✎✎✎ CC-501: A Case Study

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CC-501: A Case Study

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Case study: setting the problem (Antonio Ghezzi)

Informing Faculty IF is a repository of case studies about higher education teaching, or administrative situations. Originally established in a journal format, in the decision was made to change the outlet to a repository. Individuals interested in submitting new cases should email Editor-in-Chief Grandon Gill directly. The Informing Faculty repository invites faculty members in higher education to submit case studies relating to situations that they or their colleagues have faced or are facing. These case studies should be designed to encourage thoughtful discussions of the issues involved, in a classroom, workshop or online setting.

Second, selection of patients with a negative reference test result as 'controls' may lead to inclusion of controls that correspond to a different clinical domain, i. A third disadvantage of such case-control design is that absolute probabilities of disease presence given the index test results, i. Cases and controls are sampled from a source population of unknown size. The total number of patients that were initially suspected of the target disease based on the presence of symptoms or signs, i.

Hence, the sampling fraction of cases and controls is unknown and valid estimates of the absolute probabilities of disease presence cannot be calculated [ 12 ]. A nested case-control study in diagnostic research includes the full population or cohort of patients suspected of the target disease. The 'true' disease status is obtained for all these patients with the reference standard. Hence, there is no referral or partial verification bias. The results of the index tests can then be obtained for all subjects with the target condition but only for a sample of the subjects without the target condition. Usually all patients with the target disease are included, but this could as well be a sample of the cases.

Besides the absence of bias, all measures of diagnostic accuracy, including the positive and negative predictive values, can simply be obtained by weighing the controls with the case-control sampling fraction, as explained in Figure 1. Theoretical example of a full study population and a nested case-control sample. The index test result and the outcome are obtained for all patients of the study population.

In a case-control sample however, the controls are sampled from a source population with unknown size. Therefore, the sample fraction is unknown and valid estimate of the PPV cannot be calculated. The nested case-control study design can be advantageous over a full cross-sectional cohort design when actual disease prevalence in subjects suspected of a target condition is low, the index test is costly to perform, or if the index test is invasive and may lead to side effects.

Under these conditions, one limits patient burden and saves time and money as the index test is performed in only a sample of the control subjects. Furthermore, the nested case-control design is of particular value when stored data serum, images etc. Using a nested case-control design, only data of a sample of the full study population need to be retrieved and analysed without having to perform a new diagnostic study from the start.

This may for example apply to evaluation of tumour markers to detect cancer, but also for imaging or electrophysiology tests. Diagnostic accuracy estimates derived from a nested case-control study, should be virtually identical to a full cohort analysis. However, the variability of the accuracy estimates will increase with decreasing sample size. We illustrate this with data of a diagnostic study on a cohort of patients who were suspected of DVT.

A cross-sectional study was performed among a cohort of adult patients suspected of deep vein thrombosis DVT in primary care. This suspicion was primarily defined by the presence of a painful and swollen or red leg that existed no longer than 30 days. Details on the setting, data collection and main results have been described previously. Patients were excluded if pulmonary embolism was suspected. The general practitioner systematically documented information on patient history and physical examination. Patient history included information such as age, gender, history of malignancy, and recent surgery.

Physical examination included swelling of the affected limb and difference in circumference of the calves calculated as the circumference in centimetres of affected limb minus circumference of unaffected limb, further referred to as calf difference test. Subsequently, all patients were referred to undergo D-dimer testing. The echographist was blinded to the results of patient history, physical examination, and the D-dimer assay.

In all samples, we included always all cases with DVT. Controls were randomly sampled from the subjects without DVT. We applied four different and frequently used case-control ratios, i. In the approach, we sampled with replacement. For each case-control ratio, nested case-control samples were drawn. We focussed on two important diagnostic tests for DVT, i. The latter was specifically chosen as it allowed for the estimation and thus comparison of the area under the ROC curve ROC area. Diagnostic accuracy measures of both tests were estimated for the four case-control ratios and compared with those obtained from the full study population.

Measures of diagnostic accuracy included sensitivity and specificity, positive and negative predictive values and the odds ratio OR for the D-dimer test, and the OR and the ROC area for the calf difference test. For each case-control ratio, the point estimates and variability were determined. The median estimate of the samples was considered as the point estimate. Analyses were performed using SPSS version The distributions of the test characteristics in the control samples were similar as for the patients from the full study population without DVT Table 1.

In the full study population the sensitivity and negative predictive value were high for the D-dimer test, 0. The OR for the calf difference test was 1. The average estimates of diagnostic accuracy for each of the four case-control ratios were similar to the corresponding estimates of the full study population Figure 2. For example, the negative predictive value of the D-dimer test was 0. The OR of the calf difference test was 1.

Estimates of diagnostic accuracy of the D-dimer test and calf difference test for the nested case-control samples with case-control ratios ranging from to The boxes indicate mean values and corresponding interquartile ranges 25 th and 75 th percentile. Whiskers indicate 2. The dotted lines represent the values estimated in the full study population. The use of conventional case-control studies in diagnostic research has often been associated with biased estimates of diagnostic accuracy, due to the incorrect sampling of subjects [ 3 — 6 , 18 ]. Moreover, this study design does not allow for the estimation of the desired absolute disease probabilities. We discussed and showed that a case-control study nested within a well defined cohort of subjects suspected of a particular target disease with known sample size can yield valid estimates of diagnostic accuracy of an index test, including the absolute probabilities of disease presence or absence.

Diagnostic accuracy parameters derived from a full cross-sectional cohort of patients suspected of DVT were similar to the estimates derived from various nested case-control samples averaged over simulations. Expectedly, the variability decreased with increasing number of controls, making the measures estimated in the larger case-control samples more precise. As discussed, the number of subjects from which the index test results need to be retrieved can substantially be reduced with a nested case-control design.

Hence, the nested case-control design is particularly advantageous when the prevalence of the target condition in the cohort of patients suspected of the target disease is rare, when the index test results are costly or difficult to collect and for re-analysing stored images or specimen. However, precision of the diagnostic accuracy measures will be hampered by increased variability when too little control patients are included. Rutjes et al nicely discussed limitations of different study designs in diagnostic research [ 6 ]. They proposed the 'two-gate design with representative sampling' which resembles the nested case-control design in this paper as a valid design.

We confirmed their proposition with a quantitative analysis of a diagnostic study. Rutjes et al suggested not to use the term 'nested case-control' to prevent confusion with etiologic studies where this design is commonly applied. Indeed, diagnostic and etiologic research differs fundamentally, first and foremost on the concept of time. Diagnostic accuracy studies are, in contrast to etiologic studies, typically cross-sectional in nature. Furthermore, diagnostic associations between index and reference tests are purely descriptive, whereas in etiologic studies causal associations and potential confounding are involved. Despite these major differences we believe there is no reason not to use the term nested case-control study in diagnostic research as well.

The term inherently refers to the method of sampling of study subjects which can be the same in a diagnostic or etiologic setting, and has no direct bearing on the other issues typically related to etiologic case control studies. Our findings support the view that the nested case-control study is a valid and efficient design for diagnostic studies. We believe that the nested case-control approach should be applied more often in diagnostic research, and also be re appraised in current guidelines on diagnostic methodology. J Clin Epidemiol. Ann Intern Med. Article PubMed Google Scholar.

Clin Chem. Kraemer H: Evaluating Medical Tests. Google Scholar. Mantel N: Synthetic retrospective studies and related topics. Am Heart J. Ernster VL: Nested case-control studies. Prev Med. Encyclopedia of Biostatistics. Edited by: Armitage PCT. Mantel N, Haenszel W: Statistical aspects of the analysis of data from retrospective studies of disease.

J Natl Cancer Inst.

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