17a. Sequence Generation

What to write

Who generated the random allocation sequence and the method used

Examples

“Randomization was done using computer-generated random numbers (Stat Trek software) by trained staff at the Soltan Mirahmad Clinic (Kashan, Iran).”1

“The randomization was conducted by two independent researchers who were not involved in the study using a computer random sequence generator.”2

Explanation

Randomisation eliminates selection bias at trial entry and is the crucial component of high quality randomised trials (box 5).3 Successful randomisation hinges on two steps: generation of an unpredictable allocation sequence and concealment of this sequence from the investigators enrolling participants (item 18).4,5

Treatment allocation in trials

The method used to assign interventions to trial participants is a crucial aspect of clinical trial design. Random assignment is the preferred method; it has been successfully used regularly in trials for more than 75 years.6 Randomisation has three major advantages.7 Firstly, when properly implemented, it eliminates selection bias, balancing both known and unknown prognostic factors, in the assignment of treatments. Without randomisation, treatment comparisons may be prejudiced, whether consciously or not, by selection of participants of a particular kind to receive a particular treatment. Secondly, random assignment permits the use of probability theory to express the likelihood that any difference in outcome between intervention groups reflects mere chance.8 Thirdly, random allocation, in some situations, facilitates blinding the identity of treatments to the investigators, participants, and evaluators, possibly by use of a placebo, which reduces bias after assignment of treatments.9 Of these three advantages, reducing selection bias at trial entry is usually the most important.10

Successful randomisation in practice depends on two inter-related aspects: adequate generation of an unpredictable allocation sequence and concealment of that sequence until assignment occurs.4,5 A key issue is whether the sequence is known or predictable by the people involved in allocating participants to the comparison groups.11 The treatment allocation system should thus be set up so that the person enrolling participants does not know in advance which treatment the next person will get, a process termed allocation concealment.4,5 Proper allocation concealment shields knowledge of forthcoming assignments, whereas proper random sequences prevent correct anticipation of future assignments based on knowledge of past assignments.

Who generated the random allocation sequence is important mainly for two reasons. Firstly, someone, or some group, should take responsibility for this critical trial function. Secondly, providing information on the generator might help readers to evaluate whether anyone had access to the allocation sequence during implementation. Investigators should strive for complete separation, independence, between the trial staff involved with generation of the allocation sequence and those staff who implement assignments (see explanation for item 19).

Participants should be assigned to comparison groups in the trial on the basis of a chance (random) process characterised by unpredictability (box 5). Successful randomisation in practice depends on two inter-related aspects: adequate generation of an unpredictable allocation sequence and concealment of that sequence until assignment occurs. A key issue is whether the sequence is known or predictable by the people involved in allocating participants to the comparison groups. The treatment allocation system should thus be set up so that the person enrolling participants does not know in advance which treatment the next person will receive, a process termed allocation concealment (item 18). Proper allocation concealment shields knowledge of forthcoming assignments, whereas proper random sequences (item 17) prevent correct anticipation of future assignments based on knowledge of past assignments (box 5).

Authors should provide sufficient information such that the reader can assess the methods used to generate the random allocation sequence and the likelihood of bias in group assignment. Any software used for random sequence generation should also be reported. It is important that information on the process of randomisation is included in the body of the main article and not as a separate supplementary file, where it can be missed by the reader.

The term “random” has a precise technical meaning. With random allocation, each participant has a known probability of receiving each intervention before one is assigned, and the assigned intervention is determined by a chance process and cannot be predicted. However, “random” is sometimes used inappropriately in the literature to describe trials in which non-random, “deterministic” allocation methods were used, such as alternation, hospital numbers, or date of birth. When investigators use such non-random methods, they should describe them precisely and should not use the term “random” or any variation of it. Even the term “quasi-random” is unacceptable for describing such trials. Trials based on non-random methods generally yield biased results124,1318; bias presumably arises from the inability to adequately conceal these more predictable, non-random sequence generation systems.

Many methods of sequence generation are adequate. However, readers cannot judge adequacy from such terms as “random allocation,” “randomisation,” or “random” without further elaboration. Authors should specify the method of sequence generation, such as a random-number table or a computerised random number generator. The sequence may be generated by the process of minimisation, a non-random but generally acceptable method.

In some trials, participants are intentionally allocated in unequal numbers to each intervention: for example, to gain more experience with a new procedure or to limit costs of the trial. In such cases, authors should report the randomisation ratio (eg, 2:1, or two treatment participants per control participant; item 9).

In a representative sample of PubMed indexed trials in 2000, only 21% reported an adequate approach to random sequence generation19; this increased to 34% for a similar cohort of PubMed indexed trials in 2006.20 Two more recent studies showed further small increases to about 40%,21,22 but another reported a stubbornly similar level of 32%.18 When authors report an adequate approach to random sequence generation, in over 90% of cases they report using a random number generator on a computer or a random number table.2022

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.
Padoei F, Mamsharifi P, Hazegh P, et al. The therapeutic effect of n‐acetylcysteine as an add‐on to methadone maintenance therapy medication in outpatients with substance use disorders: A randomized, double‐blind, placebo‐controlled clinical trial. Brain and Behavior. 2022;13(1). doi:10.1002/brb3.2823
2.
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
3.
Altman DG. Randomisation. BMJ. 1991;302(6791):1481-1482. doi:10.1136/bmj.302.6791.1481
4.
Schulz KF. Empirical evidence of bias: Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. JAMA. 1995;273(5):408. doi:10.1001/jama.1995.03520290060030
5.
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
6.
BMJ. 1948;2(4582):769-782. doi:10.1136/bmj.2.4582.769
7.
SCHULZ KF. Randomized controlled trials. Clinical Obstetrics and Gynecology. 1998;41(2):245-256. doi:10.1097/00003081-199806000-00005
8.
Greenland S. Randomization, statistics, and causal inference. Epidemiology. 1990;1(6):421-429. doi:10.1097/00001648-199011000-00003
9.
Armitage P. The role of randomization in clinical trials. Statistics in Medicine. 1982;1(4):345-352. doi:10.1002/sim.4780010412
10.
Kleijnen j gøtzsche p kunz RA oxman a chalmers i . So what’s so special about randomisation? In: Manynard a chalmers i , eds. Non-random reflections on health services research. BMJ publishing group, 1997: 93-106.
11.
Chalmers L. Assembling comparison groups to assess the effects of health care. Journal of the Royal Society of Medicine. 1997;90(7):379-386. doi:10.1177/014107689709000706
12.
Savović J, Jones HE, Altman DG, et al. Influence of reported study design characteristics on intervention effect estimates from randomized, controlled trials. Annals of Internal Medicine. 2012;157(6):429-438. doi:10.7326/0003-4819-157-6-201209180-00537
13.
Moher D. CONSORT: An evolving tool to help improve the quality of reports of randomized controlled trials. JAMA. 1998;279(18):1489. doi:10.1001/jama.279.18.1489
14.
Kjaergard l, villumsen j, gluud c. Quality of randomised clinical trials affects estimates of intervention efficacy. The best evidence for health care: The role of the cochrane collaboration - abstracts of the 7th cochrane colloquium; 1999 5-9 oct; rome, italy 1999.
15.
Jüni P, Altman DG, Egger M. Assessing the quality of randomised controlled trials. Systematic Reviews in Health Care. Published online January 2001:87-108. doi:10.1002/9780470693926.ch5
16.
Thompson M, Van den Bruel A, Verbakel J, et al. Systematic review and validation of prediction rules for identifying children with serious infections in emergency departments and urgent-access primary care. Health Technology Assessment. 2012;16(15). doi:10.3310/hta16150
17.
Savović J, Turner RM, Mawdsley D, et al. Association between risk-of-bias assessments and results of randomized trials in cochrane reviews: The ROBES meta-epidemiologic study. American Journal of Epidemiology. 2017;187(5):1113-1122. doi:10.1093/aje/kwx344
18.
Saltaji H, Armijo-Olivo S, Cummings GG, Amin M, Costa BR da, Flores-Mir C. Impact of selection bias on treatment effect size estimates in randomized trials of oral health interventions: A meta-epidemiological study. Journal of Dental Research. 2017;97(1):5-13. doi:10.1177/0022034517725049
19.
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
20.
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
21.
Armijo-Olivo S, Saltaji H, Costa BR da, Fuentes J, Ha C, Cummings GG. What is the influence of randomisation sequence generation and allocation concealment on treatment effects of physical therapy trials? A meta-epidemiological study. BMJ Open. 2015;5(9):e008562. doi:10.1136/bmjopen-2015-008562
22.
Barcot O, Boric M, Poklepovic Pericic T, et al. Risk of bias judgments for random sequence generation in cochrane systematic reviews were frequently not in line with cochrane handbook. BMC Medical Research Methodology. 2019;19(1). doi:10.1186/s12874-019-0804-y

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