A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints.

ABSTRACT: In application of diagnostic accuracy, it is possible that a priori information may exist regarding the test score distributions, either between different disease populations for a single test or between multiple correlated tests. Few have considered constrained diagnostic accuracy analysis when the true disease status is binary; almost none when the disease status is ordinal. Motivated by a study on diagnosing endometriosis, we propose an approach to estimating diagnostic accuracy measures that can incorporate different stochastic order constraints on the test scores when an ordinal true disease status is in consideration. We show that the Dirichlet process mixture provides a convenient framework to both flexibly model the test score distributions and embed the a priori ordering constrain

SUBMITTER: Chen Z 

PROVIDER: S-EPMC12475919 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature