Accuracy of Diagnostic Tests (original) (raw)
Related papers
Evaluating diagnostic tests: The area under the ROC curve and the balance of errors
Statistics in Medicine, 2010
Because accurate diagnosis lies at the heart of medicine, it is important to be able to evaluate the effectiveness of diagnostic tests. A variety of accuracy measures are used. One particularly widely used measure is the AUC, the area under the Receiver Operating Characteristic (ROC) curve. This measure has a well-understood weakness when comparing ROC curves which cross. However, it also has the more fundamental weakness of failing to balance different kinds of misdiagnosis effectively. This is not merely an aspect of the inevitable arbitrariness in choosing a performance measure, but is a core property of the way the AUC is defined. This property is explored, and an alternative, the H measure, is described.
How to Analyze the Diagnostic Performance of a New Test? Explained with Illustrations
Journal of Laboratory Physicians, 2021
Diagnostic tests are pivotal in modern medicine due to their applications in statistical decision-making regarding confirming or ruling out the presence of a disease in patients. In this regard, sensitivity and specificity are two most important and widely utilized components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard test. Other diagnostic indices like positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, accuracy of a diagnostic test, and the effect of prevalence on various diagnostic indices have also been discussed. We have tried to present the performance of a classification model at all classification thresholds by reviewing the receiver operating characteristic (ROC) curve and the depiction of the tradeoff between sensitivity and (1–specificity) across a series of cutoff points when the diagnostic test is on a continuous scale. The area under the ROC (AUROC) and...
Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation
Caspian journal of internal medicine, 2013
This review provides the basic principle and rational for ROC analysis of rating and continuous diagnostic test results versus a gold standard. Derived indexes of accuracy, in particular area under the curve (AUC) has a meaningful interpretation for disease classification from healthy subjects. The methods of estimate of AUC and its testing in single diagnostic test and also comparative studies, the advantage of ROC curve to determine the optimal cut off values and the issues of bias and confounding have been discussed.
Diagnostic Accuracy of Rapid Antigen Test for COVID-19 Infection: A Retrospective Analysis
JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH
Introduction: For the diagnosis of Coronavirus Disease 2019 (COVID-19) disease, Real-Time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) is a laboratory-based technique and is considered a gold standard test, but is time consuming. A Rapid Antigen Test (RAT) is used for screening which is an immunoassay that identifies the presence of a viral antigen causing infection at the point of care. The RAT is quick, inexpensive, easily accessible and doesn’t need lab handling or sample preprocessing. Aim: To measure the sensitivity, specificity, Negative Predictive Value (NPV) and Positive Predictive Value (PPV) of RAT in comparison to RT-PCR. Materials and Methods: This retrospective study was conducted in Department of Community Medicine at Government Medical College (tertiary care centre), Surat, Gujarat, India, using secondary data from 1st July 2020 to 5th Dec 2020. The samples were collected from all the patients of Acute Respiratory Illness (ARI), Severe Acute Respiratory Il...
Diagnostics
Background: Currently, assessing the diagnostic performance of new laboratory tests assumes a perfect reference standard, which is rarely the case. Wrong classifications of the true disease status will inevitably lead to biased estimates of sensitivity and specificity. Objectives: Using Bayesian’ latent class models (BLCMs), an approach that does not assume a perfect reference standard, we re-analyzed data of a large prospective observational study assessing the diagnostic accuracy of an antigen test for the diagnosis of SARS-CoV-2 infection in clinical practice. Methods: A cohort of consecutive patients presenting to a COVID-19 testing facility affiliated with a Swiss University Hospital were recruited (n = 1465). Two real-time PCR tests were conducted in parallel with the Roche/SD Biosensor rapid antigen test on nasopharyngeal swabs. A two-test (PCR and antigen test), three-population BLCM was fitted to the frequencies of paired test results. Results: Based on the BLCM, the sensit...
COMPARING SEVERAL DIAGNOSTIC PROCEDURES USING THE INTRINSIC MEASURES OF ROC CURVE
Keywords: Diagnostic Procedure; ROC curve; AUC; Sensitivity Comparison of diagnostic tests is essential in medicine. Test procedures for comparing two or more ROC curves are all based on measures d ' , AUC and the maximum likelihood estimates of binormal ROC curves. However, intrinsic measures such as sensitivity and specificity also play a pivotal role in assessing the performance of several diagnostic procedures. In this paper, a new methodology is proposed in order to compare several diagnostic procedures using the intrinsic measures of ROC curve
Archives of Medical Science, 2021
IntroductionThe rapid transmission of Coronavirus disease 2019 (COVID-19) requires a fast, accurate, and affordable detection method. Despite doubts of its diagnostic accuracy, Rapid Diagnostic Test (RDT) is world-widely used in consideration for its practicality. This systematic review aims to determine the diagnostic accuracy of antibody-based RDT in detecting COVID-19.Material and methodsA literature search was carried out on five journal databases using the PRISMA-P 2015 method. We included all studies published up to February 2021. The risk of bias was evaluated using The Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. Data regarding peer-review status, study design, tests kit information, immunoglobulin class, target antigen, and the number of samples were extracted and tabulated. We estimated the pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with a 95% confidence interval....
Clinical Prediction of Coronavirus Disease-2019: How Accurate Can One Be?
Cureus, 2020
Background Some models based on clinical information have been reported to predict which patients have Coronavirus Disease-2019 (COVID-19) pneumonia but have failed so far to yield reliable results. We aimed to determine if physicians were able to accurately predict which patients, as described in clinical vignettes, had, or did not have this infection using their clinical acumen and epidemiological data. Methods Of 1177 patients under investigation for COVID-19 admitted, we selected 20 and presented them in a vignette form. We surveyed physicians from different levels of training (<5, and five or more years after graduation from medical school) and included non-medical participants as a control group. We asked all participants to predict the result of the PCR test for COVID-19. We measured the accuracy of responses as a whole, and at three stages of the pandemic associated with a growing incidence of COVID-19 in the community. We calculated the inter-rater reliability, sensitivity, and specificity of the clinical prediction as a whole and by pandemic stage. Results Between June 8 and August 28, 2020, 82 doctors and 20 non-medical participants completed the survey. The accuracy was 58% (59% for doctors and 52% for non-medical, p=0.002). The lowest accuracy was noted for cases in the pandemic middle stage; years of postgraduate training represented no difference. Of the 2040 total answers, 1176 were accurate and 864 inaccurate (349 false positives and 515 false negatives). Conclusion The influence of symptomatic positivity, confirmation bias, and rapid expertise acquisition on accuracy is discussed, as the disease is new, time after graduation made no difference in the response accuracy. The limited clinical diagnostic capacity emphasizes the need for a reliable diagnostic test.
A non-inferiority test for diagnostic accuracy based on the paired partial areas under ROC curves
Statistics in Medicine, 2008
Non-inferiority is a reasonable approach to assessing the diagnostic accuracy of a new diagnostic test if it provides an easier administration or reduces the cost. The area under the receiver operating characteristic (ROC) curve is one of the common measures for the overall diagnostic accuracy. However, it may not differentiate the various shapes of the ROC curves with different diagnostic significances. The partial area under the ROC curve (PAUROC) may present an alternative that can provide additional and complimentary information for some diagnostic tests which require false-positive rate that does not exceed a certain level. Non-parametric and maximum likelihood methods can be used for the non-inferiority tests based on the difference in paired PAUROCs. However, their performance has not been investigated in finite samples. We propose to use the concept of generalized p-value to construct a non-inferiority test for diagnostic accuracy based on the difference in paired PAUROCs. Simulation results show that the proposed non-inferiority test not only adequately controls the size at the nominal level but also is uniformly more powerful than the non-parametric methods. The proposed method is illustrated with a numerical example using published data.
COVID-19 Testing – Impact of Prevalence, Sensitivity, and Specificity on Patient Risk and Cost
2020
Since the beginning of the year 2020, the global healthcare system has been challenged by the threat of the SARS-COV 2 virus. Molecular, antigen, and antibody testing are the mainstay to identify infected patients and fight the virus. Molecular and antigen tests that detect the presence of the virus are relevant in the acute phase only. Serological assays detect antibodies to the Sars-CoV-2 virus in the recovering and recovered phase. Each testing methodology has its advantages and disadvantages. To evaluate the test methods, sensitivity (percent positive agreement - PPA) and specificity (percent negative agreement – PNA) are the most common metrics utilized, followed by the positive and negative predictive value (PPV and NPV), the probability that a positive or negative test result represents a true positive or negative patient. In this paper, we illustrate how patient risk and clinical costs are driven by false-positive and false-negative results. We demonstrate the value of repor...