Latent structure analysis of classification errors in screening and clinical diagnosis: An alternative to classification analysis

Classification analysis is used widely to detect classification errors determined by evaluating a screening or diagnostic instrument against a criterion measure. The usefulness of classification analysis is limited because it assumes an error-free criterion and provides no statistical test of the validity of that assumption. The classification-analysis model is a special case of a general latent-class model. This paper presents latent-class models that fall within the purview of the general model presented by Clogg & Goodman (1984, 1985) and Waiter & Irwig (1988). Variations on the general latent-class model allow the investigator to determine whether the criterion measure and/or the diagnostic or screening procedure for multiple groups can be considered error-free. Analogous to the problem of differential item functioning, the general model makes it possible to test assumptions regarding classification errors that could occur across groups. The proportion of individuals who may be misclassified by a screening instrument or diagnostic procedure can also be determined using latent structure techniques.