25. Baseline Data

What to write

A table showing baseline demographic and clinical characteristics for each group

Example

See Table 1.1

Table 1: Example of good reporting: Baseline demographic and clinical characteristics between study groups. Data are number (%) of participants unless stated otherwise. DMARD=disease-modifying drugs; ESR=erythrocyte sedimentation rate; IQR=interquartile range; MHQ=Michigan Hand Outcome Questionnaire; PCS=physical component score; MCS=mental component score; SD=standard deviation. Adapted from Lamb et al.1
Characteristic Usual care (n=242) Exercise (n=246)
Mean (SD) age (years) 63.5 (11) 61.3 (12)
Female sex 186 (76) 188 (76)
Ethnic origin
 White 235 (98) 238 (97)
 Indian 2 (1) 3 (1)
 Pakistani 1 (<1) —
 Mixed 1 (<1) 3 (1)
 Other 1 (<1) 2 (1)
Dominant in right hand 215 (90) 226 (92)
Median (IQR) No of years since rheumatoid arthritis diagnosis, estimated by participant 10 (4-22) 10 (4-21)
Median (IQR) baseline ESR 16 (8-28) 15 (7-28)
Median (IQR) baseline CRP 6 (3-12) 5 (3-12)
Drug treatment
 Biological DMARD 52 (22) 51 (21)
 Combination non-biological DMARD 53 (22) 72 (29)
 Single non-biological DMARD 118 (49) 103 (42)
 Other drugs 19 (8) 19 (8)
Mean (SD) MHQ
 Overall hand function (both) 52.1 (16.4) 52.1 (15.2)
 Activities of daily living (both) 54.1 (25.0) 54.5 (24.5)
 Work 48.4 (22.0) 48.2 (22.0)
 Pain 51.4 (19.9) 51.9 (21.9)
 Aesthetics (both) 58.6 (22.1) 56.9 (22.0)
 Satisfaction (both) 43.5 (22.3) 43.9 (19.7)
 Overall score 50.9 (16.9) 50.6 (16.4)
Mean (SD) SF-12 score
 Aggregate physical scale (PCS) 34.5 (9.5) 33.8 (9.8)
 Aggregate mental scale (MCS) 48.9 (11.0) 48.1 (10.7)

Explanation

Although the eligibility criteria (item 12a) indicate who was eligible for the trial, it is also important to know the characteristics of the participants who were actually included. This information allows readers, especially clinicians, to judge how relevant the results of a trial might be to an individual patient. Participant baseline demographics may include characteristics such as age, sex and/or gender,2 place of residence, race and/or ethnicity, culture and/or religion, language, occupation, education, or socioeconomic status. Baseline clinical characteristics include those which are identical, or closely related, to the trial outcomes.3

Randomised trials aim to compare groups of participants that differ only with respect to the intervention (treatment). Although proper random assignment prevents selection bias, it does not guarantee similarity of the groups at baseline. Any differences in baseline characteristics are, however, the result of chance rather than bias.4 Important demographic and clinical characteristics should be presented so that readers can assess how similar the groups were at baseline. Baseline data are especially valuable for outcomes that can also be measured at the start of the trial (eg, blood pressure).

Baseline information is most efficiently presented in a table. For continuous variables, such as weight or blood pressure, the variability of the data should be reported, along with average values. Continuous variables can be summarised for each group by the mean and standard deviation. When continuous data have an asymmetrical distribution, a preferable approach may be to quote the median and percentile values (eg, the 25th and 75th percentiles).5 Standard errors and CIs are not appropriate for describing variability—they are inferential rather than descriptive statistics. Variables with a small number of ordered categories (such as stages of disease I to IV) should not be treated as continuous variables; instead, numbers and proportions should be reported for each category.5,6

Significance testing of baseline differences is not recommended and should not be reported.4,7,8 Such significance tests assess the probability that observed baseline differences could have occurred by chance; however, providing the randomisation has not been subverted or comprised, any differences are caused by chance. Unfortunately, such significance tests are still relatively common.9–11 Such hypothesis testing is superfluous and can mislead investigators and their readers.12 Rather, comparisons at baseline should be based on consideration of the prognostic strength of the variables measured and the magnitude of any chance imbalances that have occurred.12

Training

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References

1.
Lamb SE, Williamson EM, Heine PJ, et al. Exercises to improve function of the rheumatoid hand (SARAH): A randomised controlled trial. The Lancet. 2015;385(9966):421-429. doi:10.1016/s0140-6736(14)60998-3
2.
Heidari S, Babor TF, De Castro P, Tort S, Curno M. Sex and gender equity in research: Rationale for the SAGER guidelines and recommended use. Research Integrity and Peer Review. 2016;1(1). doi:10.1186/s41073-016-0007-6
3.
Welch VA, Norheim OF, Jull J, Cookson R, Sommerfelt H, Tugwell P. CONSORT-equity 2017 extension and elaboration for better reporting of health equity in randomised trials. BMJ. Published online November 2017:j5085. doi:10.1136/bmj.j5085
4.
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
5.
Altman DG gore SM gardner MJ pocock SJ . Statistical guidelines for contributors to medical journals. In: Altman DG machin d bryant TN gardner MJ , eds. Statistics with confidence: Confidence intervals and statistical guidelines. 2nd ed. BMJ books, 2000: 171-90.
6.
Lang TA, Secic M. How to report statistics in medicine: Annotated guidelines for authors. The Nurse Practitioner. 1997;22(5):198. doi:10.1097/00006205-199705000-00022
7.
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
8.
Senn SJ. Base logic: Tests of baseline balance in randomized clinical trials. Clinical Research and Regulatory Affairs. 1995;12(3):171-182. doi:10.3109/10601339509019426
9.
Austin PC, Manca A, Zwarenstein M, Juurlink DN, Stanbrook MB. A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: A review of trials published in leading medical journals. Journal of Clinical Epidemiology. 2010;63(2):142-153. doi:10.1016/j.jclinepi.2009.06.002
10.
Pocock SJ, Assmann SE, Enos LE, Kasten LE. Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: Current practiceand problems. Statistics in Medicine. 2002;21(19):2917-2930. doi:10.1002/sim.1296
11.
Boer MR de, Waterlander WE, Kuijper LD, Steenhuis IH, Twisk JW. Testing for baseline differences in randomized controlled trials: An unhealthy research behavior that is hard to eradicate. International Journal of Behavioral Nutrition and Physical Activity. 2015;12(1). doi:10.1186/s12966-015-0162-z
12.
Altman DG. Comparability of randomised groups. The Statistician. 1985;34(1):125. doi:10.2307/2987510

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

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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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