A penalized latent class model for ordinal data.
Latent class models provide a useful framework for clustering observations based on several features. Application of latent class methodology to correlated, high-dimensional ordinal data poses many challenges. Unconstrained analyses may not result in an estimable model. Thus, information contained in ordinal variables may not be fully exploited by researchers. We develop a penalized latent class model to facilitate analysis of high-dimensional ordinal data. By stabilizing maximum likelihood estimation, we are able to fit an ordinal latent class model that would otherwise not be identifiable without application of strict constraints. We illustrate our methodology in a study of schwannoma, a peripheral nerve sheath tumor, that included 3 clinical subtypes and 23 ordinal histological measures.
Desantis
Stacia M
SM
Department of Biostatistics, Harvard University, 655 Huntington Avenue, Boston, MA 02115, USA. sdesanti@hsph.harvard.edu
Houseman
E Andrés
EA
Coull
Brent A
BA
Stemmer-Rachamimov
Anat
A
Betensky
Rebecca A
RA
D001699:Biometry / Q000379:methods; D016000:Cluster Analysis; D019937:Diagnostic Techniques and Procedures / Q000706:statistics & numerical data; D006652:Histological Techniques / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D009442:Neurilemmoma / Q000145:classification* / Q000473:pathology; D012107:Research Design; D014894:Weights and Measures