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Updated: Oct 8, 2026

Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Latent model for correlated binary data with diagnostic error
1Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland 20892-7938, USA. jshih@helix.nih.gov
Abstract:
We propose a methodology for modeling correlated binary data measured with diagnostic error. A shared random effect is used to induce correlations in repeated true latent binary outcomes and in observed responses and to link the probability of a true positive outcome with the probability of having a diagnosis error. We evaluate the performance of our proposed approach through simulations and compare it with an ad hoc approach. The methodology is illustrated with data from a study that assessed the probability of corneal arcus in patients with familial hypercholesterolemia.
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