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Latent class analysis of diagnostic agreement
Author(s) -
Uebersax John S.,
Grove William M.
Publication year - 1990
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780090509
Subject(s) - latent class model , computer science , class (philosophy) , interpretation (philosophy) , set (abstract data type) , data set , data mining , statistics , artificial intelligence , machine learning , mathematics , programming language
Abstract We describe methods based on latent class analysis for analysis and interpretation of agreement on dichotomous diagnostic ratings. This approach formulates agreement in terms of parameters directly related to diagnostic accuracy and leads to many practical applications, such as estimation of the accuracy of individual ratings and the extent to which accuracy may improve with multiple opinions. We describe refinements in the estimation of parameters for varying panel designs, and apply latent class methods successfully to examples of medical agreement data that include data previously found to be poorly fitted by two‐class models. Latent class techniques provide a powerful and flexible set of tools to analyse diagnostic agreement and one should consider them routinely in the analysis of such data.