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The identification of most relevant clinical criteria related to low backpain disorders may aid the evaluation of the nature of pain suffered in a waythat usefully informs patient assessment and treatment. Data concerning lowback pain can be of categorical nature, in the form of a check-list in whicheach item denotes presence or absence of a clinical condition. Latent classanalysis is a model-based clustering method for multivariate categoricalresponses, which can be applied to such data for a preliminary diagnosis of thetype of pain. In this work, we propose a variable selection method for latentclass analysis applied to the selection of the most useful variables indetecting the group structure in the data. The method is based on thecomparison of two different models and allows the discarding of those variableswith no group information and those variables carrying the same information asthe already selected ones. We consider a swap-stepwise algorithm where at eachstep the models are compared through an approximation to their Bayes factor.The method is applied to the selection of the clinical criteria most useful forthe clustering of patients in different classes. It is shown to perform aparsimonious variable selection and to give a clustering performance comparableto the expert-based classification of patients into three classes of pain.

Variable Selection for Latent Class Analysis with Application to Low Back Pain Diagnosis