Estimating Diagnostic Error without a Gold Standard: A Mixed Membership Approach

Authored by: Edoardo M. Airoldi , David M. Blei , Elena A. Erosheva , Stephen E. Fienberg , Elena A. Erosheva , Cyrille Joutard

Handbook of Mixed Membership Models and Their Applications

Print publication date:  November  2014
Online publication date:  November  2014

Print ISBN: 9781466504080
eBook ISBN: 9781466504097
Adobe ISBN:

10.1201/b17520-10

 Download Chapter

 

Abstract

Evaluation of sensitivity and specificity of diagnostic tests in the absence of a gold standard typically relies on latent structure models. For example, two extensions of latent class models in the biostatistics literature, Gaussian random effects (Qu et al., 1996) and finite mixture (Albert and Dodd, 2004), form the basis of several recent approaches to estimating sensitivity and specificity of diagnostic tests when no (or partial) gold standard evaluation is available. These models attempt to account for additional item dependencies that cannot be explained with traditional latent class models, where the classes typically correspond to healthy and diseased individuals.

 Cite
Search for more...
Back to top

Use of cookies on this website

We are using cookies to provide statistics that help us give you the best experience of our site. You can find out more in our Privacy Policy. By continuing to use the site you are agreeing to our use of cookies.