14. Outcomes

What to write

Prespecified primary and secondary outcomes, including the specific measurement variable (eg, systolic blood pressure), analysis metric (eg, change from baseline, final value, time to event), method of aggregation (eg, median, proportion), and time point for each outcome

Example

See Table 1.1

Table 1: Five core elements of a defined outcome with examples MADRS=Montgomery-Asberg Depression Rating Scale.
Element term Definition used Example 1 Example 2 Example 3
Domain Title or concept to describe one or more outcomes Blood pressure Depression Death
       Measurement variable or specific measurement

 Corresponds to the data collected directly from the trial participants; description includes the instrument used to assess the outcome domain — — —
• Descriptive name Systolic blood pressure measured with Omran upper arm blood pressure monitor MADRS All cause mortality, per hospital database
• If applicable, the total score or subscale that will be analysed Not applicable MADRS total score Not applicable
Specific metric Participant level unit of measurement (eg, change from baseline, final value or a value at a time point, time to event) for the analysis Value at a time point Change from baseline Time to event
       Method of aggregation

 Procedure for estimating the treatment effect — — —
• If the outcome will be treated as continuous, categorical, or, time to event variable Continuous variable Binary variable Time to event
• For continuous variables, a measure of central tendency (eg, mean value); for categorical and time-to-event data variables, proportion with an event, and (if relevant) the specific cut-off values or categories compared Mean value Proportion of participants with ≥50% decrease Incidence density and between group incidence density rate
       Time point

 Timing of follow-up measurements — — —
• When outcome measurements will be obtained 2, 4, and 12 weeks after randomisation 2, 4, 6, and 8 weeks after randomisation Daily
• Which of the outcome measurements will be analysed 12 weeks after randomisation 8 weeks after randomisation End of follow-up

Table adapted from Butcher et al.1

Explanation

All randomised trials assess outcomes, for which the groups are compared. Most trials have several outcomes, some of which are of more importance than others. The primary outcome is the prespecified outcome considered to be of greatest importance to relevant stakeholders (such as patients, policy makers, clinicians, and funders) and should be the one used in the sample size calculation (item 16). The primary outcome should be explicitly indicated as such in the report of a randomised trial. Other outcomes of interest are secondary outcomes.

It is important to explain the rationale and clinical relevance for chosen efficacy and harm outcomes, including whether they are part of a core outcome set.2,3 A core outcome set is an agreed standardised set of outcomes that should be measured and reported, as a minimum, in all clinical trials in specific areas of health or health care. The COMET (Core Outcome Measures in Effectiveness Trials) initiative and COMET database facilitate access to core outcome sets (https://www.comet-initiative.org/).

Most trials have a single primary outcome. Having several primary outcomes can incur potential problems of interpretation associated with multiplicity of analyses (items 28 and 30). There are typically multiple secondary outcomes (ie, the outcomes prespecified in the trial protocol to assess any additional effects of the intervention). Secondary outcomes can include harms that may include unintended effects of the intervention (item 27).

The primary and secondary outcomes reported should be consistent with the outcomes prespecified in the trial protocol and the registry. Evidence shows important discrepancies between the outcome reported in the registry or protocol and outcomes reported in final publications, frequently in favour of statistically significant results. Any change in outcome(s) specified in the protocol should be reported, with reasons (item 10).4–6

All outcomes, whether primary or secondary, should be described and completely defined. This information is typically also detailed in the trial’s protocol and the trial registry. The principle here is that the information provided should be sufficient to allow others to use the same outcomes.7 For each outcome, it is important to detail: (1) the specific measurement variable, which corresponds to the data collected directly from trial participants (eg, Beck Depression Scale; all cause mortality) with definition where relevant (eg, major bleeding was defined as fatal bleeding or symptomatic bleeding in a critical area or organ; all cause mortality per hospital database); (2) the specific participant level analysis metric, which corresponds to the format of the outcome data that was used from each trial participant for analysis (eg, change from baseline; final value or value at a time point; time to event); (3) the method of aggregation, which refers to the summary measure format for each trial group (eg, mean; proportion of participants with score >2); and (4) the measurement time point of interest for analysis.8 For composite outcomes, all individual components of the composite outcome should be described as secondary outcomes.1 Only half of randomised trials published in PubMed indexed journals in 2000 and 2006 specified the primary outcome.9,10 In recent samples of trials published in specific fields, reporting has improved but still two thirds did not provide a complete definition.11,12

The use of previously developed and validated scales can help to enhance quality of measurement.13,14 For example, assessment of health related quality of life using a validated instrument is critical to the integrity and applicability of the trial.15 Authors should report measurement properties of outcome measurement instruments to assist in interpretation and comparison with similar studies.16

In most trials, information on outcomes is set to be collected as part of the trial conduct. However, some trials may use existing data collecting structures (eg, national, healthcare or administrative registries). This should be clarified in the methods. There is empirical evidence that treatment effect estimates may be different in trials where outcomes are obtained from routinely collected data.17

Training

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References

1.
Butcher NJ, Monsour A, Mew EJ, et al. Guidelines for reporting outcomes in trial reports: The CONSORT-outcomes 2022 extension. JAMA. 2022;328(22):2252. doi:10.1001/jama.2022.21022
2.
COMET. Core outcome measures in effectiveness trials. Https://comet-initiative.org/ [accessed 6 nov 2023].
3.
Hughes KL, Clarke M, Williamson PR. A systematic review finds core outcome set uptake varies widely across different areas of health. Journal of Clinical Epidemiology. 2021;129:114-123. doi:10.1016/j.jclinepi.2020.09.029
4.
Chan AW, Hróbjartsson A, Haahr MT, Gøtzsche PC, Altman DG. Empirical evidence for selective reporting of outcomes in randomized trials: Comparison of protocols to published articles. JAMA. 2004;291(20):2457. doi:10.1001/jama.291.20.2457
5.
Chan A-W. Outcome reporting bias in randomized trials funded by the canadian institutes of health research. Canadian Medical Association Journal. 2004;171(7):735-740. doi:10.1503/cmaj.1041086
6.
Estimating the prevalence of discrepancies between study registrations and publications: A systematic review and meta-analyses. BMJ Open. 2023;13(10):e076264. doi:10.1136/bmjopen-2023-076264
7.
Glasziou P, Meats E, Heneghan C, Shepperd S. What is missing from descriptions of treatment in trials and reviews? BMJ. 2008;336(7659):1472-1474. doi:10.1136/bmj.39590.732037.47
8.
Zarin DA, Tse T, Williams RJ, Califf RM, Ide NC. The ClinicalTrials.gov results database — update and key issues. New England Journal of Medicine. 2011;364(9):852-860. doi:10.1056/nejmsa1012065
9.
Chan AW, Altman DG. Epidemiology and reporting of randomised trials published in PubMed journals. The Lancet. 2005;365(9465):1159-1162. doi:10.1016/s0140-6736(05)71879-1
10.
Hopewell S, Dutton S, Yu L-M, Chan A-W, Altman DG. The quality of reports of randomised trials in 2000 and 2006: Comparative study of articles indexed in PubMed. BMJ. 2010;340(mar23 1):c723-c723. doi:10.1136/bmj.c723
11.
Bridgman AC, McPhie ML, Voineskos SH, Chan AW, Drucker AM. Reporting of primary outcome measures and sample size calculations in randomized controlled trials in dermatology journals. Journal of the American Academy of Dermatology. 2022;87(4):912-914. doi:10.1016/j.jaad.2021.12.022
12.
Stoll M, Lindner S, Marquardt B, et al. Completeness and consistency of primary outcome reporting in COVID-19 publications in the early pandemic phase: A descriptive study. BMC Medical Research Methodology. 2023;23(1). doi:10.1186/s12874-023-01991-9
13.
McDowell I. Measuring Health. Oxford University Press; 2006. doi:10.1093/acprof:oso/9780195165678.001.0001
14.
Streiner DL, Norman GR, Cairney J. Health Measurement Scales. Oxford University Press; 2015. doi:10.1093/med/9780199685219.001.0001
15.
Sanders C, Egger M, Donovan J, Tallon D, Frankel S. Reporting on quality of life in randomised controlled trials: Bibliographic study. BMJ. 1998;317(7167):1191-1194. doi:10.1136/bmj.317.7167.1191
16.
Clarke M. Standardising outcomes for clinical trials and systematic reviews. Trials. 2007;8(1). doi:10.1186/1745-6215-8-39
17.
Mc Cord KA, Ewald H, Agarwal A, et al. Treatment effects in randomised trials using routinely collected data for outcome assessment versus traditional trials: Meta-research study. BMJ. Published online March 2021:n450. doi:10.1136/bmj.n450

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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.

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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.

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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

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.

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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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