Variable Selection for Latent Class Analysis with Application to Low Back Pain Diagnosis
|Title:||Variable Selection for Latent Class Analysis with Application to Low Back Pain Diagnosis||Authors:||Fop, Michael
Murphy, Thomas Brendan
|Permanent link:||http://hdl.handle.net/10197/9199||Date:||28-Dec-2017||Online since:||2018-01-24T12:07:48Z||Abstract:||The identification of most relevant clinical criteria related to low back pain disordersis a crucial task for a quick and correct diagnosis of the nature of pain and its treatment.Data concerning low back pain can be of categorical nature, in form of check-list in whicheach item denotes presence or absence of a clinical condition. Latent class analysis is amodel-based clustering method for multivariate categorical responses which can be appliedto such data for a preliminary diagnosis of the type of pain. In this work we propose avariable selection method for latent class analysis applied to the selection of the mostuseful variables in detecting the group structure in the data. The method is based onthe comparison of two different models and allows the discarding of those variables withno group information and those variables carrying the same information as the alreadyselected ones. We consider a swap-stepwise algorithm where at each step the models arecompared through and approximation to their Bayes factor. The method is applied tothe selection of the clinical criteria most useful for the clustering of patients in differentclasses of pain. It is shown to perform a parsimonious variable selection and to give agood clustering performance. The quality of the approach is also assessed on simulateddata||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||The Institute of Mathematical Statistics||Journal:||Annals of Applied Statistics||Volume:||11||Issue:||4||Start page:||2080||End page:||2110||Copyright (published version):||2017 Institute of Mathematical Studies||Keywords:||Clinical criteria selection; Clustering; Latent class analysis; Low back pain; Mixture models; Model-based clustering; Variable selection||DOI:||10.1214/17-AOAS1061||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
Insight Research Collection
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