6. Eligibility criteria

What to write

Eligibility criteria.

Explanation

Since a diagnostic accuracy study describes the behaviour of a test under particular circumstances, a report of the study must include a complete description of the criteria that were used to identify eligible participants. Eligibility criteria are usually related to the nature and stage of the target condition and the intended future use of the index test; they often include the signs, symptoms or previous test results that generate the suspicion about the target condition. Additional criteria can be used to exclude participants for reasons of safety, feasibility and ethical arguments.

Excluding patients with a specific condition or receiving a specific treatment known to adversely affect the way the test works can lead to inflated diagnostic accuracy estimates.1 An example is the exclusion of patients using β blockers in studies evaluating the diagnostic accuracy of exercise ECG.

Some studies have one set of eligibility criteria for all study participants; these are sometimes referred to as single-gate or cohort studies. Other studies have one set of eligibility criteria for participants with the target condition, and (an)other set(s) of eligibility criteria for those without the target condition; these are called multiple-gate or case–control studies.2

In the first example, the eligibility criteria list presenting signs and symptoms, an age limit and exclusion based on specific conditions and treatments. Since the same set of eligibility criteria applies to all study participants, this is an example of a single-gate study.

In the second example, the authors used different eligibility criteria for participants with and without the target condition: one group consisted of patients with a confirmed diagnosis of Clostridium difficile, and one group consisted of healthy controls. This is an example of a multiple-gate study. Extreme contrasts between severe cases and healthy controls can lead to inflated estimates of accuracy.34

Examples

‘Patients eligible for inclusion were consecutive adults (≥18 years) with suspected pulmonary embolism, based on the presence of at least one of the following symptoms: unexplained (sudden) dyspnoea, deterioration of existing dyspnoea, pain on inspiration, or unexplained cough. We excluded patients if they received anticoagulant treatment (vitamin K antagonists or heparin) at presentation, they were pregnant, follow-up was not possible, or they were unwilling or unable to provide written informed consent’.5

‘Eligible cases had symptoms of diarrhoea and both a positive result for toxin by enzyme immunoassay and a toxigenic C difficile strain detected by culture (in a sample taken less than seven days before the detection round). We defined diarrhoea as three or more loose or watery stool passages a day. We excluded children and adults on intensive care units or haematology wards. Patients with a first relapse after completing treatment for a previous C difficile infection were eligible but not those with subsequent relapses. […] For each case we approached nine control patients. These patients were on the same ward as and in close proximity to the index patient. Control patients did not have diarrhoea, or had diarrhoea but a negative result for C difficile toxin by enzyme immunoassay and culture (in a sample taken less than seven days previously)’.6

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.
Philbrick JT, Horwitz RI, Feinstein AR. Methodologic problems of exercise testing for coronary artery disease: Groups, analysis and bias. The American Journal of Cardiology. 1980;46(5):807-812. doi:10.1016/0002-9149(80)90432-4
2.
Rutjes AW, Reitsma JB, Vandenbroucke JP, Glas AS, Bossuyt PM. Case–control and two-gate designs in diagnostic accuracy studies. Clinical Chemistry. 2005;51(8):1335-1341. doi:10.1373/clinchem.2005.048595
3.
Lijmer JG. Empirical evidence of design-related bias in studies of diagnostic tests. JAMA. 1999;282(11):1061. doi:10.1001/jama.282.11.1061
4.
Rutjes AWS, Reitsma JB, Di Nisio M, Smidt N, Rijn JC van, Bossuyt PMM. Evidence of bias and variation in diagnostic accuracy studies. Canadian Medical Association Journal. 2006;174(4):469-476. doi:10.1503/cmaj.050090
5.
Geersing G-J, Erkens PMG, Lucassen WAM, et al. Safe exclusion of pulmonary embolism using the wells rule and qualitative d-dimer testing in primary care: Prospective cohort study. BMJ. 2012;345(oct04 2):e6564-e6564. doi:10.1136/bmj.e6564
6.
Bomers MK, Agtmael MA van, Luik H, Veen MC van, Vandenbroucke-Grauls CMJE, Smulders YM. Using a dog’s superior olfactory sensitivity to identify clostridium difficile in stools and patients: Proof of principle study. BMJ. 2012;345(dec13 8):e7396-e7396. doi:10.1136/bmj.e7396

Citation

For attribution, please cite this work as:
Bossuyt PM, Reitsma JB, Bruns DE, et al. The STARD reporting guideline for writing diagnostic accuracy study articles. The EQUATOR Network guideline dissemination platform. doi:10.1234/equator/1010101

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.

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 behavior. 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 subjects 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

Medical test

Any method for collecting additional information about the current or future health status of a patient.

Index test

The test under evaluation.

Target condition

The disease or condition that the index test is expected to detect.

Clinical reference standard

The best available method for establishing the presence or absence of the target condition; a gold standard would be an error-free reference standard.

Sensitivity

Proportion of those with the target condition who test positive with the index test.

Specificity

Proportion of those without the target condition who test negative with the index test.

Intended use of the test

Whether the index test is used for diagnosis, screening, staging, monitoring, surveillance, prediction, prognosis, or other reasons.

Role of the test

The position of the index test relative to other tests for the same condition (for example, triage, replacement, add-on, new test).

Indeterminate results

Results that are neither positive or negative.