5b. Conflicts of interest

What to write

Financial and other conflicts of interest of the manuscript authors

Example

“SYR reports grants from Amgen, Astellas, Daiichi Sankyo, Eisai, Merck, Roche, Zymeworks, Indivumed, MSD, Ono/Bristol Myers Squibb, AstraZeneca, BI, Taiho, Lilly, SN Bioscience. SRF has received honoraria as an invited speaker for Lilly, Eisai, Daiichi Sankyo, MSD, and Ono/Bristol Myers Squibb; has participated on advisory boards for Amgen and Indivumed; and has served as an advisor for Astellas, Daiichi Sankyo, Eisai, LG Biochem, Merck Sharpe Dohme, Ono/Bristol Myers Squibb, and AstraZeneca. D-YO reports grants from AstraZeneca, Novartis, Array, Eli Lilly, Servier, BeiGene, Merck Sharpe Dohme, and Handok; and has participated on a data safety monitoring board or advisory board for AstraZeneca, Novartis, Genentech/Roche, Merck Serono, Bayer, Taiho, ASLAN, Halozyme, Zymeworks, Bristol Myers Squibb/Celgene, BeiGene, Basilea, Turning Point, Yuhan, Arcus Biosciences, IQVIA, and Merck Sharpe Dohme. M-HR reports research grants from AstraZeneca; consulting fees from Bristol Myers Squibb, Ono, Lilly, Merck Sharpe Dohme, Novartis, Daiichi Sankyo, AstraZeneca, Sanofi, and Astellas; and has received honoraria for lectures, presentations, speakers bureaus, or educational events from Bristol Myers Squibb, Ono, Lilly, Merck Sharpe Dohme, Novartis, Daiichi Sankyo, AstraZeneca, Sanofi, and Astellas . . .

“LY, SB and PB report full-time employment by Merck Sharp and Dohme, a subsidiary of Merck (Rahway, NJ, USA), and stock ownership in Merck. LSW reports consulting fees from Amgen; and has received honoraria for lectures, presentations, speakers bureaus, or educational events from Novartis, Bristol Myers Squibb, Merck Sharpe Dohme, Roche, and Amgen.

“PY, YB, JLee, MGF, JLi, MAL, TC, SQ, SL, and HP declare no competing interests.”1

Explanation

Disclosure of authors’ conflicts of interest provides important context for readers of a trial report when ascertaining the overall methodological rigor of a trial (eg, relevance of the type of comparator intervention and eligibility criteria for patients) and risk of bias (eg, selective reporting of favourable results). Conflicts of interest of all trial manuscript authors should be reported, along with any procedures to reduce the risk of conflicts of interest influencing the trial’s design, conduct, analysis, or reporting.

Conflicts of interest can be defined as “a set of circumstances that creates a risk that professional judgement or actions regarding a primary interest will be unduly influenced by a secondary interest.”2 In the context of authors of a trial report, conflicts of interest imply a risk that investigators’ personal interests and allegiances, or ties with companies or organisations, have undue influence on the design, conduct, analysis, or reporting of a trial. The concept implies a risk of influence and is not indicative of actual wrongdoing.

Conflicts of interest are most often associated with the drug and device industries. Types of financial ties include salary support or grants; ownership of stock or options; honorariums (eg, for advice, authorship, or public speaking); paid consultancy or service on advisory boards and medical education companies; and receipt of patents or patents pending. An analysis of 200 trials from 2015 reported that 57% of trials had at least one author declaring financial conflicts of interests.3

Conflicts of interest may also exist with support from or affiliation with government agencies, charities, and professional and civic organisations. Non-financial conflicts of interest include academic commitments; personal or professional relationships; and political, religious, or other affiliations with special interests or advocacy positions. An analysis of 200 trials found that 4% of trials had at least one author declaring non-financial conflicts of interest.3 There is ongoing discussion on the association between a problematic non-financial conflict of interest and a reasonable point of view.4

A cross sectional study of 190 randomised trials, published in core clinical journals, found that trials with authors’ conflicts of interest had more positive results than trials without. The presence of a financial tie was associated with a positive study outcome (odds ratio 3.23; 95% CI 1.7 to 6.1). This association was also present after adjustment for the study funding source (odds ratio 3.57; 95% CI 1.7 to 7.7).5

Although financial conflicts of interest are often declared in trials,3 the declarations are generally imprecise, and undisclosed conflicts are common.6 A systematic review of studies comparing financial conflicts of interest declared in medical publications or guidelines (not only randomised trials) with declarations in payment databases (eg, the Open Payments Database) found that the median percentage of authors with “non-concordant” disclosures was 81%.6 A study including only randomised trials found that 35 (30%) of 115 authors from non-industry funded trials had undisclosed conflicts of interest whereas that was the case for 102 (50%) of 203 authors from industry funded trials.7 For financial conflicts that cannot be tracked to public databases and for non-financial conflicts, the rate of non-disclosure is unknown but it is likely to be even higher.

Training

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References

1.
Rha SY, Oh DY, Yañez P, et al. Pembrolizumab plus chemotherapy versus placebo plus chemotherapy for HER2-negative advanced gastric cancer (KEYNOTE-859): A multicentre, randomised, double-blind, phase 3 trial. The Lancet Oncology. 2023;24(11):1181-1195. doi:10.1016/s1470-2045(23)00515-6
2.
Institute of medicine . Conflict of interest in medical research, education, and practice. National academies press, 2009.
3.
Hakoum MB, Jouni N, Abou-Jaoude EA, et al. Authors of clinical trials reported individual and financial conflicts of interest more frequently than institutional and nonfinancial ones: A methodological survey. Journal of Clinical Epidemiology. 2017;87:78-86. doi:10.1016/j.jclinepi.2017.04.002
4.
Lenzer J. When is a point of view a conflict of interest? BMJ. Published online November 2016:i6194. doi:10.1136/bmj.i6194
5.
Ahn R, Woodbridge A, Abraham A, et al. Financial ties of principal investigators and randomized controlled trial outcomes: Cross sectional study. BMJ. Published online January 2017:i6770. doi:10.1136/bmj.i6770
6.
El-Rayess H, Khamis AM, Haddad S, et al. Assessing concordance of financial conflicts of interest disclosures with payments’ databases: A systematic survey of the health literature. Journal of Clinical Epidemiology. 2020;127:19-28. doi:10.1016/j.jclinepi.2020.06.040
7.
Rasmussen K, Schroll J, Gøtzsche PC, Lundh A. Under-reporting of conflicts of interest among trialists: A cross-sectional study. Journal of the Royal Society of Medicine. 2014;108(3):101-107. doi:10.1177/0141076814557878

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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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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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