21b. Definition of who is included in each analysis

What to write

Definition of who is included in each analysis (eg, all randomised participants), and in which group

Examples

“The primary statistical analyses were performed according to the treatment to which the participants were randomly assigned. The analyses of the efficacy and safety outcomes (other than adverse events) included all available data from all randomized participants who contributed at least 1 value after baseline for the outcome of interest. The data that were obtained after a participant enrolled in another trial of an investigational treatment were excluded from the analyses. However, the participant was included in the analyses if that participant contributed at least 1 value after baseline for the outcome of interest prior to enrolling in another trial.”1

“Efficacy outcomes were assessed using intention-to-treat analysis (ie, the full set of all randomly assigned patients). Safety outcomes were assessed using the safety analysis set of all randomly allocated patients exposed to at least one dose of randomised intervention.”2

“Efficacy analyses and other exploratory analyses were performed in the full analysis set (defined as all patients randomly assigned to the study, including those who did not receive a dose of study treatment). Safety analyses were performed in the safety analysis set (defined as patients who received at least one dose of study treatment). The per protocol set was defined as all patients in the full analysis set who complied with the protocol in terms of exposure to study treatment, availability of tumour assessments, and absence of major protocol deviations likely to affect efficacy outcomes. Sensitivity analyses of the primary endpoint were performed on the per protocol analysis set.”3

“The primary analysis population was defined as all participants who completed baseline and 36-week assessments. The primary analysis of the primary outcome, AMCA [amended motor club assessment] score at 36 weeks, followed a modified intention-to-treat approach, regardless of compliance to the intervention, but did exclude patients who were deemed ineligible after randomisation, those who withdrew from the trial and were unwilling for their previously collected data to be used, or those who did not provide baseline and week 36 measurements.”4

Explanation

A key strength of a randomised trial design is the avoidance of bias when randomly allocating trial participants to interventions. To preserve the benefits of randomisation, all randomised participants are included in the analysis and retained in the group to which they were allocated. Meeting these two conditions defines an intention-to-treat analysis—which is widely recommended as the preferred analysis strategy.57 However, strict adherence to an intention-to-treat analysis is often difficult to achieve owing to missing outcomes for some trial participants (item 21c) or non-adherence to the trial intervention protocol. While imputation of missing outcomes would allow an intention-to-treat analysis, it does not guarantee an avoidance of bias except under strong assumptions about the missing data which may be unknown.

Various strategies for performing intention-to-treat analyses in the presence of missing outcome data are available.8 When the number of missing outcomes is not large, the analysis population could be all randomised participants with outcome observed (known as an “available case” population) under a plausible missing data mechanism, and sensitivity analyses could be performed exploring departures from this assumption (thereby using all randomised participants at least in sensitivity analyses).8 Concerns may arise when the frequency or the causes of dropping out differ between the intervention groups. Striving for intention-to-treat analysis by imputing values for participants with missing outcomes may lead to use of inadequate methods such as last observation carried forward.81011

Regardless of whether all randomised participants (completely observed outcomes or imputed outcomes) or a subset of randomised participants with observed outcomes are included in the primary analysis, the analysis population should be described. Authors often describe performing analyses on a “modified intention-to-treat” population to cover departures from a strict intention-to-treat that excludes participants who did not adequately adhere to the protocol such that they did not receive some minimum amount of the intervention—in such cases, what defines the minimum amount of the intervention should be explained (eg, those participants receiving at least one dose of the medication). It is also common to include analyses based on a per protocol population, which includes participants completing the study with no major protocol deviations. Excluding participants may compromise the randomisation and lead to biased estimates of treatment effects if appropriate methods are not used. Other analysis populations are possible (eg, a safety population), and their rationale and definition should be explained. Thus, authors should clearly define which participants are included in each analysis and in which intervention group and avoid terms such as “modified intention-to-treat” or “per protocol” analysis.

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.
Guglieri M, Bushby K, McDermott MP, et al. Effect of different corticosteroid dosing regimens on clinical outcomes in boys with duchenne muscular dystrophy: A randomized clinical trial. JAMA. 2022;327(15):1456. doi:10.1001/jama.2022.4315
2.
Davies M, Færch L, Jeppesen OK, et al. Semaglutide 2·4 mg once a week in adults with overweight or obesity, and type 2 diabetes (STEP 2): A randomised, double-blind, double-dummy, placebo-controlled, phase 3 trial. The Lancet. 2021;397(10278):971-984. doi:10.1016/s0140-6736(21)00213-0
3.
André F, Hee Park Y, Kim SB, et al. Trastuzumab deruxtecan versus treatment of physician’s choice in patients with HER2-positive metastatic breast cancer (DESTINY-Breast02): A randomised, open-label, multicentre, phase 3 trial. The Lancet. 2023;401(10390):1773-1785. doi:10.1016/s0140-6736(23)00725-0
4.
Freeman J, Hendrie W, Jarrett L, et al. Assessment of a home-based standing frame programme in people with progressive multiple sclerosis (SUMS): A pragmatic, multi-centre, randomised, controlled trial and cost-effectiveness analysis. The Lancet Neurology. 2019;18(8):736-747. doi:10.1016/s1474-4422(19)30190-5
5.
Nuesch E, Trelle S, Reichenbach S, et al. The effects of excluding patients from the analysis in randomised controlled trials: Meta-epidemiological study. BMJ. 2009;339(sep07 1):b3244-b3244. doi:10.1136/bmj.b3244
6.
Chalmers I, Matthews R, Glasziou P, Boutron I, Armitage P. Trial analysis by treatment allocated or by treatment received? Origins of “the intention-to-treat principle” to reduce allocation bias: Part 1. Journal of the Royal Society of Medicine. 2023;116(10):343-350. doi:10.1177/01410768231203922
7.
Chalmers I, Matthews R, Glasziou P, Boutron I, Armitage P. Trial analysis by treatment allocated or by treatment received? Origins of “the intention-to-treat principle” to reduce allocation bias: Part 2. Journal of the Royal Society of Medicine. 2023;116(11):386-394. doi:10.1177/01410768231203936
8.
White IR, Horton NJ, Carpenter J, statistics rimas, Pocock SJ. Strategy for intention to treat analysis in randomised trials with missing outcome data. BMJ. 2011;342(feb07 1):d40-d40. doi:10.1136/bmj.d40
9.
Lachin JM. Fallacies of last observation carried forward analyses. Clinical Trials. 2015;13(2):161-168. doi:10.1177/1740774515602688
10.
Molnar. 2009;3.
11.
White IR, Carpenter J, Horton NJ. Including all individuals is not enough: Lessons for intention-to-treat analysis. Clinical Trials. 2012;9(4):396-407. doi:10.1177/1740774512450098

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