16b. Interim analyses and stopping criteria

What to write

Explanation of any interim analyses and stopping guidelines

Examples

“Interim analyses of effectiveness and safety endpoints were performed on behalf of the data monitoring committee on an approximately annual basis during the period of recruitment. These analyses were done with the use of the Haybittle–Peto principle and hence no adjustment was made in the final p values to determine significance.”1

“One interim analysis of the primary endpoint and safety data was planned for when approximately 50% of the participants had completed D28 [day 28]. Statistical significance and futility boundaries were estimated for the interim and final analysis based on 50,000 simulations from the PASS® software (NCSS, Kaysville, Utah) by simulating a group sequential test for two means assuming normality testing. At the interim analysis, the two-sided significance boundary for clinical efficacy was 0.00312 and for futility of detecting μAUT00063 > μplacebo, the one-sided O’Brien-Fleming boundary was 0.39,141. Hence, at the final analysis, the two-sided significance boundary for clinical efficacy would be 0.04761. The Independent Data Monitoring Committee (IDMC) was advised to consider making recommendations for early termination only where there was a clear demonstration of futility.”2

“Three planned analyses (two interim analyses and one final analysis) were performed when the observed number of events were 25, 47, and 84, respectively. Data were released by DSMC [data and safety monitoring committee] after final analysis. Efficacy stopping boundaries were based on the O’Brien-Fleming spending function. Futility boundaries were based on testing the alternative hypothesis at the 0.039 level.”3

“Two interim analyses to be performed using the Haybittle-Peto approach were scheduled, after enrolment of 1000 and 2000 patients, respectively. The significance level associated with both interim analyses was 0.001 and the significance level associated with the final analysis was 0.049. With this method, the overall risk of type 1 error was 5%.”4

Explanation

Numerous randomised trials enrol participants over extended periods of time. If an intervention demonstrates exceptional efficacy, the study might require early termination on ethical grounds. To mitigate this concern, assessing results as data accumulates is advisable, ideally through an independent data monitoring committee (DMC), sometimes referred to as a data and safety monitoring board (DSMB).5 However, conducting multiple statistical evaluations on accruing data without proper adjustment may result in misleading conclusions. For instance, examining data from a trial at five interim analyses using a P value of 0.05 would elevate the overall false-positive rate closer to 19% rather than the expected 5%. Further to stopping early for efficacy, interim analyses can be used to evaluate (1) futility, to assess whether a trial is likely to meet its objectives; or (2) safety, to assess whether there is evidence for increased risk of harms (in the intervention group relative to the comparator group).5 Interim analyses can also be used to reassess the sample size, using updated information from interim trial data (eg, through an internal pilot), to ensure adequate power of the trial.

Various group sequential statistical approaches exist to adjust for multiple looks (ie, analyses) at the data, and these should be predetermined in the trial protocol (see item 27a of the SPIRIT 2025 statement6). Using these methods, data are compared at each interim analysis, where a P value below the specified critical value by the chosen group sequential method signifies statistical significance. Some researchers view group sequential methods as a tool for decision making, while others regard them as a definitive stopping point, intending to halt the trial if the observed P value falls below the critical threshold.

Authors should disclose whether they or the DMC/DSMB performed multiple looks at the data (interim analyses). If such multiple looks occurred, it is important to specify the frequency; the triggers prompting them; the statistical methods applied (including any formal stopping rules); and whether these procedures were planned and documented in the trial protocol before the trial commenced, before the DMC examined any interim data, or at a later stage. Authors should also report the time point at which any interim analyses where conducted (and by whom); and state who decided to continue, stop or modify the trial, and whether they were blinded to the treatment allocation. Unfortunately, the reporting of interim analyses and stopping rules is frequently inadequate in published trial reports,7 even in cases where trials indeed halted earlier than originally planned.

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References

1.
Horne AW, Tong S, Moakes CA, et al. Combination of gefitinib and methotrexate to treat tubal ectopic pregnancy (GEM3): A multicentre, randomised, double-blind, placebo-controlled trial. The Lancet. 2023;401(10377):655-663. doi:10.1016/s0140-6736(22)02478-3
2.
Hall DA, Ray J, Watson J, et al. A balanced randomised placebo controlled blinded phase IIa multi-centre study to investigate the efficacy and safety of AUT00063 versus placebo in subjective tinnitus: The QUIET-1 trial. Hearing Research. 2019;377:153-166. doi:10.1016/j.heares.2019.03.018
3.
Hogan LE, Brown PA, Ji L, et al. Children’s oncology group AALL1331: Phase III trial of blinatumomab in children, adolescents, and young adults with low-risk b-cell ALL in first relapse. Journal of Clinical Oncology. 2023;41(25):4118-4129. doi:10.1200/jco.22.02200
4.
Reignier J, Plantefeve G, Mira JP, et al. Low versus standard calorie and protein feeding in ventilated adults with shock: A randomised, controlled, multicentre, open-label, parallel-group trial (NUTRIREA-3). The Lancet Respiratory Medicine. 2023;11(7):602-612. doi:10.1016/s2213-2600(23)00092-9
5.
Ciolino JD, Kaizer AM, Bonner LB. Guidance on interim analysis methods in clinical trials. Journal of Clinical and Translational Science. 2023;7(1). doi:10.1017/cts.2023.552
6.
Chan AW, Boutron I, Hopewell S, et al. SPIRIT 2025 statement: Updated guideline for protocols of randomised trials. BMJ. 2025;389:e081477. doi:10.1136/bmj-2024-081477
7.
Stegert M, Kasenda B, Elm E von, et al. An analysis of protocols and publications suggested that most discontinuations of clinical trials were not based on preplanned interim analyses or stopping rules. Journal of Clinical Epidemiology. 2016;69:152-160. doi:10.1016/j.jclinepi.2015.05.023

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.

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Maximum impact

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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).

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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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