Among 295 patients suspected of coronary artery disease, as independently determined by coronary angiography, we assessed variation in sensitivity and specificity according to patient history, physical examination, exercise test results, and disease severity in 207 patients with and 88 patients without coronary artery disease, respectively. We evaluated the extent to which the sensitivity, specificity, and likelihood ratio of the exercise test to diagnose coronary artery disease vary across subgroups of a certain patient population. Clinical vali- dation of these concepts needs to be obtained prior to its widespread application. Results: For a given PPV ρ approaching k, the number of positive test iterations given a prevalence φ needed is:, where ni = number of testing iterations necessary to achieve ρ, the desired positive predictive value, ln = the natural logarithm, a = sensitivity, b = specificity, φ = disease prevalence/pre-test probability and k = constant.Ĭonclusions: Based on the aforementioned derivation, we provide reference tables for the number of test iterations needed to obtain a ρ(φ) of 50, 75, 95 and 99% as a function of various levels of sensitivity, specificity and disease prevalence/pre-test probability. We likewise derive the equation which determines the number of iterations of a positive test needed to obtain a desired positive pre- dictive value, represented graphically by the tablecloth function. Methods: We use Bayes’ Theorem to derive the positive predictive value equation, and apply the Bayesian updating method to obtain the equation for the positive predictive value (PPV) following repeated testing.

Herein, we establish a mathematical model to determine whether sequential testing with a single test overcomes the aforementioned Bayesian lim- itations and thus improves the reliability of screening tests. This method is also attainable to implement nonparametrically.Background: Bayes’ Theorem confers inherent limitations on the accuracy of screening tests as a function of disease prevalence.

This method is applied to a real-data example and simulation studies have been carried out to assess the statistical properties and predictive accuracy of the proposed composite test. This sequential composite test is based on a mixture of two multivariate normal latent models for the distribution of the test results in case and non-case groups, and the optimal classification rule is derived returning the greatest sensitivity at a given specificity. Rather than combining the tests, we treat the tests as a sequence.

The aim of this paper is to develop a method to construct a composite test from multiple applicable tests and derive an optimal classification rule under the absence of a gold standard. When there is a lack of a gold standard, an algorithm of classifying subjects into the case and non-case status is necessary for combining multiple markers. In many medical applications, combining information from multiple biomarkers could yield a better diagnosis than any single one on its own.

Net sensitivity for sequential testing