Abstract
The accuracy of diagnostic tests with binary end-points is most frequently measured by sensitivity and specificity. However, from the clinical perspective, the main purpose of a diagnostic agent is to assess the probability of a patient actually being diseased and hence predictive values are more suitable here. As predictive values depend on the pre-test probability of disease, we provide a method to take risk factors influencing the patient’s prior probability of disease into account, when calculating predictive values. Furthermore, approaches to assess confidence intervals and a methodology to compare predictive values by statistical tests are presented. Hereby the methods can be used to analyze predictive values of factorial diagnostic trials, such as multi-modality, multi-reader-trials. We further performed a simulation study assessing length and coverage probability for different types of confidence intervals, and we present the R-Package facROC that can be used to analyze predictive values in factorial diagnostic trials in particular. The methods are applied to a study evaluating CT-angiography as a noninvasive alternative to coronary angiography for diagnosing coronary artery disease. Hereby the patients’ symptoms are considered as risk factors influencing the respective predictive values.
Highlights
The main purpose of a diagnostic agent is to assess a patient’s true health status, so the probability of the test giving the correct diagnosis is an important assessment of diagnostic ability
Hereby the positive predictive value describes the probability that a patient with an abnormal test result is diseased and the negative predictive value represents the probability that a patient with normal test result is free of disease
As we assume that the prevalence of disease in each risk group has been assessed in prior studies, estimators for the positive and the negative predictive value for each risk group can be calculated by plugging in the estimators of sensitivity, specificity and prevalence in Equations (1) and (2)
Summary
The main purpose of a diagnostic agent is to assess a patient’s true health status, so the probability of the test giving the correct diagnosis is an important assessment of diagnostic ability. Even though sensitivity and specificity are useful and powerful measures to understand how effective a diagnostic test is, they involve a main disadvantage: these quantities do not assess the accuracy of a diagnostic agent in a practically useful way. They concentrate on how accurate the diagnostic test is in discriminating diseased and non-diseased subjects but fail to give an assessment of a normal or abnormal test result of an individual patient. This interpretation of the test result is only provided by predictive values
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
