Title or abstract

Item 1. Identification as a study of diagnostic accuracy using at least one measure of accuracy (such as sensitivity, specificity, predictive values or AUC)

Example. ‘Main outcome measures: Sensitivity and specificity of CT colonography in detecting individuals with advanced neoplasia (i.e., advanced adenoma or colorectal cancer) 6 mm or larger’.12

Explanation. When searching for relevant biomedical studies on a certain topic, electronic databases such as MEDLINE and Embase are indispensable. To facilitate retrieval of their article, authors can explicitly identify it as a report of a diagnostic accuracy study. This can be performed by using terms in the title and/or abstract that refer to measures of diagnostic accuracy, such as ‘sensitivity’, ‘specificity’, ‘positive predictive value’, ‘negative predictive value’, ‘area under the ROC curve (AUC)’ or ‘likelihood ratio’.

In 1991, MEDLINE introduced a specific keyword (MeSH heading) for indexing diagnostic studies: ‘Sensitivity and Specificity.’ Unfortunately, the sensitivity of using this particular MeSH heading to identify diagnostic accuracy studies can be as low as 51%.13 As of May 2015, Embase's thesaurus (Emtree) has 38 check tags for study types; ‘diagnostic test accuracy study’ is one of them, but was only introduced in 2011.

In the example, the authors mentioned the terms ‘sensitivity’ and ‘specificity’ in the abstract. The article will now be retrieved when using one of these terms in a search strategy, and will be easily identifiable as one describing a diagnostic accuracy study.

Abstract

Item 2. Structured summary of study design, methods, results and conclusions (for specific guidance, see STARD for Abstracts)

Example. See STARD for Abstracts (manuscript in preparation; checklist will be available at http://www.equator-network.org/reporting-guidelines/stard/).

Explanation. Readers use abstracts to decide whether they should retrieve the full study report and invest time in reading it. In cases where access to the full study report cannot be obtained or where time is limited, it is conceivable that clinical decisions are based on the information provided in abstracts only.

In two recent literature surveys, abstracts of diagnostic accuracy studies published in high-impact journals or presented at an international scientific conference were found insufficiently informative, because key information about the research question, study methods, study results and the implications of findings were frequently missing.14 ,15

Informative abstracts help readers to quickly appraise critical elements of study validity (risk of bias) and applicability of study findings to their clinical setting (generalisability). Structured abstracts, with separate headings for objectives, methods, results and interpretation, allow readers to find essential information more easily.16

Building on STARD 2015, the newly developed STARD for Abstracts provides a list of essential items that should be included in journal and conference abstracts of diagnostic accuracy studies (list finalised; manuscript under development).

Introduction

Item 3. Scientific and clinical background, including the intended use and clinical role of the index test

Example. ‘The need for improved efficiency in the use of emergency department radiography has long been documented. This need for selectivity has been identified clearly for patients with acute ankle injury, who generally are all referred for radiography, despite a yield for fracture of less than 15%. The referral patterns and yield of radiography for patients with knee injuries have been less well described but may be more inefficient than for patients with ankle injuries. […] The sheer volume of low-cost tests such as plain radiography may contribute as much to rising health care costs as do high-technology, low-volume procedures. […] If validated in subsequent studies, a decision rule for knee-injury patients could lead to a large reduction in the use of knee radiography and significant health care savings without compromising patient care’.17

Explanation. In the introduction of scientific study reports, authors should describe the rationale for their study. In doing so, they can refer to previous work on the topic, remaining uncertainty and the clinical implications of this knowledge gap. To help readers in evaluating the implications of the study, authors can clarify the intended use and the clinical role of the test under evaluation, which is referred to as the index test.

The intended use of a test can be diagnosis, screening, staging, monitoring, surveillance, prognosis, treatment selection or other purposes.18 The clinical role of the test under evaluation refers to its anticipated position relative to other tests in the clinical pathway.19 A triage test, for example, will be used before an existing test because it is less costly or burdensome, but often less accurate as well. An add-on test will be used after existing tests, to improve the accuracy of the total test strategy by identifying false positives or false negatives of the initial test. In other cases, a new test may be used to replace an existing test.

Defining the intended use and clinical role of the test will guide the design of the study and the targeted level of sensitivity and specificity; from these definitions follow the eligibility criteria, how and where to identify eligible participants, how to perform tests and how to interpret test results.19

Specifying the clinical role is helpful in assessing the relative importance of potential errors (false positives and false negatives) made by the index test. A triage test to rule out disease, for example, will need very high sensitivity, whereas the one that mainly aims to rule in disease will need very high specificity.

In the example, the intended use is diagnosis of knee fractures in patients with acute knee injuries, and the potential clinical role is triage test; radiography, the existing test, would only be performed in those with a positive outcome of the newly developed decision rule. The authors outline the current scientific and clinical background of the health problem studied, and their reason for aiming to develop a triage test: this would reduce the number of radiographs and, consequently, healthcare costs.

Item 4. Study objectives and hypotheses

Example (1). ‘The objective of this study was to evaluate the sensitivity and specificity of 3 different diagnostic strategies: a single rapid antigen test, a rapid antigen test with a follow-up rapid antigen test if negative (rapid-rapid diagnostic strategy), and a rapid antigen test with follow-up culture if negative (rapid-culture)—the AAP diagnostic strategy—all compared with a 2-plate culture gold standard. In addition, […] we also compared the ability of these strategies to achieve an absolute diagnostic test sensitivity of >95%’.20

Example (2). ‘Our 2 main hypotheses were that rapid antigen detection tests performed in physician office laboratories are more sensitive than blood agar plate cultures performed and interpreted in physician office laboratories, when each test is compared with a simultaneous blood agar plate culture processed and interpreted in a hospital laboratory, and rapid antigen detection test sensitivity is subject to spectrum bias’.21

Explanation. Clinical studies may have a general aim (a long-term goal, such as ‘to improve the staging of oesophageal cancer’), specific objectives (well-defined goals for this particular study) and testable hypotheses (statements than can be falsified by the study results).

In diagnostic accuracy studies, statistical hypotheses are typically defined in terms of acceptability criteria for single tests (minimum levels of sensitivity, specificity or other measures). In those cases, hypotheses generally include a quantitative expression of the expected value of the diagnostic parameter. In other cases, statistical hypotheses are defined in terms of equality or non-inferiority in accuracy when comparing two or more index tests.

A priori specification of the study hypotheses limits the chances of post hoc data-dredging with spurious findings, premature conclusions about the performance of tests or subjective judgement about the accuracy of the test. Objectives and hypotheses also guide sample size calculations. An evaluation of 126 reports of diagnostic test accuracy studies published in high-impact journals in 2010 revealed that 88% did not state a clear hypothesis.22

In the first example, the authors' objective was to evaluate the accuracy of three diagnostic strategies; their specific hypothesis was that the sensitivity of any of these would exceed the prespecified value of 95%. In the second example, the authors explicitly describe the hypotheses they want to explore in their study. The first hypothesis is about the comparative sensitivity of two index tests (rapid antigen detection test vs culture performed in physician office laboratories); the second is about variability of rapid test performance according to patient characteristics (spectrum bias).

Methods

Item 5. Whether data collection was planned before the index test and reference standard were performed (prospective study) or after (retrospective study)

Example. ‘We reviewed our database of patients who underwent needle localization and surgical excision with digital breast tomosynthesis guidance from April 2011 through January 2013. […] The patients’ medical records and images of the 36 identified lesions were then reviewed retrospectively by an author with more than 5 years of breast imaging experience after a breast imaging fellowship’.23

Explanation. There is great variability in the way the terms ‘prospective’ and ‘retrospective’ are defined and used in the literature. We believe it is therefore necessary to describe clearly whether data collection was planned before the index test and reference standard were performed, or afterwards. If authors define the study question before index test and reference standards are performed, they can take appropriate actions for optimising procedures according to the study protocol and for dedicated data collection.24

Sometimes, the idea for a study originates when patients have already undergone the index test and the reference standard. If so, data collection relies on reviewing patient charts or extracting data from registries. Though such retrospective studies can sometimes reflect routine clinical practice better than prospective studies, they may fail to identify all eligible patients, and often result in data of lower quality, with more missing data points.24 A reason for this could be, for example, that in daily clinical practice, not all patients undergoing the index test may proceed to have the reference standard.

In the example, the data were clearly collected retrospectively: participants were identified through database screening, clinical data were abstracted from patients' medical records, though images were reinterpreted.

Item 6. Eligibility criteria

Example (1). ‘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’.25

Example (2). ‘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)’.26

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.27 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.28

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.6 ,29

Item 7. On what basis potentially eligible participants were identified (such as symptoms, results from previous tests, inclusion in registry)

Example. ‘We reviewed our database of patients who underwent needle localization and surgical excision with digital breast tomosynthesis guidance from April 2011 through January 2013’.23

Explanation. The eligibility criteria specify who can participate in the study, but they do not describe how the study authors identified eligible participants. This can be performed in various ways.30 A general practitioner may evaluate every patient for eligibility that he sees during office hours. Researchers can go through registries in an emergency department, to identify potentially eligible patients. In other studies, patients are only identified after having been subjected to the index test. Still other studies start with patients in whom the reference standard was performed. Many retrospective studies include participants based on searching hospital databases for patients that underwent the index test and the reference standard.31

Differences in methods for identifying eligible patients can affect the spectrum and prevalence of the target condition in the study group, as well as the range and relative frequency of alternative conditions in patients without the target condition.32 These differences can influence the estimates of diagnostic accuracy.

In the example, participants were identified through searching a patient database and were included if they underwent the index test and the reference standard.

Item 8. Where and when potentially eligible participants were identified (setting, location and dates)

Example. ‘The study was conducted at the Emergency Department of a university-affiliated children's hospital between January 21, 1996, and April 30, 1996’.33

Explanation. The results of a diagnostic accuracy study reflect the performance of a test in a particular clinical context and setting. A medical test may perform differently in a primary, secondary or tertiary care setting, for example. Authors should therefore report the actual setting in which the study was performed, as well as the exact locations: names of the participating centres, city and country. The spectrum of the target condition as well as the range of other conditions that occur in patients suspected of the target condition can vary across settings, depending on which referral mechanisms are in play.34–36

Since test procedures, referral mechanisms and the prevalence and severity of diseases can evolve over time, authors should also report the start and end dates of participant recruitment.

This information is essential for readers who want to evaluate the generalisability of the study findings, and their applicability to specific questions, for those who would like to use the evidence generated by the study to make informed healthcare decisions.

In the example, study setting and study dates were clearly defined.

Item 9. Whether participants formed a consecutive, random or convenience series

Example. ‘All subjects were evaluated and screened for study eligibility by the first author (E.N.E.) prior to study entry. This was a convenience sample of children with pharyngitis; the subjects were enrolled when the first author was present in the emergency department’.37

Explanation. The included study participants may be either a consecutive series of all patients evaluated for eligibility at the study location and satisfying the inclusion criteria, or a subselection of these. A subselection can be purely random, produced by using a random numbers table, or less random, if patients are only enrolled on specific days or during specific office hours. In that case, included participants may not be considered a representative sample of the targeted population, and the generalisability of the study results may be jeopardised.2 ,29

In the example, the authors explicitly described a convenience series where participants were enrolled based on their accessibility to the clinical investigator.

Item 10a. Index test, in sufficient detail to allow replication

Item 10b. Reference standard, in sufficient detail to allow replication

Example. ‘An intravenous line was inserted in an antecubital vein and blood samples were collected into serum tubes before (baseline), immediately after, and 1.5 and 4.5 h after stress testing. Blood samples were put on ice, processed within 1 h of collection, and later stored at −80°C before analysis. The samples had been through 1 thaw–freeze cycle before cardiac troponin I (cTnI) analysis. We measured cTnI by a prototype hs assay (ARCHITECT STAT high-sensitivity troponin, Abbott Diagnostics) with the capture antibody detecting epitopes 24–40 and the detection antibody epitopes 41–49 of cTnI. The limit of detection (LoD) for the high sensitivity (hs) cTnI assay was recently reported by other groups to be 1.2 ng/L, the 99th percentile 16 ng/L, and the assay 10% coefficient of variation (CV) 3.0 ng/L. […] Samples with concentrations below the range of the assays were assigned values of 1.2 […] for cTnI. […]’.38

Explanation. Differences in the execution of the index test or reference standard are a potential source of variation in diagnostic accuracy.39 ,40 Authors should therefore describe the methods for executing the index test and reference standard, in sufficient detail to allow other researchers to replicate the study, and to allow readers to assess (1) the feasibility of using the index test in their own setting, (2) the adequacy of the reference standard and (3) the applicability of the results to their clinical question.

The description should cover key elements of the test protocol, including details of:

1. the preanalytical phase, for example, patient preparation such as fasting/feeding status prior to blood sampling, the handling of the sample prior to testing and its limitations (such as sample instability), or the anatomic site of measurement;

2. the analytical phase, including materials and instruments and analytical procedures;

3. the postanalytical phase, such as calculations of risk scores using analytical results and other variables.

Between-study variability in measures of test accuracy due to differences in test protocol has been documented for a number of tests, including the use of hyperventilation prior to exercise ECG and the use of tomography for exercise thallium scintigraphy.27 ,40

The number, training and expertise of the persons executing and reading the index test and the reference standard may also be critical. Many studies have shown between-reader variability, especially in the field of imaging.41 ,42 The quality of reading has also been shown to be affected in cytology and microbiology by professional background, expertise and prior training to improve interpretation and to reduce interobserver variation.43–45 Information about the amount of training of the persons in the study who read the index test can help readers to judge whether similar results are achievable in their own settings.

In some cases, a study depends on multiple reference standards. Patients with lesions on an imaging test under evaluation may, for example, undergo biopsy with a final diagnosis based on histology, whereas patients without lesions on the index test undergo clinical follow-up as reference standard. This could be a potential source of bias, so authors should specify which patient groups received which reference standard.2 ,3

More specific guidance for specialised fields of testing, or certain types of tests, will be developed in future STARD extensions. Whenever available, these extensions will be made available on the STARD pages at the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) website (http://www.equator-network.org/).

In the example, the authors described how blood samples were collected and processed in the laboratory. They also report analytical performance characteristics of the index test device, as obtained in previous studies.

Item 11. Rationale for choosing the reference standard (if alternatives exist)

Example. ‘The MINI [Mini International Neuropsychiatric Inventory] was developed as a short and efficient diagnostic interview to be used in both research and clinical settings (reference supporting this statement provided by the authors). It has good reliability and validity rates compared with other gold standard diagnostic interviews, such as the Structured Clinical Interview for DSM [Diagnostic and Statistical Manual of Mental Disorders] Disorders (SCID) and the Composite International Diagnostic Interview (references supporting this statement provided by the authors)’.46

Explanation. In diagnostic accuracy studies, the reference standard is used for establishing the presence or absence of the target condition in study participants. Several reference standards may be available to define the same target condition. In such cases, authors are invited to provide their rationale for selecting the specific reference standard from the available alternatives. This may depend on the intended use of the index test, the clinical relevance or practical and/or ethical reasons.

Alternative reference standards are not always in perfect agreement. Some reference standards are less accurate than others. In other cases, different reference standards reflect related but different manifestations or stages of the disease, as in confirmation by imaging (first reference standard) versus clinical events (second reference standard).

In the example, the authors selected the MINI, a structured diagnostic interview commonly used for psychiatric evaluations, as the reference standard for identifying depression and suicide risk in adults with epilepsy. As a rationale for their choice, they claimed that the MINI test was short to administer, efficient for clinical and research purposes, reliable and valid when compared with alternative diagnostic interviews.

Item 12a. Definition of and rationale for test positivity cut-offs or result categories of the index test, distinguishing prespecified from exploratory

Item 12b. Definition of and rationale for test positivity cut-offs or result categories of the reference standard, distinguishing prespecified from exploratory

Example. ‘We also compared the sensitivity of the risk-model at the specificity that would correspond to using a fixed FIT [fecal immunochemical test] positivity threshold of 50 ng/ml. We used a threshold of 50 ng/ml because this was the anticipated cut-off for the Dutch screening programme at the time of the study’.47

Explanation. Test results in their original form can be dichotomous (positive vs negative), have multiple categories (as in high, intermediate or low risk) or be continuous (interval or ratio scale).

For tests with multiple categories, or continuous results, the outcomes from testing are often reclassified into positive (disease confirmed) and negative (disease excluded). This is performed by defining a threshold: the test positivity cut-off. Results that exceed the threshold would then be called positive index test results. In other studies, an ROC curve is derived, by calculating the sensitivity–specificity pairs for all possible cut-offs.

To evaluate the validity and applicability of these classifications, readers would like to know these positivity cut-offs or result categories, how they were determined and whether they were defined prior to the study or after collecting the data. Prespecified thresholds can be based on (1) previous studies, (2) cut-offs used in clinical practice, (3) thresholds recommended by clinical practice guidelines or (4) thresholds recommended by the manufacturer. If no such thresholds exist, the authors may be tempted to explore the accuracy for various thresholds after the data have been collected.

If the authors selected the positivity cut-off after performing the test, choosing the one that maximised test performance, there is an increased risk that the resulting accuracy estimates are overly optimistic, especially in small studies.48 ,49 Subsequent studies may fail to replicate the findings.50 ,51

In the example, the authors stated the rationale for their selection of cut-offs.

Item 13a. Whether clinical information and reference standard results were available to the performers or readers of the index test

Item 13b. Whether clinical information and index test results were available to the assessors of the reference standard

Example. ‘Images for each patient were reviewed by two fellowship-trained genitourinary radiologists with 12 and 8 years of experience, respectively, who were blinded to all patient information, including the final histopathologic diagnosis’.52

Explanation. Some medical tests, such as most forms of imaging, require human handling, interpretation and judgement. These actions may be influenced by the information that is available to the reader.1 ,53 ,54 This can lead to artificially high agreement between tests, or between the index test and the reference standard.

If the reader of a test has access to information about signs, symptoms and previous test results, the reading may be influenced by this additional information, but this may still represent how the test is used in clinical practice.2 The reverse may also apply, if the reader does not have enough information for a proper interpretation of the index test outcome. In that case, test performance may be affected downwards, and the study findings may have limited applicability. Either way, readers of the study report should know to which extent, such additional information was available to test readers and may have influenced their final judgement.

In other situations, the assessors of the reference standard may have had access to the index test results. In those cases, the final classification may be guided by the index test result, and the reported accuracy estimates for the index test will be too high.1 ,2 ,27 Tests that require subjective interpretation are particularly susceptible to this bias.

Withholding information from the readers of the test is commonly referred to as ‘blinding’ or ‘masking’. The point of this reporting item is not that blinding is desirable or undesirable, but, rather, that readers of the study report need information about blinding for the index test and the reference standard to be able to interpret the study findings.

In the example, the readers of unenhanced CT for differentiating between renal angiomyolipoma and renal cell carcinoma did not have access to clinical information, nor to the results of histopathology, the reference standard in this study.

Item 14. Methods for estimating or comparing measures of diagnostic accuracy

Example. ‘Statistical tests of sensitivity and specificity were conducted by using the McNemar test for correlated proportions. All tests were two sided, testing the hypothesis that stereoscopic digital mammography performance differed from that of digital mammography. A p-value of 0.05 was considered as the threshold for significance’.55

Explanation. Multiple measures of diagnostic accuracy exist to describe the performance of a medical test, and their calculation from the collected data is not always straightforward.56 Authors should report the methods used for calculating the measures that they considered appropriate for their study objectives.

Statistical techniques can be used to test specific hypotheses, following from the study's objectives. In single-test evaluations, authors may want to evaluate if the diagnostic accuracy of the tests exceeds a prespecified level (eg, sensitivity of at least 95%, see Item 4).

Diagnostic accuracy studies can also compare two or more index tests. In such comparisons, statistical hypothesis testing usually involves assessing the superiority of one test over another, or the non-inferiority.57 For such comparisons, authors should indicate what measure they specified to make the comparison; these should match their study objectives, and the purpose and role of the index test relative to the clinical pathway. Examples are the relative sensitivity, the absolute gain in sensitivity and the relative diagnostic OR.58

In the example, the authors used McNemar's test statistic to evaluate whether the sensitivity and specificity of stereoscopic digital mammography differed from that of digital mammography in patients with elevated risk for breast cancer. In itself, the resulting p value is not a quantitative expression of the relative accuracy of the two investigated tests. Like any p value, it is influenced by the magnitude of the difference in effect and the sample size. In the example, the authors could have calculated the relative or absolute difference in sensitivity and specificity, including a 95% CI that takes into account the paired nature of the data.

Item 15. How indeterminate index test or reference standard results were handled

Example. ‘Indeterminate results were considered false-positive or false-negative and incorporated into the final analysis. For example, an indeterminate result in a patient found to have appendicitis was considered to have had a negative test result’.59

Explanation. Indeterminate results refer to those that are neither positive or negative.60 Such results can occur on the index test and the reference standard, and are a challenge when evaluating the performance of a diagnostic test.60–63 The occurrence of indeterminate test results varies from test to test, but frequencies up to 40% have been reported.62

There are many underlying causes for indeterminate test results.62 ,63 A test may fail because of technical reasons or an insufficient sample, for example, in the absence of cells in a needle biopsy from a tumour.43 ,64 ,65 Sometimes test results are not reported as just positive or negative, as in the case of ventilation–perfusion scanning in suspected pulmonary embolism, where the findings are classified in three categories: normal, high probability or inconclusive.66

In itself, the frequency of indeterminate test results is an important indicator of the feasibility of the test, and typically limits the overall clinical usefulness; therefore, authors are encouraged to always report the respective frequencies with reasons, as well as failures to complete the testing procedure. This applies to the index test and the reference standard.

Ignoring indeterminate test results can produce biased estimates of accuracy, if these results do not occur at random. Clinical practice may guide the decision on how to handle indeterminate results.

There are multiple ways for handling indeterminate test results in the analysis when estimating accuracy and expressing test performance.63 They can be ignored altogether, be reported but not accounted for or handled as a separate test result category. Handling these results as a separate category may be useful when indeterminate results occur more often, for example, in those without the target condition than in those with the target condition. It is also possible to reclassify all such results: as false positives or false negatives, depending on the reference standard result (‘worst-case scenario’), or as true positives and true negatives (‘best-case scenario’).

In the example, the authors explicitly chose a conservative approach by considering all indeterminate results from the index test as being false-negative (in those with the target condition) or false-positive (in all others), a strategy sometimes referred to as the ‘worst-case scenario’.

Item 16. How missing data on the index test and reference standard were handled

Example. ‘One vessel had missing FFR_CT and 2 had missing CT data. Missing data were handled by exclusion of these vessels as well as by the worst-case imputation’.67

Explanation. Missing data are common in any type of biomedical research. In diagnostic accuracy studies, they can occur for the index test and reference standard. There are several ways to deal with them when analysing the data.68 Many researchers exclude participants without an observed test result. This is known as ‘complete case’ or ‘available case’ analysis. This may lead to a loss in precision and can introduce bias, especially if having a missing index test or reference standard result is related to having the target condition.

Participants with missing test results can be included in the analysis if missing results are imputed.68–70 Another option is to assess the impact of missing test results on estimates of accuracy by considering different scenarios. For the index test, for example, in the ‘worst-case scenario’, all missing index test results are considered false-positive or false-negative depending on the reference standard result; in the ‘best-case scenario’, all missing index test results are considered true-positive or true-negative.

In the example, the authors explicitly reported how many cases with missing index test data they encountered and how they handled these data: they excluded them, but also applied a ‘worst-case scenario’.

Item 17. Any analyses of variability in diagnostic accuracy, distinguishing prespecified from exploratory

Example. ‘To assess the performance of urinary indices or their changes over the first 24 hours in distinguishing transient AKI [acute kidney injury] from persistent AKI, we plotted the receiver-operating characteristic curves for the proportion of true positives against the proportion of false positives, depending on the prediction rule used to classify patients as having persistent AKI. The same strategy was used to assess the performance of indices and their changes over time in two predefined patient subgroups; namely, patients who did not receive diuretic therapy and patients without sepsis’.71

Explanation. The relative proportion of false-positive or false-negative results of a diagnostic test may vary depending on patient characteristics, experience of readers, the setting and previous test results.2 ,3 Researchers may therefore want to explore possible sources of variability in test accuracy within their study. In such analyses, investigators typically assess differences in accuracy across subgroups of participants, readers or centres.

Post hoc analyses, performed after looking at the data, carry a high risk for spurious findings. The results are especially likely not to be confirmed by subsequent studies. Analyses that were prespecified in the protocol, before data were collected, have greater credibility.72

In the example, the authors reported that the accuracy of the urinary indices was evaluated in two subgroups that were explicitly prespecified.

Item 18. Intended sample size and how it was determined

Example. ‘Study recruitment was guided by an expected 12% prevalence of adenomas 6 mm or larger in a screening cohort and a point estimate of 80% sensitivity for these target lesions. We planned to recruit approximately 600 participants to achieve margins of sampling error of approximately 8 percentage points for sensitivity. This sample would also allow 90% power to detect differences in sensitivity between computed tomographic colonography and optical colonoscopy of 18 percentage points or more’.73

Explanation. Performing sample size calculations when developing a diagnostic accuracy study may ensure that a sufficient amount of precision is reached. Sample size calculations also take into account the specific objectives and hypotheses of the study.

Readers may want to know how the sample size was determined, and whether the assumptions made in this calculation are in line with the scientific and clinical background, and the study objectives. Readers will also want to learn whether the study authors were successful in recruiting the targeted number of participants. Methods for performing sample size calculations in diagnostic research are widely available,74–76 but such calculations are not always performed or provided in reports of diagnostic accuracy studies.77 ,78

Many diagnostic accuracy studies are small; a systematic survey of studies published in 8 leading journals in 2002 found a median sample size of 118 participants (IQR 71–350).77 Estimates of diagnostic accuracy from small studies tend to be imprecise, with wide CIs around them.

In the example, the authors reported in detail to achieve a desired level of precision for an expected sensitivity of 80%.