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Comparative diagnostic test accuracy studies assess and compare the accuracy of 2 or more tests in the same study. Although these studies have the potential to yield reliable evidence regarding comparative accuracy, shortcomings in the design, conduct, and analysis may bias their results. The currently recommended quality assessment tool for diagnostic test accuracy studies, QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2), is not designed for the assessment of test comparisons. The QUADAS-C (Quality Assessment of Diagnostic Accuracy Studies-Comparative) tool was developed as an extension of QUADAS-2 to assess the risk of bias in comparative diagnostic test accuracy studies. Through a 4-round Delphi study involving 24 international experts in test evaluation and a face-to-face consensus meeting, an initial version of the tool was developed that was revised and finalized following a pilot study among potential users. The QUADAS-C tool retains the same 4-domain structure of QUADAS-2 (Patient Selection, Index Test, Reference Standard, and Flow and Timing) and comprises additional questions to each QUADAS-2 domain. A risk-of-bias judgment for comparative accuracy requires a risk-of-bias judgment for the accuracy of each test (resulting from QUADAS-2) and additional criteria specific to test comparisons. Examples of such additional criteria include whether participants either received all index tests or were randomly assigned to index tests, and whether index tests were interpreted with blinding to the results of other index tests. The QUADAS-C tool will be useful for systematic reviews of diagnostic test accuracy addressing comparative questions. Furthermore, researchers may use this tool to identify and avoid risk of bias when designing a comparative diagnostic test accuracy study.

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.

When no gold standard is available to evaluate a diagnostic or screening test, as is often the case, an imperfect reference standard test must be used instead. Furthermore, the errors of the test and its reference standard may not be independent. Some authors have opined that positively dependent errors will lead to overestimation of test performance. Although positive dependence does increase agreement between the test and the reference standard, it is not clear if test accuracy will necessarily be overestimated in this situation, and the case of negatively associated test errors is even less clear. To examine this issue in more detail, we derive the apparent sensitivity, specificity, and overall accuracy of a test relative to an imperfect reference standard and the bias in these parameters. We demonstrate that either positive or negative bias can occur if the reference standard is imperfect. The type and magnitude of bias depend on several components: the disease prevalence, the true test sensitivity and specificity, the covariance between the false-negative test errors among the true disease cases, and the covariance between the false-positive test errors among the true noncases. If, for example, sensitivity and specificity are 0.8 for both the test and reference standard and the errors have a moderate positive dependence, test sensitivity is then underestimated at low prevalence but overestimated at high prevalence, while the opposite occurs for specificity. We illustrate these ideas through general numerical calculations and an empirical example of screening for breast cancer with magnetic resonance imaging and mammography., (Copyright © 2012 John Wiley & Sons, Ltd.)

Background: There is substantial variability in the prognosis of acute low back pain (LBP). The ability to identify the probability of individual patients recovering by key time points would be valuable in making informed decisions about the amount and type of treatment to provide. Predicting recovery based on presentation 1-week after initially seeking care is clinically important and may be more accurate than predictions made at initial presentation. The aim of this study was to predict the probability of recovery at 1-week, 1-month and 3-months after 1-week review in patients who still have LBP 1-week after initially seeking care.Methods: The study sample comprised 1070 patients with acute LBP, with a pain score of ≥2 1-week after initially seeking care. The primary outcome measure was days to recovery from pain. Ten potential prognostic factors were considered for inclusion in a multivariable Cox regression model.Results: The final model included duration of current episode, number of previous episodes, depressive symptoms, intensity of pain at 1-week, and change in pain over the first week after seeking care. Depending on values of the predictor variables, the probability of recovery at 1-week, 1-month and 3-months after 1-week review ranged from 4% to 59%, 19% to 91% and 30% to 97%, respectively. The model had good discrimination (C = 0.758) and calibration.Conclusions: This study found that a model based on five easily collected variables could predict the probability of recovery at key time points in people who still have LBP 1-week after seeking care.Significance: A clinical prediction model based on five easily collected variables was able to predict the likelihood of recovery from an episode of acute LBP at three key time points. The model had good discrimination (C = 0.758) and calibration. [ABSTRACT FROM AUTHOR]

Background Dermoscopy is a noninvasive technique that enables the clinician to perform direct microscopic examination of diagnostic features, not seen by the naked eye, in pigmented skin lesions. Diagnostic accuracy of dermoscopy has previously been assessed in meta-analyses including studies performed in experimental and clinical settings. Objectives To assess the diagnostic accuracy of dermoscopy for the diagnosis of melanoma compared with naked eye examination by performing a meta-analysis exclusively on studies performed in a clinical setting. Methods We searched for publications from 1987 to January 2008 and found nine eligible studies. The selected studies compare diagnostic accuracy of dermoscopy with naked eye examination using a valid reference test on consecutive patients with a defined clinical presentation, performed in a clinical setting. Hierarchical summary receiver operator curve analysis was used to estimate the relative diagnostic accuracy for clinical examination with, and without, the use of dermoscopy. Results We found the relative diagnostic odds ratio for melanoma, for dermoscopy compared with naked eye examination, to be 15·6 [95% confidence interval (CI) 2·9–83·7, P = 0·016]; removal of two outlier studies changed this to 9·0 (95% CI 1·5–54·6, P = 0·03). Conclusions Dermoscopy is more accurate than naked eye examination for the diagnosis of cutaneous melanoma in suspicious skin lesions when performed in the clinical setting. [ABSTRACT FROM AUTHOR]