In recent years diagnostic practice in psychiatry has become increasingly structured in an attempt to standardize definitions of disorders and improve reliability.
At the same time there has been an increasing recognition of the need to take account of uncertainty in the process of diagnostic decision making.
For the most part, diagnosis is still represented by a binary outcome while this is known to entail a substantial loss of information.
Many diagnostic schemes involve, in part, taking thresholds on the numbers of symptoms required from symptom lists.
A model is proposed here, using ideas derived from latent class analysis to permit generalization from these schemes through moving from a binary to a probabilistic measure of psychiatric case status and replacing thresholds with smoothed transitions.
An outcome measure is produced where disorder status is expressed in terms of probabilities without changing the meaning of the original measure.
Prevalence estimates (using ICD-10 Depressive Episode criteria) are more stable and can be given with increased precision.
Disorder status when expressed in this way retains more diagnostic information and provides a useful extension to traditional binary analyses when looking at prevalence and risk factor estimation.
Mots-clés Pascal : Etat dépressif, Trouble humeur, Modèle probabiliste, Diagnostic, Prévalence, Epidémiologie, Mesure, Facteur risque, Modèle variable latente, Méthodologie, Homme
Mots-clés Pascal anglais : Depression, Mood disorder, Probabilistic model, Diagnosis, Prevalence, Epidemiology, Measurement, Risk factor, Latent variable model, Methodology, Human
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 97-0380625
Code Inist : 002B18C07A. Création : 12/09/1997.