17b. Type of Randomisation

What to write

Type of randomisation and details of any restriction (eg, stratification, blocking, and block size)

Examples

“Treatment assignment was generated using a simple randomization scheme . . . given the open-label nature of the intervention to limit the potential bias due to predictable treatment assignment.”1

“Randomization (1:1) was performed by an independent researcher using computer generated random table numbers, with a block size of 20 and stratified for the indication of the IUI [intrauterine insemination] (mild male factor or unexplained subfertility).”2

“Participants were randomized at an individual-level (1:1 ratio) and were stratified by recruitment location (VU [Vrije University] and UvA [University of Amsterdam]). Block randomization was applied with randomly varied block sizes (6–12 allocations per block).”3

“Randomization was stratified by treatment centre, clinical severity (<4 vs >4 on a Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale standardized to range from 0 to 10), and by whether patients had previously received TENS [transcutaneous electrical nerve stimulation] with randomly varied block sizes of 2, 4, and 6.”4

“Randomization sequence was created using Stata 9.0 (StataCorp., College Station, TX) statistical software and was stratified by center with a 1:1 allocation using random block sizes of 2,4, and 6.”5

Explanation

In trials of several hundred participants or more, simple randomisation can usually be trusted to generate similar numbers in the two trial groups6 and to generate groups that are roughly comparable in terms of known and unknown prognostic variables.7 For smaller trials of fewer than around 200 participants,8 which are common, some form of restricted randomisation procedure to help achieve balance between groups in size or characteristics may be useful (box 6). However, larger trials of greater than approximately 200 participants may also benefit from registration. For example, they may stop before reaching their target size, they may need more power at interim analyses, or they may benefit from stratification with restriction.

Randomisation and minimisation

Simple randomisation

Pure randomisation based on a single allocation ratio is known as simple randomisation. Simple randomisation with a 1:1 allocation ratio is analogous to a coin toss, although we do not advocate coin tossing for randomisation in a randomised trial. The term “simple” is somewhat of a misnomer. While other randomisation schemes sound complex and more sophisticated, in reality, simple randomisation is elegantly sophisticated in that it is more unpredictable and could surpass the bias prevention levels of all other alternatives.

Restricted randomisation

Restricted randomisation specifies any randomised approach that is not simple randomisation. Blocked randomisation is the most common form. Other means of restricted randomisation include replacement, biased coin, and urn randomisation, although these are used much less frequently.8

Blocked randomisation

Blocking can be used to ensure close balance of the numbers in each group at any time during the trial. After a block of every eight participants was assigned, for example, four would be allocated to each arm of the trial.9 Improved balance comes at the cost of reducing the unpredictability of the sequence. Although the order of interventions varies randomly within each block, a person running the trial could deduce some of the next treatment allocations if they discovered the block size.10 Blinding the interventions, using larger block sizes, and randomly varying the block size can ameliorate this problem.

Stratified randomisation

Stratification is used to ensure a good balance of participant characteristics in each group. By chance, particularly in small trials, trial groups may not be well matched for baseline characteristics, such as age and stage of disease. This weakens the trial’s credibility.11 Such imbalances can be avoided without sacrificing the advantages of randomisation. Stratification ensures that the numbers of participants receiving each intervention are closely balanced within each stratum. Stratified randomisation is achieved by performing a separate randomisation procedure within each of two or more subsets of participants (eg, those defining each centre, age, or disease severity). Stratification by centre is common in multicentre trials. Stratification requires some form of restriction, such as blocking within strata. Stratification without some form of restriction is ineffective.

Minimisation

Minimisation improves balance between intervention groups for several selected patient factors (eg, age).12,13 The first patient is truly randomly allocated; for each subsequent participant, the treatment allocation that minimises the imbalance on the selected factors between groups at that time is identified. That allocation may then be used, or a choice may be made at random with a heavy weighting in favour of the intervention that would minimise imbalance (eg, with a probability of 0.8). The use of a random component is generally preferable. Minimisation has the advantage of creating small groups closely similar in terms of measurable participant characteristics at all stages of the trial.

Minimisation offers the only acceptable alternative to randomisation, and some have argued that it is superior.14 Conversely, minimisation lacks the theoretical basis for eliminating bias on all known and unknown factors. Nevertheless, in general, trials that use minimisation are considered methodologically equivalent to randomised trials, even when a random element is not incorporated.

It is important to indicate whether no restriction was used by stating such or by stating that simple randomisation was done. Otherwise, the methods used to restrict the randomisation, along with the method used for random selection, should be specified. For blocked randomisation, authors should provide details on how the blocks were generated (eg, by using a permuted block design with a computer random number generator), the block size or sizes, and whether the block size was fixed or randomly varied. If the trialists became aware of the block size(s), that information should also be reported as such knowledge could lead to them correctly deciphering future treatment assignments. Authors should specify whether stratification was used and, if so, which factors (eg, recruitment site, sex, disease stage) were involved; the categorisation cut-off thresholds within stratums; and the method used for restriction. Although stratification is a useful technique, especially for smaller trials, it can be complicated to implement and may not perform as well as expected if many stratifying factors are used. If minimisation (box 6) was used, it should be explicitly identified, as should the variables incorporated into the scheme; whether a random element was used should also be stated.

With blocking, although the order of interventions varies randomly within each block, individuals running the trial could deduce some of the future treatment allocations if they discovered the block size.10 Discovering block sizes is much more likely in unblinded trials, where treatment allocations become known after assignment (box 6). Certain techniques, such as large block sizes and randomly varying block sizes, can help prevent the deciphering of future treatment allocations. Unfortunately, particularly with unblinded trials, a review “found that very few trials used techniques that would eliminate the risk of selection bias,” and that “These findings indicate that a substantial proportion of unblinded trials are at risk of selection bias.”15 Indeed, in a recent study of 179 open, unblinded randomised trials, small block sizes were associated with subversion.16

Only 9% of 206 reports of trials in specialty journals17 and 39% of 80 trials in general medical journals reported use of stratification.18 In each case, only about half of the reports mentioned the use of restricted randomisation. Those studies and that of Adetugbo and Williams19 found that the sizes of the treatment groups in many trials were very often the same or quite similar, yet blocking or stratification had not been mentioned. One of a few possible causes of this close balance in numbers is under-reporting of the use of restricted randomisation, although non-random manipulation of treatment assignments is also suspected. A more recent study of 298 reports of trials in general medical journals found 69% reported the use of a stratified block method.20

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.
Mentz RJ, Anstrom KJ, Eisenstein EL, et al. Effect of torsemide vs furosemide after discharge on all-cause mortality in patients hospitalized with heart failure: The TRANSFORM-HF randomized clinical trial. JAMA. 2023;329(3):214. doi:10.1001/jama.2022.23924
2.
Statema-Lohmeijer CH, Schats R, Lissenberg-Witte BI, Kostelijk EH, Lambalk CB, Vergouw CG. A short versus a long time interval between semen collection and intrauterine insemination: A randomized controlled clinical trial. Human Reproduction. 2023;38(5):811-819. doi:10.1093/humrep/dead044
3.
Karyotaki E, Klein AM, Ciharova M, et al. Guided internet-based transdiagnostic individually tailored cognitive behavioral therapy for symptoms of depression and/or anxiety in college students: A randomized controlled trial. Behaviour Research and Therapy. 2022;150:104028. doi:10.1016/j.brat.2021.104028
4.
Reichenbach S, Jüni P, Hincapié CA, et al. Effect of transcutaneous electrical nerve stimulation (TENS) on knee pain and physical function in patients with symptomatic knee osteoarthritis: The ETRELKA randomized clinical trial. Osteoarthritis and Cartilage. 2022;30(3):426-435. doi:10.1016/j.joca.2021.10.015
5.
Creinin MD, Meyn LA, Borgatta L, et al. Multicenter comparison of the contraceptive ring and patch: A randomized controlled trial. Obstetrics &amp; Gynecology. 2008;111(2):267-277. doi:10.1097/01.aog.0000298338.58511.d1
6.
Lachin JM. Properties of simple randomization in clinical trials. Controlled Clinical Trials. 1988;9(4):312-326. doi:10.1016/0197-2456(88)90046-3
7.
Peto R, Pike MC, Armitage P, et al. Design and analysis of randomized clinical trials requiring prolonged observation of each patient. I. Introduction and design. British Journal of Cancer. 1976;34(6):585-612. doi:10.1038/bjc.1976.220
8.
Schulz k grimes DA . Essential concepts in clinical research: Randomized controlled trials and observational epidemiology. 2nd ed. Elsevier, 2019.
9.
Altman DG, Bland JM. Statistics notes: How to randomise. BMJ. 1999;319(7211):703-704. doi:10.1136/bmj.319.7211.703
10.
Schulz KF. Subverting randomization in controlled trials. JAMA: The Journal of the American Medical Association. 1995;274(18):1456. doi:10.1001/jama.1995.03530180050029
11.
Enas GG, Enas NH, Spradlin CT, Wilson MG, Wiltse CG. Baseline comparability in clinical trials: Prevention of “poststudy anxiety.” Drug Information Journal. 1990;24(3):541-548. doi:10.1177/009286159002400312
12.
Altman DG. Randomisation. BMJ. 1991;302(6791):1481-1482. doi:10.1136/bmj.302.6791.1481
13.
Pocock SJ . Clinical trials: A practical approach. John wiley & sons ltd, 1983.
14.
Treasure T, MacRae KD. Minimisation: The platinum standard for trials? BMJ. 1998;317(7155):362-363. doi:10.1136/bmj.317.7155.362
15.
Kahan BC, Rehal S, Cro S. Risk of selection bias in randomised trials. Trials. 2015;16(1). doi:10.1186/s13063-015-0920-x
16.
Clark L, Burke L, Margaret Carr R, Coleman E, Roberts G, Torgerson DJ. A review found small variable blocking schemes may not protect against selection bias in randomized controlled trials. Journal of Clinical Epidemiology. 2022;141:90-98. doi:10.1016/j.jclinepi.2021.09.009
17.
Schulz KF. Assessing the quality of randomization from reports of controlled trials published in obstetrics and gynecology journals. JAMA: The Journal of the American Medical Association. 1994;272(2):125. doi:10.1001/jama.1994.03520020051014
18.
Altman DG, Doré CJ. Randomisation and baseline comparisons in clinical trials. The Lancet. 1990;335(8682):149-153. doi:10.1016/0140-6736(90)90014-v
19.
Adetugbo K, Williams H. How well are randomized controlled trials reported in the dermatology literature? Archives of Dermatology. 2000;136(3). doi:10.1001/archderm.136.3.381
20.
Ciolino JD, Palac HL, Yang A, Vaca M, Belli HM. Ideal vs. Real: A systematic review on handling covariates in randomized controlled trials. BMC Medical Research Methodology. 2019;19(1). doi:10.1186/s12874-019-0787-8

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