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Joint Modeling of Sensitivity and Specificity by Gavino Puggioni; Alan E. Gelfand; Joann G. Elmore is a Mathematics article available to read on EtoBox.

What is Joint Modeling of Sensitivity and Specificity about?

## Abstract Sensitivity and specificity are two customary performance measures associated with medical diagnostic tests. Typically, they are modeled independently as a function of risk factors using logistic regression, which provides estimated functions for these probabilities. Change in these probabilities across levels of risk factors is of primary interest and the indirect relationship is often displayed using a receiver operating characteristic curve. We refer to this as analysis of ‘first‐order’ behavior. Here, we consider what we refer to as ‘second‐order’ behavior where we examine the stochastic dependence between the (random) estimates of sensitivity and specificity. To do so, we argue that a model for the four cell probabilities that determine the joint distribution of screening test result and outcome result is needed. Such a modeling induces sensitivity and specificity as functions of these cell probabilities. In turn, this raises the issue of a coherent specification for these cell probabilities, given risk factors, i.e. a specification that ensures that all probabilities calculated under it fall between 0 and 1. This leads to the question of how to provide models that

Who reads Joint Modeling of Sensitivity and Specificity?

Author

Gavino Puggioni; Alan E. Gelfand; Joann G. Elmore

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0277-6715)

Published

2008

Language

EN

Field

Mathematics (Physical Sciences)