  The identification of most relevant clinical criteria related to low back
pain disorders may aid the evaluation of the nature of pain suffered in a way
that usefully informs patient assessment and treatment. Data concerning low
back pain can be of categorical nature, in the form of a check-list in which
each item denotes presence or absence of a clinical condition. Latent class
analysis is a model-based clustering method for multivariate categorical
responses, which can be applied to such data for a preliminary diagnosis of the
type of pain. In this work, we propose a variable selection method for latent
class analysis applied to the selection of the most useful variables in
detecting the group structure in the data. The method is based on the
comparison of two different models and allows the discarding of those variables
with no group information and those variables carrying the same information as
the already selected ones. We consider a swap-stepwise algorithm where at each
step the models are compared through an approximation to their Bayes factor.
The method is applied to the selection of the clinical criteria most useful for
the clustering of patients in different classes. It is shown to perform a
parsimonious variable selection and to give a clustering performance comparable
to the expert-based classification of patients into three classes of pain.
