Latent model for correlated binary data with diagnostic error.
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.
Shih
J H
JH
Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland 20892-7938, USA. jshih@helix.nih.gov
Albert
P S
PS
D000328:Adult; D000367:Age Factors; D001112:Arcus Senilis / Q000150:complications / Q000175:diagnosis / Q000453:epidemiology; D001699:Biometry; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003951:Diagnostic Errors / Q000706:statistics & numerical data*; D006801:Humans; D006938:Hyperlipoproteinemia Type II / Q000150:complications; D008954:Models, Biological; D015233:Models, Statistical; D011336:Probability; D012307:Risk Factors