a core component of exercise for patients with rotator cuff disorders. Subacromial corticosteroid injections are commonly used to reduce local tissue inflammation and pain. Compared with placebo, corticosteroid injections have short-term benefit in the shoulder, although the longer-term benefits and harms are not known. Corticosteroid injections are being used increasingly in clinical practice alongside physiotherapy for the management of people with rotator cuff disorders; hence justification for investigating corticosteroid injection in the GRASP (Getting it Right: Addressing Shoulder Pain) trial alongside physiotherapist-prescribed exercise.”1

Explanation

Typically, the introduction of the trial report consists of free-flowing text, in which authors explain the scientific background and rationale for their trial, and its general outline. The rationale may be explanatory (eg, to assess the possible influence of a drug on renal function under tightly regulated conditions) or pragmatic (eg, to guide practice by comparing the benefits and harms of two treatments in a clinical setting). Authors should report any evidence of the benefits and harms of active interventions included in a trial and should suggest a plausible explanation for how the interventions might work, if this is not obvious. Understanding the rationale or theory underpinning an intervention helps readers to understand which aspects or components are likely to be essential to its efficacy.2 In addition, authors should justify the choice of comparator(s).3,4 The choice of the comparator (active or placebo) will influence effect estimates. It could raise ethical concerns if patients could be allocated to a placebo or to a suboptimal treatment while an active treatment has been proven effective. Authors should justify the need for the trial they conducted and show that there was equipoise about the best treatment for the condition in the population being studied.

The Declaration of Helsinki states that biomedical research involving people should be based on a thorough knowledge of the scientific literature.5 It is unethical to expose humans unnecessarily to the risks of research. Some clinical trials have been shown to have been unnecessary because the question they addressed had been, or could have been, answered by a systematic review of the existing literature.68 Thus, the need for a new trial should be justified in the introduction. Ideally, this justification should include a reference to one or more systematic reviews of previous trials. In the absence of a published systematic review, authors should report and summarise the results of previous relevant trials or note their absence.9 The percentage of published trial reports that cite a systematic review of pre-existing evidence where one is available has increased over time, but over a quarter still fail to do so.10

Training

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References

1.
Hopewell S, Keene DJ, Marian IR, et al. Progressive exercise compared with best practice advice, with or without corticosteroid injection, for the treatment of patients with rotator cuff disorders (GRASP): A multicentre, pragmatic, 2 × 2 factorial, randomised controlled trial. The Lancet. 2021;398(10298):416-428. doi:10.1016/s0140-6736(21)00846-1
2.
Hoffmann TC, Glasziou PP, Boutron I, et al. Better reporting of interventions: Template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014;348(mar07 3):g1687-g1687. doi:10.1136/bmj.g1687
3.
Dawson L, Zarin DA, Emanuel EJ, Friedman LM, Chaudhari B, Goodman SN. Considering usual medical care in clinical trial design. PLoS Medicine. 2009;6(9):e1000111. doi:10.1371/journal.pmed.1000111
4.
Luijn JC van, Loenen AC van, Gribnau FW, Leufkens HG. Choice of comparator in active control trials of new drugs. Annals of Pharmacotherapy. 2008;42(11):1605-1615. doi:10.1345/aph.1l115
5.
World medical association declaration of helsinki. Ethical principle for medical research involving human participants. 75th WMA general assembly, finland, 2024. Https://www.wma.net/policies-post/wma-declaration-of-helsinki/ (accessed 10 february 2025).
6.
Lau J, Antman EM, Jimenez-Silva J, Kupelnick B, Mosteller F, Chalmers TC. Cumulative meta-analysis of therapeutic trials for myocardial infarction. New England Journal of Medicine. 1992;327(4):248-254. doi:10.1056/nejm199207233270406
7.
Fergusson D, Glass KC, Hutton B, Shapiro S. Randomized controlled trials of aprotinin in cardiac surgery: Could clinical equipoise have stopped the bleeding? Clinical Trials. 2005;2(3):218-232. doi:10.1191/1740774505cn085oa
8.
Clarke M, Brice A, Chalmers I. Accumulating research: A systematic account of how cumulative meta-analyses would have provided knowledge, improved health, reduced harm and saved resources. Gluud LL, ed. PLoS ONE. 2014;9(7):e102670. doi:10.1371/journal.pone.0102670
9.
Savulescu J, Chalmers I, Blunt J. Are research ethics committees behaving unethically? Some suggestions for improving performance and accountability. BMJ. 1996;313(7069):1390-1393. doi:10.1136/bmj.313.7069.1390
10.
Jia Y, Li B, Yang Z, et al. Trends of randomized clinical trials citing prior systematic reviews, 2007-2021. JAMA Network Open. 2023;6(3):e234219. doi:10.1001/jamanetworkopen.2023.4219

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).

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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.

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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.

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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"}.

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Systematic review protocols

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Meta analyses of Observational Studies

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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.

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Randomised Trial Protocols

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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.

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Case Reports

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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.

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Animal Research

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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.

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Economic Evaluations in Healthcare

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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.

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