Evaluation of diagnostic criteria for autism using latent class models

To illustrate the use of latent class models for comparing alternative diagnostic criteria for autism. The models are based on the notion that the "true" classification of an individual is unknown but does exist at some unobserved, or "latent," level. Estimates of sensitivity and specificity are obtained for each set of diagnostic criteria through maximum likelihood techniques in relation to the latent standard.

In this paper, latent class models are used to compare DSM-III, DSM-III-R, and ICD-10 criteria for autism in a sample of 342 individuals with autism or other developmental disabilities. The diagnoses were made by one or more child psychiatrists who evaluated each patient and assigned a diagnosis of autism based on their own expert clinical judgment. In addition, the raters also determined whether criteria were met for the various diagnostic systems.

The results indicate that the ICD-10 criteria agree best with the latent standard and a diagnosis based on expert opinion.

It is suggested that latent class models can be usefully applied to the evaluation of other psychiatric disorders as well and represent an important new tool in evaluating diagnostic criteria by providing a way of dealing with data lacking an observable gold standard.