7. Objectives

What to write

Specific objectives related to benefits and harms

Example

“To evaluate whether a structured exercise programme improved functional and health related quality of life outcomes compared with usual care for women at high risk of upper limb disability after breast cancer surgery.”1

Explanation

Objectives are the questions that the trial was designed to answer. Adequate reporting of the research question is essential to allow readers to appraise and interpret the trial results. The PICO framework, which requires defining the patient population (P); the experimental intervention (I); the comparator intervention or condition (C); and the outcome or outcomes (O) of interest, has been proposed to help define the research question. PICO is sometimes styled as PICOTS, to include T (the timeframe) and/or S (the setting).2

Treatment decisions require an evaluation of the balance between benefit and harm; however, information about harms is frequently omitted or incompletely reported in published reports of trial results.36 Trials whose primary objective is to evaluate benefits of an intervention may not be powered to detect harms, but authors should still report whether they considered harms outcomes when planning the trial.7

Authors should clarify whether the aim is to establish superiority of the experimental intervention, or non-inferiority or equivalence, as compared with the comparator intervention.8 Authors should also report whether the trial is intended to provide preliminary data (a pilot or feasibility trial),9 explore pharmacokinetic properties, or generate confirmatory results.

For multi-arm trials, authors should clarify which treatment group comparisons are of interest (eg, A v B; A v C). If authors planned to readjust the objective during the trial (eg, in some platform trials or basket trials10), this should be reported. Finally, trials can be designed to study the effect of the experimental intervention under different conditions, often described on a spectrum from ideal conditions (explanatory trial) to standard clinical care conditions (pragmatic trial).11

The objectives should be phrased using neutral wording (eg, “to compare the effect of treatment A versus treatment B on outcome X for persons with condition Y”) rather than in terms of a particular direction of effect.12 The trial objectives should align with what was specified in the trial registry and protocol; any changes to the trial objectives after it commenced should be reported with reasons (item 10).

Recently, some trials have been designed using the estimands framework to define the research question and trial objectives. While the terminology surrounding estimands may be new to some investigators, it is expected that the use of this framework will become more widespread. Box 1 provides more information about the estimands framework and how it is being used.

Estimands

Concerns have been raised that the precise research questions that randomised trials set out to answer are often unclear.13 In particular, there is often ambiguity around how events occurring after randomisation (termed intercurrent events) are handled. Specifying the research question using an estimands framework is increasingly used to improve clarity. Despite calls for estimands to be included in the CONSORT 2025 statement,13,14 their inclusion did not reach consensus. However, we provide a brief overview of estimands and introduce terminology, so they can be applied and reported if used. A more detailed primer on the estimand framework which provides practical guidance on estimands in studies of healthcare interventions can be found elsewhere.14

ICH E9(R1) defines an estimand as “a precise description of the treatment effect reflecting the clinical question posed by a given clinical trial objective.”15 The estimands framework provides a structured description of the objectives in an attempt to bring clarity in specifying the research question, which can be used to guide the study design, data collection, and statistical analysis methods. In brief, an estimand comprises five key attributes: population, treatment groups, endpoint, summary measure, and handling of intercurrent events (see Table 1). A separate estimand should be defined for each study outcome, and for some outcomes, more than one estimand may be defined.

Table 1: Five key attributes of the estimand framework14.
Attribute Definition
Population Patients for whom researchers want to estimate the treatment effect
Treatment groups Different intervention strategies being compared in the treatment effect definition
Endpoint Outcome for each participant that is used in the treatment effect definition
Summary measure Method used to summarise and compare the endpoint between treatment conditions (eg, risk ratio, odds ratio)
Handling of intercurrent events Strategies used to handle each intercurrent event (see below) in the treatment effect definition; different strategies could be used for different types of intercurrent events

Intercurrent events are post-baseline events (or post-randomisation events in randomised trials) that affect the interpretation or existence of outcome data. These events frequently affect receipt of treatment (eg, treatment switching or treatment discontinuation) or preclude existence of the outcome (eg, death, if it is not defined as part of the outcome).

The ICH E9(R1) outlines five strategies for handling intercurrent events, which are at the core of the estimand framework (see Table 2).

Table 2: Strategies for handling intercurrent events
Strategy Description
Treatment policy The occurrence of the intercurrent event is considered irrelevant in defining the treatment effect of interest: the value for the outcome of interest is used regardless of whether the intercurrent event occurs
Hypothetical The treatment effect in a scenario where the intercurrent event did not occur is of interest
Composite The intercurrent event is incorporated into the outcome definition
While on treatment The outcome before the occurrence of the intercurrent event is of interest
Principal stratum The outcome in a subpopulation of patients who would not (or would) experience the intercurrent event is of interest.

Although the terminology surrounding estimands may be new to some investigators, it is expected that defining research questions using the estimands framework will become more widespread. A number of existing reporting guidelines have recently included estimands with the reporting recommendations.1618 If the estimands framework has been used to design the trial or the data collection or to inform the statistical analysis (by guiding choice of appropriate methods), then this should be made clear in the manuscript and the methods and results should be reported within the framework.

Training

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

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References

1.
Bruce J, Mazuquin B, Canaway A, et al. Exercise versus usual care after non-reconstructive breast cancer surgery (UK PROSPER): Multicentre randomised controlled trial and economic evaluation. BMJ. Published online November 2021:e066542. doi:10.1136/bmj-2021-066542
2.
AHRQ. Using the PICOTS framework to strengthen evidence gathered in clinical trials—guidance from the AHRQ’s evidence-based practice centers program. Available at: Https://www.fda.gov/media/109448/download [accessed 13 march 2024].
3.
Golder S, Loke YK, Wright K, Norman G. Reporting of adverse events in published and unpublished studies of health care interventions: A systematic review. Ioannidis JP, ed. PLOS Medicine. 2016;13(9):e1002127. doi:10.1371/journal.pmed.1002127
4.
Lineberry N, Berlin JA, Mansi B, et al. Recommendations to improve adverse event reporting in clinical trial publications: A joint pharmaceutical industry/journal editor perspective. BMJ. Published online October 2016:i5078. doi:10.1136/bmj.i5078
5.
Junqueira DR, Phillips R, Zorzela L, et al. Time to improve the reporting of harms in randomized controlled trials. Journal of Clinical Epidemiology. 2021;136:216-220. doi:10.1016/j.jclinepi.2021.04.020
6.
Phillips R, Hazell L, Sauzet O, Cornelius V. Analysis and reporting of adverse events in randomised controlled trials: A review. BMJ Open. 2019;9(2):e024537. doi:10.1136/bmjopen-2018-024537
7.
Junqueira DR, Zorzela L, Golder S, et al. CONSORT harms 2022 statement, explanation, and elaboration: Updated guideline for the reporting of harms in randomised trials. BMJ. Published online April 2023:e073725. doi:10.1136/bmj-2022-073725
8.
Piaggio G, Elbourne DR, Pocock SJ, Evans SJW, Altman DG, CONSORT Group for the. Reporting of noninferiority and equivalence randomized trials: Extension of the CONSORT 2010 statement. JAMA. 2012;308(24):2594. doi:10.1001/jama.2012.87802
9.
Eldridge SM, Chan CL, Campbell MJ, et al. CONSORT 2010 statement: Extension to randomised pilot and feasibility trials. BMJ. Published online October 2016:i5239. doi:10.1136/bmj.i5239
10.
Park JJH, Harari O, Dron L, Lester RT, Thorlund K, Mills EJ. An overview of platform trials with a checklist for clinical readers. Journal of Clinical Epidemiology. 2020;125:1-8. doi:10.1016/j.jclinepi.2020.04.025
11.
Schwartz D, Lellouch J. Explanatory and pragmatic attitudes in therapeutical trials. Journal of Clinical Epidemiology. 2009;62(5):499-505. doi:10.1016/j.jclinepi.2009.01.012
12.
Fleming TR. Clinical trials: Discerning hype from substance. Annals of Internal Medicine. 2010;153(6):400-406. doi:10.7326/0003-4819-153-6-201009210-00008
13.
Cro S, Kahan BC, Rehal S, et al. Evaluating how clear the questions being investigated in randomised trials are: Systematic review of estimands. BMJ. Published online August 2022:e070146. doi:10.1136/bmj-2022-070146
14.
Kahan BC, Hindley J, Edwards M, Cro S, Morris TP. The estimands framework: A primer on the ICH E9(R1) addendum. BMJ. Published online January 2024:e076316. doi:10.1136/bmj-2023-076316
15.
European medicines agency. ICH E9 (R1) addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles for clinical trials. Available at: Https://www.ema.europa.eu/en/ documents/scientific-guideline/ich-e9-r1-addendum-estimands- sensitivity-analysis-clinical-trials-guideline-statistical-principles_en.pdf. 2020.
16.
Homer V, Yap C, Bond S, et al. Early phase clinical trials extension to guidelines for the content of statistical analysis plans. BMJ. Published online February 2022:e068177. doi:10.1136/bmj-2021-068177
17.
Kahan BC, Hall SS, Beller EM, et al. Reporting of factorial randomized trials: Extension of the CONSORT 2010 statement. JAMA. 2023;330(21):2106. doi:10.1001/jama.2023.19793
18.
Kahan BC, Hall SS, Beller EM, et al. Consensus statement for protocols of factorial randomized trials: Extension of the SPIRIT 2013 statement. JAMA Network Open. 2023;6(12):e2346121. doi:10.1001/jamanetworkopen.2023.46121

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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