A Bayesian framework for estimating the incremental value of a diagnostic test in the absence of a gold standard.

ABSTRACT:

Background

The absence of a gold standard, i.e., a diagnostic reference standard having perfect sensitivity and specificity, is a common problem in clinical practice and in diagnostic research studies. There is a need for methods to estimate the incremental value of a new, imperfect test in this context.

Methods

We use a Bayesian approach to estimate the probability of the unknown disease status via a latent class model and extend two commonly-used measures of incremental value based on predictive values [difference in the area under the ROC curve (AUC) and integrated discrimination improvement (IDI)] to the context where no gold standard exists. The methods are illustrated using simulated data and applied to the problem of estimating the incremental value of a novel interfe

SUBMITTER: Ling DI 

PROVIDER: S-EPMC4077291 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature